Rabbani Rash-ha Wahi - QUT Rash-Ha_Wahi...Rabbani Rash-ha Wahi B.Sc. in Civil Engineering Submitted...

TOWARDS AN UNDERSTANDING OF THE

FACTORS ASSOCIATED WITH SEVERE

INJURIES TO CYCLISTS IN CRASHES WITH

MOTOR VEHICLES

Rabbani Rash-ha Wahi

B.Sc. in Civil Engineering

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Centre for Accident Research and Road Safety - Queensland

Faculty of Health

Queensland University of Technology

2018

Towards an Understanding of the Factors Associated with Severe Injuries to Cyclists in Crashes with Motor

Vehicles i

Keywords

Bicycle motor-vehicle crashes, Crash type, Cyclist, Injury severity, Hospital

admission data, Intersections, Mixed logit model, Police-reported data, Sampling

weights, Trajectory, Traffic control measures, Under-reporting, Unbiased

ii Towards an Understanding of the Factors Associated with Severe Injuries to Cyclists in Crashes with Motor

Vehicles


Vehicles iii

Abstract

In low-cycling countries such as the United States and Australia, collisions with

motor vehicles are the major causes of severe injuries to cyclists. An analysis of the

Australian literature finds that fear of collisions prevents many people from taking up

cycling. Many studies have demonstrated that these crashes are under-reported in

police data, yet police data remain the best source of information about crash locations

and circumstances on a large scale. Therefore, this research aimed to address the gaps

in three directions: alternative methods of adjusting for the effects of under-reporting

where linkage with hospital data is not possible or is uneconomic; the potential effects

of the type of traffic control to better inform measures for improving intersection

operations and cyclist safety; and, the role of pre-crash trajectory in influencing cyclist

injury severity.

Police-reported crash data for crashes involving a bicycle was gathered from the

Department of Transport and Main Roads for the State of Queensland, Australia from

2002 to 2014. The police-reported data contains detailed information about crashes

involving cyclist including driver/rider characteristics, roadway geometric features,

weather conditions, environmental characteristics, and traffic characteristics. The

aggregate data from the Queensland Hospital Admitted Patient Data Collection

(QHAPDC) for 2009-10 was used to understand the limitations of police-reported data.

A preliminary analysis of the police-reported data showed in study one that 86.4% of

the reported crashes involved a collision between a bicycle and a motor vehicle. In

other words, only 12% of the bicycle crashes reported to police were single-vehicle

(SV) crashes. Study one further demonstrated that only about 10% of cyclists admitted

to hospital as a result of single vehicle crashes featured in police-reported

iv Towards an Understanding of the Factors Associated with Severe Injuries to Cyclists in Crashes with Motor

Vehicles

hospitalisation data, which is consistent with previous findings and indicating that

single vehicle bicycle crashes are under-reported. Comparison of police-reported data

and hospital admission data confirmed the findings of previous research that extent of

under-reporting was greater in remote and very remote locations. Therefore, it was

decided to focus the research program on bicycle involved motor-vehicle (BMV)

crashes in major cities and inner/outer regional areas. Separate data-processing

techniques were adopted depending on the objective of each study. The dependent

variable in this dissertation is cyclist injury severity. The injury severity is recorded by

police according to four levels: fatality, hospitalised injury (effectively “taken to

hospital”), medically treated injury, and minor injury. Due to the low number of fatal

injuries, the fatal and hospitalised injury levels were combined for the first two studies.

Mixed logit models were utilized to analyse and quantify the impact of numerous

independent variables on cyclist injury severity.

The second study focused on developing a simple approach to adjust for the

effects of under-reporting in police data in circumstances where the optimal solution

of linkage with health system data is not possible or uneconomic. Using aggregate

hospital admission data, the distribution of age, gender, and remoteness classification

(variables which are available in both police and hospital admission datasets) was

calculated and then used to weight the police-reported data. The weighted and

unweighted police-reported data are different for the variable includes child cyclists,

older (60+) cyclists, and on cycling in inner regional areas. The result of this study

suggests that while the extent of under-reporting is considerable, and it varies by age,

gender and location combinations, it is largely unbiased in relation to crash factors.

Therefore, it is reassuring that the police data can be used to identify the types of

locations and crashes that need to be examined.


Vehicles v

The third study examined how injury severities vary across three traffic control

measures: operating traffic signals, stop/give-way (yield) sign, and no traffic control.

A likelihood ratio test revealed that the effects of explanatory variables were not

consistent for each type of traffic controls, therefore, separate mixed logit models were

estimated for each traffic control type. Despite similar distributions of injury severity

across the 3 types of traffic control, more factors were identified as influencing cyclist

injury severity at stop/give-way controlled intersections than at signalized

intersections or intersections with no traffic control. Increased injury severity for riders

aged 40-49 and 60+ and those not wearing helmets were the only consistent findings

across all traffic control types, although the effect of not wearing helmets was smaller

at uncontrolled intersections. Cyclists who were judged to be at fault were more

severely injured at stop/give-way and signalized intersections. Speed zone influenced

injury severity only at stop/give-way signs and appears to reflect differences in

intersection design, rather than speed limits per se. While most BMV crashes occurred

on dry road surface, wet road surface was associated with an increased cyclist injury

severity at stop/give-way intersections.

The final study addressed the impacts of vehicle trajectory and manoeuvres in

crashes between cyclists and light passenger vehicles at intersections. It tested the

hypothesis that cyclist injury severity in these crashes is fundamentally a function of

the speed of the motor vehicle and therefore should be greater in crashes where the

motor vehicle was travelling straight ahead than when the motor vehicle was turning.

The crash data included Definitions for Classifying Accidents codes which summarise

the trajectories of the vehicles involved. These codes were reclassified to distinguish

five different trajectory types: straight same direction, straight opposite direction,

turning same direction, turning opposite direction, and straight right angle. Mixed

vi Towards an Understanding of the Factors Associated with Severe Injuries to Cyclists in Crashes with Motor

Vehicles

logit models of cyclist injury severity were then developed for the new trajectory type

and the traditional crash type (e.g., angle, rear-end, sideswipe and head-on).

Likelihood ratio tests were conducted to compare these models. Overall, the results of

the analysis provide credence that the trajectory type is a promising classification to

explain crash pattern than the traditional crash types in the context of cyclist injury

severity at intersections.

The findings of this dissertation suggest that type of traffic control influences the

cyclists injury severity in collisions with motor vehicles at intersections and that

trajectory may be a more useful concept than crash type in classifying these crashes.

These findings can provide valuable insights to assist transport and enforcement

agencies in designing cycling infrastructure to reduce the severity of cyclist injury

resulting from bicycle-motor vehicle crashes.

The empirical results to be obtained from the models can provide further insights

to improve intersection operations, which can make a positive impact on the safety of

cyclists. Additionally, the weighting approach provides a simple first approximation

to adjust for under-reporting in police data and assess the consequences for the picture

of roadway and other factors contributing to bicycle-motor vehicle collisions.


Vehicles vii

Table of Contents

Keywords ...................................................................................................................... i

Abstract ....................................................................................................................... iii

Table of Contents ....................................................................................................... vii

List of Figures ............................................................................................................. xi

List of Tables............................................................................................................. xiii

List of Abbreviations.................................................................................................. xv

Statement of Original Authorship ............................................................................ xvii

Acknowledgements ................................................................................................... xix

Associated Publications and Presentations ............................................................... xxi

Chapter 1: Introduction ...................................................................................... 1

1.1 Background ......................................................................................................... 1

1.2 Gaps in the literature ........................................................................................... 5

1.3 Research aim and questions ................................................................................ 7

1.4 Scope of the research ........................................................................................ 10

1.5 Thesis outline .................................................................................................... 11

Chapter 2: Literature Review ........................................................................... 15

2.1 Literature search methods ................................................................................. 15

2.2 Under-reporting of bicycle crashes ................................................................... 16

2.3 Factors affecting bicycle crash severity ............................................................ 19

2.3.1 Rider characteristics ................................................................................ 20

2.3.2 Driver characteristics............................................................................... 22

2.3.3 Road characteristics................................................................................. 23

2.3.4 Traffic characteristics .............................................................................. 24

2.3.5 Environmental characteristics ................................................................. 26

2.4 Statistical methods applied in injury severity analysis ..................................... 28

viii Towards an Understanding of the Factors Associated with Severe Injuries to Cyclists in Crashes with Motor

Vehicles

2.5 Chapter summary .............................................................................................. 31

Chapter 3: Research Design .............................................................................. 35

3.1 Study Setting ..................................................................................................... 35

3.2 Research Design ............................................................................................... 36

3.2.1 Modelling approaches ............................................................................. 38

3.2.2 Model development ................................................................................. 40

3.2.3 Impact of model parameters .................................................................... 41

3.2.4 Parameter selection criteria ..................................................................... 41

3.2.5 Model fitness ........................................................................................... 43

3.3 Conceptual framework ...................................................................................... 43

3.4 Crash data elements .......................................................................................... 45

3.4.1 Merging files ........................................................................................... 48

3.5 Health risk assesment and Research ethics ....................................................... 49

3.6 Chapter summary .............................................................................................. 49

Chapter 4: Bicycle crash patterns and trends ................................................. 51

4.1 Crash Data Sources ........................................................................................... 51

4.2 Data Analysis .................................................................................................... 52

4.3 Results and Discussion ..................................................................................... 53

4.3.1 Selection of data subsets for later studies ................................................ 53

4.3.2 Descriptive analysis of selected data subsets .......................................... 56

4.4 Chapter summary .............................................................................................. 62

Chapter 5: Weighting as a simple approach to adjust for under-reporting . 65

5.1 Method .............................................................................................................. 65

5.1.1 Data ......................................................................................................... 65

5.1.2 Weighting procedure ............................................................................... 66

5.2 Results ............................................................................................................... 67

5.2.1 Estimated weights .................................................................................... 67

5.2.2 Crash patterns in weighted and unweighted datasets .............................. 68

5.3 Discussion ......................................................................................................... 71


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5.4 Chapter summary .............................................................................................. 72

Chapter 6: Influence of type of traffic control on injury severity ................. 75

6.1 Method .............................................................................................................. 76

6.1.1 Data description....................................................................................... 76

6.1.2 Analysis approach ................................................................................... 78

6.2 Results .............................................................................................................. 79

6.2.1 Descriptive analysis................................................................................. 79

6.2.2 Model estimation ..................................................................................... 82

6.3 Discussion ......................................................................................................... 85

6.3.1 Rider characteristics ................................................................................ 85

6.3.2 Roadway characteristics .......................................................................... 87

6.3.3 Environmental characteristics ................................................................. 88

6.3.4 Crash characteristics ................................................................................ 88

6.4 Chapter summary .............................................................................................. 89

Chapter 7: The influence of motor vehicle trajectory on injury severity ..... 91

7.1 Method .............................................................................................................. 92

7.1.1 Data description....................................................................................... 92

7.1.2 Analysis approach ................................................................................... 95

7.2 Results .............................................................................................................. 95

7.2.1 Descriptive analysis................................................................................. 95

7.2.2 Model estimation ..................................................................................... 98

7.3 Discussion ....................................................................................................... 101

7.3.1 Crash characteristics .............................................................................. 102

7.3.2 Roadway characteristics ........................................................................ 104

7.3.3 Rider characteristics .............................................................................. 105

7.3.4 Driver characteristics............................................................................. 106

7.4 Chapter summary ............................................................................................ 106

Chapter 8: Discussion and Conclusions ......................................................... 109

8.1 Research background ...................................................................................... 109

8.2 Review of major findings ............................................................................... 110

x Towards an Understanding of the Factors Associated with Severe Injuries to Cyclists in Crashes with Motor

Vehicles

8.2.1 RQ1: For what types of bicycle crashes is police-reported crash

data most adequate? ............................................................................... 110

8.2.2 RQ2: How does the pattern of police-reported bicycle crashes

change when adjusted for under-reporting? .......................................... 111

8.2.3 RQ3: How do traffic control measures influence cyclist injury

severity in crashes with motor-vehicles at intersections? ..................... 112

8.2.4 RQ4: Can motor vehicle trajectory better explain injury severity in

crashes with bicycles than crash type? .................................................. 114

8.3 Implications .................................................................................................... 115

8.3.1 Road and roadside engineering ............................................................. 115

8.3.2 Education and engagement .................................................................... 119

8.3.3 Enforcement .......................................................................................... 119

8.3.4 Evaluation .............................................................................................. 120

8.4 Strengths and Limitations ............................................................................... 121

8.5 Concluding remarks ........................................................................................ 124

Bibliography ........................................................................................................... 127

Appendix ................................................................................................................. 145


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List of Figures

Figure 1:1 Structure of the thesis ............................................................................... 13

Figure 3:1 Research steps........................................................................................... 37

Figure 3:2 Safe System Approach.............................................................................. 44

Figure 4:1 Bicycle crash type by year ........................................................................ 53

Figure 4:2 Comparison between number of cyclists reported by police as

hospitalised (QRCD) and hospital admissions (QHAPDC) from SV

bicycle crashes ............................................................................................. 54

Figure 4:3 Selection of data subsets ........................................................................... 55

Figure 4:4 MV bicycle crashes by ARIA+ location, QRCD 2002-2014 ................... 56

Figure 4:5 Injury severity of police-reported MV bicycle crashes by year ............... 57

Figure 4:6 MV bicycle crashes by time of the day .................................................... 58

Figure 4:7 MV bicycle crashes by road type ............................................................. 61

Figure 6:1 Selection of data subsets ........................................................................... 77

Figure 7:1 Trajectory types of Bicycle-LPV crashes ................................................. 94

xii Towards an Understanding of the Factors Associated with Severe Injuries to Cyclists in Crashes with Motor

Vehicles


Vehicles xiii

List of Tables

Table 2.1 Summary of statistical approaches in studies of cyclist injury severity .... 30

Table 3.1 Crash database structure............................................................................. 46

Table 4.1 Descriptive statistics for MV bicycle crashes ............................................ 59

Table 5.1 Demographic characteristics for QRCD 2005-2014 .................................. 68

Table 5.2 Demographic characteristics for QHAPDC 2009-2010 ............................. 68

Table 5.3 Weights based on demographic characteristics ......................................... 68

Table 5.4 Descriptive statistics for unweighted and weighted QRCD for 2005-

2014.............................................................................................................. 69

Table 6.1 Descriptive characteristics of BMV crashes under different traffic

control measures at intersections. ................................................................ 80

Table 6.2 Mixed Logit Injury Severity Models for BMV Crashes under Various

Traffic Control Measures at Intersections .................................................... 82

Table 6.3 Comparisons of variable elasticities between different traffic control

measures at intersections.............................................................................. 84

Table 7.1 Descriptive statistics for the bicycle-LPV crashes at intersections............ 96

Table 7.2 Mixed logit severity model results for crash type at intersections ............. 99

Table 7.3 Mixed logit severity model results for trajectory type at intersections .... 100

xiv Towards an Understanding of the Factors Associated with Severe Injuries to Cyclists in Crashes with Motor

Vehicles


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List of Abbreviations

Abbreviation/symbol Definition

AADT Annual Average Daily Traffic

ABS Australian Bureau of Statistics

AIC Akaike Information Criterion

ARIA+ Accessibility/Remoteness Index of Australia

BAC Blood Alcohol Concentration

BMV Bicycle motor-vehicle

CARRS-Q Centre for Accident Research and Road Safety-

Queensland

DIC Deviance Information Criterion

LPV Light Passenger vehicle

MCMC Markov Chain Monte Carlo

MV Multi-vehicle

MXL Mixed logit model

MNL Multinomial Logit

OLS Ordinary Least Square

OP Ordered Probit

QHAPDC Queensland Hospital Admitted Patient Data

Collection

QRCD Queensland Road Crash Database

QUT Queensland University of Technology

RE Relative Exposure

SSA Safe System Approach

SPSS Statistical Package for the Social Sciences

TMR Transport and Main Roads

WESMLE Weighted Exogenous Sample Maximum Likelihood

Estimator

xvi Towards an Understanding of the Factors Associated with Severe Injuries to Cyclists in Crashes with Motor

Vehicles


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Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the best

of my knowledge and belief, the thesis contains no material previously published or

written by another person except where due reference is made.

Signature:

Date: September 2018

QUT Verified Signature

xviii Towards an Understanding of the Factors Associated with Severe Injuries to Cyclists in Crashes with Motor

Vehicles


Vehicles xix

Acknowledgements

First and foremost, my utmost gratitude goes to my Principal Supervisor

Professor Narelle Haworth, whose sincerity and constant guidance, and giving me

constructive feedback with suggestions on my dissertation as it progresses and

completes. Professor Narelle has been my inspiration and gratefully acknowledged for

the experience and knowledge; I gained while working with her throughout my PhD

journey.

I would also like to thank my Associate Supervisors: Dr. Ashim Debnath and

Dr. Mark King, for their invaluable insights and opinions about my research.

Furthermore, a special thanks to my final seminar review panel, Professor Andry

Rakotonirainy and Dr. Marilyn Johnson, for their valuable feedback on my research. I

am indebted to three anonymous reviewers for the insightful comments to make Study

3 worthy of publication in the Journal, Transport Research Record (TRR).

I wish to convey my deepest appreciation to the Queensland University of

Technology (QUT) for awarding me with the QUT Postgraduate Research Award

(QUTPRA) and QUT HDR Tuition Fee Sponsorships. Many thanks also go to the

QUT's Research Degrees Committee to have given me the opportunity to attend the

97th Transportation Research Board (TRB) Annual Meeting conference in

Washington, DC, with funding to partially cover my airfare expenses.

This research would not be possible without the road traffic crash data support

from Queensland Transport and Main Road. Special thanks also go to Dr. Angela

Watson for sharing the aggregate hospital data from QHAPDC. Many thanks to Kat

Bowman, the professional editor of my thesis, who provided proofreading advice

according to the guidelines of Australian Standards for Editing Practice.

xx Towards an Understanding of the Factors Associated with Severe Injuries to Cyclists in Crashes with Motor

Vehicles

I will always remember my fellow post graduate students in the CARRS-Q.

During my PhD study, they kept me well-balanced with random exciting discussion,

valuable feedback on my research, having fun on exotic trips, and shared feelings of

existential crisis. Thanks also go members of the QUT Bangladeshi Association

(QUTBA), who have given me everlasting good memories which I will always hold

them dear to my heart.

And finally, I would like to give my deepest thanks to my parents, Mr. Azizur

Rahman and Mrs Shahana Aziz, for endless love, blessings, and their confidence on

my abilities helped me to pursue PhD degree in abroad. I am further appreciative of

my brother, Tanzil Shahriar and my sister, Sanzida Sharmin for their unwavering

support and constant motivation throughout the PhD journey.


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Associated Publications and Presentations

Publications

1. Wahi, R. R., Haworth, N, Debnath, A. K., & King, M. J. (2018). Influence of

type of traffic control on injury severity in bicycle-motor vehicles crashes at

intersections, Transportation Research Record: Journal of the Transportation

Research Board. (https://doi.org/10.1177/0361198118773576)

2. Wahi, R. R., Haworth, N, King, M. J., & Debnath, A. K. (2018). Can motor

vehicle trajectory better explain injury severity in crashes with bicycles than

crash type? (Manuscript submitted to Accident Analysis & Prevention)

Presentations

1. Wahi, R. R., Haworth, N, Debnath, A. K., & King, M. J. (2018). Influence of

type of traffic control on injury severity in bicycle-motor vehicles crashes at

intersections. Paper presented at the 97th TRB Annual Meeting of the

Transportation Research Board, Washington, DC, USA

2. Wahi, R. R., (2018). Towards an Understanding of the Factors Associated with

Severe Injuries to Cyclists in Crashes with Motor Vehicles. Poster presented at

Doctoral Student Research in Transportation Safety, 97th TRB Annual

Meeting of the Transportation Research Board, Washington, DC, USA

3. Wahi, R. R., (2018). Towards an Understanding of the Factors Associated with

Severe Injuries to Cyclists in Crashes with Motor Vehicles. Presented at UNC

Highway Safety Research Center: Chapel Hill, NC, USA

4. Wahi, R. R., Debnath, A. K., Haworth, N, & King, M. J. (2016). Towards a

multi-level understanding of factors influencing bicycle crash frequency and

injury severity, Annual School of Psychology and Counselling Postgraduate

Research Symposium. 2 November 2016. South Bank, Brisbane, Australia.

5. Wahi, R. R., (2015). Understanding the Factors Influencing Bicyclist Safety in

Queensland, IHBI Inspires Postgraduate Student Conference, Brisbane,

Australia, and November 2015. (Short listed for Real World Application prize)

Chapter 1: Introduction 1

Chapter 1: Introduction

1.1 BACKGROUND

Cycling is advantageous to both individuals and communities, as it is pollution-

free, healthy, energy-efficient, versatile, and fun (Buehler, 2012; Marshall, 2008).

Converting automobile trips into cycling trips reduces the use of automobiles and

traffic congestion, while concurrently improving the population’s health and the

climate. While riding a bicycle provides sustainable opportunities for people to be

more active, the safety of cyclists is a key concern in many countries (Bassett Jr,

Pucher, Buehler, Thompson, & Crouter, 2008). Many studies (e.g., Washington,

Haworth, & Schramm, 2012) have shown that cycling is relatively risky compared to

other modes of transport, due to the fragile nature of the unprotected human body. For

example, for the same distance, the risk of fatalities for cyclists in Australia is 4.5 times

higher than that for vehicle occupants, and the risk of cyclist injury 13 times (according

to police-reported crash data) or 34 times (according to hospital maintained data)

higher than that for motor vehicle drivers (Garrard, Greaves, & Ellison, 2010).

Bicycle-involved crashes can be classified based on the location of crash

occurrence into on-road, and off-road crashes. In the financial year 2009-10 in

Australia, 57.3% of cyclists were hospitalised as a result of on-road traffic incidents

(Tovell, McKenna, Bradley, & Pointer, 2012). On-road cycling typically involves

greater risks of crashes, as cyclists are exposed to other vehicles (parked and moving).

In addition, inconsistent provision of on-road riding facilities (e.g., termination of a

bike lane) means that transitioning from a riding facility (e.g., a bike lane) to a shared

space can cause conflicts among bicycles and other vehicles. If more Australians take

up cycling, it is likely that the number of cyclist injuries will also increase (Sikic,

2 Chapter 1: Introduction

Mikocka-Walus, Gabbe, McDermott, & Cameron, 2009). These statistics give cause

for concern and demand specific attention to improve the safety of on-road cycling.

Cyclists are the most vulnerable party in a BMV crash. While single bicycle

crashes may happen more frequently, BMV crashes account for the majority of those

recorded in police and hospital databases (Boufous, de Rome, Senserrick, & Ivers,

2013; Chong, Poulos, Olivier, Watson, & Grzebieta, 2010). The greater mass and

speed of motorised vehicles means that BMV crashes usually result in more serious

injuries compared to single bicycle crashes (Wegman, Zhang, & Dijkstra, 2012).

Experienced riders are less likely to have severe single bicycle crash incidents due to

better skills in controlling their bicycles and greater physical fitness (Schepers, 2012).

Thus, safety factors associated with the cyclist injury severity in BMV crashes have

become an important concern when developing effective road safety measures.

In BMV crashes, cyclists are more likely to be severely injured compared to

persons in a vehicle, but the Safe System Approach (SSA) that underlies the Australian

National Road Safety Strategy is not as well developed in Australia for cyclists as for

vehicle occupants. The Safe System Approach is based upon the principle that road

users make mistakes but that the impact of a mistake should not result in death and

serious injury. Over the past decade cyclist fatalities in Australia have averaged thirty

seven per year, meaning cyclists made up around 3 percent of on-road fatalities over

that period (BITRE, 2016). With respect to severe injury, the situation is worse with

cyclists now representing approximately 18 percent of on-road crashes resulting in a

hospital admission (BITRE, 2016). In the state of Victoria, for example, the rate of

hospitalised major trauma for cyclists increased from 2007 to 2015, whereas there was

no change for motor vehicle occupants (Beck et al., 2017). This indicates that cyclists

require more emphasis on reducing substantial burden of on-road bicycle related


serious injury. There is a necessity to incorporate bicycle safety explicitly in the SSA

to develop systems that are forgiving.

To achieve the desired Safe System outcome for cyclists, it seems that further

attention is required on cyclist injury severity rather than crash frequency for an entity

(i.e., total number of crashes in an intersection or midblock section). On the other hand,

modelling in crash frequency requires comprehensive exposure information to draw a

reliable conclusion (Hauer & Hakkert, 1988). With regards to under-reporting, earlier

research also shows that crashes resulting in less severe injury are less likely to be

reported in crash data bases, which may produce biased estimates (Aptel et al., 1999).

In Australia and most other countries, the potential reasons for road casualties

are determined through on-scene investigation and police-reporting, which provide

some understanding of immediate crash causal factors. There are two other data

sources that provide information on bicycle crashes: hospital maintained databases,

and self-reported surveys. Hospitals record information for people who are admitted

to hospital or attend for emergency treatment after a crash. Self-reported data are

collected through surveys and typically involve a sample of the cyclist population.

While the police-reported data includes many variables related to crash circumstance

(e.g., how a crash occurred, where it occurred, who was involved, which factors

contributed to the crash), the hospital data do not include this kind of information.

Conversely, validity of self-report data can be questionable because of non-response

and failure to recall (Jenkins, Earle‐Richardson, Slingerland, & May, 2002; Tivesten,

Jonsson, Jakobsson, & Norin, 2012). Thus, many road safety studies are evaluated on

the basis of police-reported data.

Bicycle motor-vehicle crash injuries are the result of complex interactions

between rider, driver, traffic, roadway, and environmental factors. Riders of differing


age and gender may require different road safety approaches. For example, children

and adolescents have a higher rate of bicycle injuries compared to adult cyclists

(Boufous, Rome, Senserrick, & Ivers, 2011; Hagel, Romanow, Enns, Williamson, &

Rowe, 2015). Further study has suggested that child and adult cyclist perceive the

cycling infrastructure differently (Ghekiere et al., 2014). Other factors that have an

impact on cycling safety are the roadway and traffic characteristics. For example,

Moore, Schneider Iv, Savolainen, and Farzaneh (2011) found that BMV crashes at

intersections are more likely to result in higher injury severity compared to other road

sections. The level of bicycle safety at intersections not only depends on the

rider/driver characteristics, but also on traffic characteristics, such as the posted speed

limit, density, and traffic signals (Wei & Lovegrove, 2013). The final factor is the

environmental characteristics. Weather and lighting conditions have an effect not only

on bicycle safety but also on traffic flow patterns in general (Nankervis, 1999). Since

all these factors interact, understanding the complex interaction, and to what degree

each factor contributes to cyclist injury severity is important in developing appropriate

remedial measures.

The poor quality data and the complex interaction in BMV crashes indicates that

system factors are not nested within a structure to reduce the severe injury to an

acceptable level. Therefore, analysis of injury severity has been a major interest to

researchers in on-road bicycle safety, since such research aims to not only understand

where, when, and under what circumstance BMV crashes occur but also to decrease

the injury severity. An understanding of the factors associated with cyclist injury

severity is essential to assist road safety practitioners, policy makers, and insurance

companies to make informed decisions to improve cycle safety.


1.2 GAPS IN THE LITERATURE

While many researchers have focused on the analysis of cyclist injury severity,

there are several research gaps that still exist, such as the methodological issues

regarding the effects of under-reporting in bicycle crash injuries, potential effects of

type of traffic control on cyclist injury severity levels, and impact of vehicles’ pre-

crash trajectory, suggesting the need to provide further insights in bicycle safety

literature. In subsequent sections, various research gaps for cyclist injury severity

analysis are presented and discussed briefly.

First, despite having more information about the crash circumstances in police-

reported data, researchers have shown the extent of under-reporting of bicycle crashes

is considerable when compared with hospital admission data (Yannis, Papadimitriou,

Chaziris, & Broughton, 2014). Various statistical methods have been developed to

address under-reporting in crash injury data. For example, Yamamoto, Hashiji, and

Shankar (2008) found that sequential probit models performed better than ordered

probit models in terms of bias in the parameter estimates. Yasmin and Eluru (2013)

undertook a comparison of ordered and unordered models to explore which models

perform better in the presence of under-reporting in the crash data. Ye and Lord (2014)

showed that an outcome based sampling method in model estimation via the use of a

Weighted Exogenous Sample Maximum Likelihood Estimator (WESMLE) can

account for under-reporting issues. These methods have been tested for all types of

crash injuries aggregately, but have not yet been applied to individual crash types,

including bicycle crash injury. No attempts have been made to date to develop a

methodological framework that can adjust for the effects of under-reporting in order

to better understand the contributing factors to cyclist injury severity in BMV crashes


and implement more effective countermeasures that can create a safer road

environment for cyclists.

Second, the majority of BMV crashes occur at road intersections (Kim, Kim,

Ulfarsson, & Porrello, 2007). The issues at each intersection are often unique, making

it difficult for drivers to determine the position of cyclists, as there can be a range of

paths through each intersection (i.e. hook turn, filtering, in normal flow of traffic, and

bicycle lane). This creates an additional decision load and makes it potentially

dangerous for other road users.

Previous studies have also identified that type of intersection control (signals,

give-way signs/stop signs, or none) can influence cyclist injury severity. For example,

Eluru, Bhat, and Hensher (2008) found higher cyclist injury severity at unsignalised

intersections than signalised intersections. Another recent study by Wang, Lu, and Lu

(2015) focused on un-signalised intersections and found that stop-controlled

intersections were associated with higher injury severities to cyclists, compared to

uncontrolled intersections. Earlier research has predominantly focused on different

contributing factors at intersections, while the type of intersection control has received

much less attention. Traffic control characteristics are particularly important because

BMV crashes are often severe due to motor vehicles crossing signalised intersections

at high speeds (Wachtel & Lewiston, 1994). Drivers are more likely to be at-fault at

unsignalised intersections, and not-at-fault cyclists struck by MVs experience more

severe injury (Kim & Li, 1996). Therefore, there is a need to analyse BMV crashes at

intersections, relate the BMV crashes to the traffic control, and understand other

influencing factors to determine how to reduce cyclist injury severity at intersections.

Finally, many studies have considered the traditional crash type (e.g., head-on

or angle crashes) as an independent variable in modelling cyclist injury severity (e.g.,


Kim, et al., 2007; Moore, et al., 2011). Such crash type analyses are categorised based

on initial point of impact of the target and striking vehicles in motor-vehicle crashes,

where the parties involved are likely to be similar in mass and velocity than in BMV

crashes. Consequently, Hauer, Ng, and Lovell (1988) argued that injury patterns might

be associated with the traffic flow patterns to which the colliding vehicle belongs. For

example, Isaksson-Hellman and Werneke (2017) found that crashes in which bicycles

and motor-vehicles were travelling in the same or opposite directions were less

frequent but increased the cyclist injury severity. A possible explanation is that when

the motor-vehicle is moving in a straight severity ahead on the roadway it is likely to

have higher speed. If the cyclists are struck at a high impact speed, they tend to sustain

higher injury severity (Badea-Romero & Lenard, 2013). It seems that the severity of

the cyclist injury depends on the trajectory and manoeuvre of the two vehicles before

the collision, not on the initial point of impact of the vehicle. Therefore, it is

hypothesised in this study that motor-vehicles (MV) moving straight along roadways

increase the likelihood of severe injury to cyclists, while turning motor vehicles are

likely to have lower operating speeds, resulting in a lower probability of severe injury

to the cyclist.

1.3 RESEARCH AIM AND QUESTIONS

While the Safe System approach has susceptibility to injury and forgiving

infrastructure as focal principles; the increased frequency of serious injury among

cyclists in Australia in recent years indicates that the problem has not been adequately

considered or understood. One of the key barriers to improving safety for cyclists is

the poor quality of data, which means the magnitude of the problem is potentially

underestimated and the identification of effective countermeasures is therefore

impeded.


Despite their inadequacies, police data remain the best source of information

about the locations, times, and circumstances of crashes on a large scale to underpin

appropriate countermeasures. Therefore, the aim of this dissertation is to develop

statistical models that best utilise police-reported data to obtain a better understanding

of the factors contributing to severe injuries in BMV crashes.

The review of the literature presented in Chapter 2 identifies three significant

gaps, both in Australia and internationally. Knowledge of bicycle safety issues and

ways to improve the safety of cyclists could effectively be enhanced by addressing

these gaps in the literature, which shows the need for this PhD study. The research

questions requiring further attention are discussed below.

Research question 1: For what types of bicycle crashes are police-reported crash data

most adequate?

As noted in Section 1.2, various studies have demonstrated that police-reported

databases are often biased and incomplete. Despite the limitations, police crash reports

remain a comprehensive source of information to map the crash locations, and help to

identify the contributing factors to crashes. Research Question 1, therefore will

examine police data for different types of crashes to identify those where data quality

is potentially better.

Research question 2: How does the pattern of police-reported bicycle crashes change

when adjusted for under-reporting?

A growing body of literature has focused on modelling bicycle crash frequency

and associated injury severity levels. Despite significant levels of under-reporting in

the police-reported bicycle crash data, no studies have systematically adjusted for the

effects of under-reporting in crash data modelling. Without considering under-

reporting issues, modelling results are likely to be biased and inaccurate.


Consequently, the contributing factors to bicycle crash frequency and injury severity

and their relative contribution, which is important to develop appropriate

countermeasures for the factors, would be incomplete and misleading. Research

Question 2 therefore develops an appropriate methodology to adjust for the effects of

under-reporting in bicycle crash data.

Based on the outcome of the first two research questions, there remains a need

for better methods to examine the roles of specific factors in BMV crashes at

intersections, which are the focus of Research Question 3 and Research Question 4.

Research question 3: How do traffic control measures influence cyclist injury severity

in crashes with motor-vehicles at intersections?

Existing studies have shown that cyclist injury severity is substantially different

at signalised and unsignalised intersection locations. Despite the considerable studies

that have looked at different contributing factors to cyclist injury severity, a limited

number of studies have specifically focused on the influence of traffic control

measures. Therefore, a disaggregated picture of injury severities sustained by cyclists

involved with motor-vehicles is still not understood at each type of intersection

control. This is important to understand because each crash occurrence derives from a

specific sequence of events generally having unique influencing factors which are

characteristic of populations or locations. If this research question further identifies the

problematic attributes that affect crash incidence at different severity levels at

intersections, then appropriate remedial measures can be formulated.

Research question 4: Can motor-vehicle trajectory better explain injury severity in

crashes with bicycles than crash type?

Some studies have examined the factors that affect cyclist injury severities

among various crash types by employing different statistical modelling techniques.


However, cyclist injury severity may be influenced by vehicle movement and

travelling direction, and very little is known about crash mechanism, because research

to date has understandably tended to focus on the traditional crash types (e.g., head-on

crash and angle crashes). Reconstructing the crash typologies is important for practical

decision making because the severity of cyclist injury likely depends on the trajectory

and manoeuvre of the vehicles at intersections. Research Question 4, therefore, further

investigates in a comprehensive and holistic way whether motor-vehicle trajectory

influences cyclist injury severity at intersections.

1.4 SCOPE OF THE RESEARCH

The research aimed to identify the contributing factors that influence cyclist

injury severity. It is acknowledged that many bicycle crashes occur in off-road

locations, but they are not included in the research because they are unlikely to involve

motor vehicles and are not required to be reported to police.

The scope of the data investigation is limited to on-road bicycle crashes which

occurred in Queensland during the period 2002-2014, as recorded in the police-

reported database. Supplementary information from existing studies in the context of

this jurisdiction were used in addition to the crash data.

At-fault status in crash data was assigned by police officers or investigators, who

are likely to attend the scene of the crashes. Based on police officers’ judgement of at-

fault status, the most-at-fault party involved in a crash is considered as the at-fault

party, whereas the other party is considered as ‘not-at-fault’. It is noted that such

conventions of at-fault status are common in the literature (e.g., Haque, Chin, &

Huang, 2009; Russo, Savolainen, Schneider, & Anastasopoulos, 2014).

Due to issues related to a greater extent of under-reporting, single bicycle crashes

and bicycle crashes that occurred in remote and very remote areas as defined by the


Accessibility/Remoteness Index of Australia (AIHW, 2004) are outside of the scope

of current research.

Selection of explanatory variables (i.e., contributing factors) in the statistical

models of injury severity will be based on the available information in the police-

reported crash dataset.

1.5 THESIS OUTLINE

The flow chart of the dissertation is presented in Figure 1.1. The dissertation is

organised as follows:

Chapter 1: Presents a brief introduction about bicycle safety aspects, research

gaps, research questions, scope of the research, and outline of the

thesis.

Chapter 2: Summarises the literature on under-reporting issues and risk factors

of bicycle crashes resulting in severe injury or fatality. Current issues

of methodological approaches to analysis of bicycle injury data are

also discussed. Additionally, this chapter also explains and

summarises the research gaps of these studies.

Chapter 3: Describes the proposed methods for achieving the research objectives

and gives an explanation of the methodology, including the selection

of the model. This is followed by the conceptual framework based on

the Safe System Approach that defines relationships between risk

factors and injury severity in bicycle crashes. Finally, ethical issues

and health risk assessment relating to this research project are

discussed.

Chapter 4: Presents the preliminary findings obtained from Study 1 of this

research program. This study involves identifying the appropriate


data subsets for the later studies and generating insights about the

potential variables to be used in the injury severity models (Studies 3

and 4).

Chapter 5: Examines whether the use of aggregate hospital data to weight police

data is a useful compromise in improving data quality. Arising from

the findings, it shows the extents to which weighting can affect the

distributions of the variables in the police dataset.

Chapter 6: Investigates the factors affecting BMV crashes at intersections by

considering the each traffic control measure. Several important

findings and the relative magnitude of the elasticity effects are also

discussed.

Chapter 7: Examines whether the trajectory type is a promising classification to

explain crash pattern rather than the traditional crash types in the

context of cyclist injury severity at intersections. The findings of this

study provide valuable insights into crash pattern factors that affect

injury outcomes at intersections.

Chapter 8: Summarises the key findings followed by a discussion of the

implications of this dissertation. The limitations are also discussed

along with possible future research directions on this topic.


Figure 1:1 Structure of the thesis

Chapter 1: Introduction

Chapter 2: Literature review

Chapter 3: Research design

and Methodology

Chapter 4: Bicycle crash

pattern and trends

Chapter 6: Influence of

type of traffic control on

injury severity in bicycle-

motor vehicle crashes at

intersections

Chapter 7: The influence

of motor-vehicle trajectory

on cyclist injury severity at

intersections

Chapter 5: Weighting as a

simple approach to adjust for

under-reporting

Chapter 8: Discussion and

Conclusions


Chapter 2: Literature Review 15

Chapter 2: Literature Review

This review of the literature begins with a discussion of under-reporting issues

in bicycle crashes, followed by a review of the factors that influence cyclist injury

severity. This approach is consistent with the focus on injury severity in later chapters.

A comprehensive review of the statistical models used in the literature to examine the

contributing factors is then presented. First, however, the literature search criteria are

briefly outlined.

2.1 LITERATURE SEARCH METHODS

A comprehensive review was conducted to present scientific evidence and

identify gaps in current knowledge regarding bicycle safety. In December 2017, papers

addressing research into factors affecting the bicycle crashes were sought using the

following databases: Scopus, Transport Research International Documentation

(TRID), Science Direct, Google Scholar and Web of Science. The search was

conducted with the keywords on December 2017. To ensure all relevant studies were

identified, the following keywords were used and adapted as appropriate for each

database: bicycle-motor vehicle*, cyclist*, bicycle*, crash*, collision*, injury*, fatal*,

and under-reporting*. These keywords were also grouped using the Boolean operators

“or”/“and”. Studies where the focus was on bicycle-pedestrian collision and electric

bicycles were excluded. Finally, abstracts and articles published in languages other

than English were excluded.

16 Chapter 2: Literature Review

2.2 UNDER-REPORTING OF BICYCLE CRASHES

Police-reported crash data is typically maintained by transport or enforcement

agencies by following a systematic process of data collection and processing. When a

crash is reported to police either by the crash-involved parties or members of the

public, a police officer typically attends the crash site to record information about the

crash, its location, involved parties, and causes of the crash etc. This information is

recorded in a pre-defined form which is later checked and entered into a digitised

database.

Despite having a systematic process of crash data collection and processing, the

police-reported crash data suffers from issues related to under-reporting and missing

information. Elvik and Mysen (1999) concluded from a meta-analysis of 49 studies of

under-reporting in police data from 13 countries that the highest degree of under-

reporting was among vulnerable road user crashes, particularly bicycle crashes. This

was further confirmed by a recent survey study (Shinar et al., 2018) across 17 countries

which showed that an average of only 10% of all bicycle crashes were reported to the

police.

A handful of studies have also used the population survey method to gather

information about the variability of under-reporting across settings. For example, a

Belgian cohort study focused on minor accidents involving cyclists (1087 cyclists, 70

crashes) found that 7% were reported to police (de Geus et al., 2012). A study in New

Zealand (1456 cyclists, 784 crashes) found only 7% were reported to police (Tin,

Woodward, & Ameratunga, 2013), while a Tasmanian study conducted over an 8-

month period (136 cyclists, 59 crashes) found higher reporting levels for severe injury,

with 36% reported to police (Palmer et al., 2014). A cross-sectional study of 2056

cyclists in Queensland found that 545 reported cycling injuries, while 9% of the


respondents indicated that their serious injury was reported to police (Heesch, Garrard,

& Sahlqvist, 2011).

Comparison of police-reported data and hospital maintained data shows that

many cyclist injuries are not recorded in the police dataset. For example, Watson

(2014) compared the numbers of injured cyclists recorded as admitted to hospital in

the hospital database (Queensland Hospital Admitted Patient Data Collection) with the

numbers coded as ‘hospitalised’ in the police-reported database (Queensland Road

Crash Database) in Queensland in 2009. Only just more than half of the admitted

cyclists recorded in the hospital database were coded as hospitalised in the police-

reported database. Similar findings have been obtained in other countries. Proportions

of hospital recorded cyclist injuries that appeared in police databases were about 50%

in Belgium (De Mol & Lammar, 2006), 22% in New Zealand (Langley, Dow,

Stephenson, & Kypri, 2003), and 34% in Germany (Juhra et al., 2012). While the

above studies show that hospital databases have more injury observations than police

databases, it is to be noted that the hospital databases include only injured cyclists who

are admitted to hospital, whereas the police databases potentially record injuries of all

severity levels (e.g., fatal, hospitalised, medically treated, minor injury etc.). The

evidence presented in this section suggests that while police-reported data are valuable,

the true extent of under-reporting is actually even greater because some causalities

may be admitted to hospital without reported to the police.

The extent of under-reporting in police or hospital data varies greatly by the

injury severity levels, crash types, and crash locations. For example, crashes resulting

in slight injuries are less likely to be reported to police than crashes resulting in serious

injuries (Elvik, Vaa, Erke, & Sorensen, 2009; Harris, 1990; Ward, Lyons, & Thoreau,

2006). BMV crashes are more likely to be reported than single-vehicle bicycle crashes


(i.e., falling off bike, or colliding with a fixed object) due to the high impact force,

higher likelihood of causing injuries, and associated compensation issues

(Vandenbulcke et al., 2009; Winters & Branion-Calles, 2017). Crashes which have

occurred in remote areas are also more likely to not be reported to police than those

which have occurred in less remote and urban areas (Watson, Watson, & Vallmuur,

2015).

Researchers have also questioned the accuracy and completeness of police crash

reports (Watson, et al., 2015). For instance, if an ambulance arrives before the police,

the injured parties may not be able to speak directly with police, and so the crash

circumstances may be incorrectly recorded. Watson, Watson, and Vallmuur (2013)

showed from a comparison of hospital and police maintained databases that police data

often incorrectly records injury severity levels. For example, police may record a case

as ‘hospitalised’ when an injured person is taken to hospital but allowed to go home

after minor treatment.

A considerable amount of literature has explored the underlying reasons for

under-reporting in police-reported data. For example, time constraints for crash

involved people are major barriers to incident reporting because reporting systems are

time consuming to complete (Williams, Phipps, & Ashcroft, 2013). Substantial

literature also acknowledges the problem of under-reporting which can occur when the

injured party in crash was at-fault and did not wish to inform police to avoid complex

situations (Loo & Tsui, 2007; Rosman, 2001), along with concern about the family

distress and social image (Peltzer & Renner, 2004). Police distrust has also been

mentioned as an impeding factor to report a crash, as the police undertake the dual role

of service provider and authority (Kaplan, Janstrup, & Prato, 2017).


Under-reporting of crashes means that the police-reported data is a biased sample

of all crashes. If this important issue is not tackled by using appropriate methods, it is

likely that the results obtained from crash data modelling will be inaccurate and

misleading. Linkage of police crash data with other sources of data (hospital,

ambulance, death registries) provides a means to estimate the degree of under-

reporting and compile more complete information regarding crashes which are

recorded in multiple data sets (Rosman, 2001). However, there can be considerable

challenges associated with data linkage such as extended time frames, restrictions on

access to data, and costs of obtaining and analysing the data on a regular basis.

Therefore, the development of a robust, non-complicated, and relatively inexpensive

method to adjust for the effect of the under-reporting is highly desirable.

Given the extent of under-reporting of bicycle crashes, there are major problems

in determining bicycle crash frequency or the factors that influence it using police data.

However, comparisons of crash frequency may be misleading, as it is dependent on

the distance ridden, which varies dramatically among countries, as well as the risk

factors associated with riding. For this reason the focus here, as in many similar

studies, will be on injury severity and not crash frequency.

2.3 FACTORS AFFECTING BICYCLE CRASH SEVERITY

This section presents a discussion of the factors that contribute to the severity of

bicycle crash injuries by categorising them into five broad groups: (2.3.1) rider

characteristics, (2.3.2) driver characteristics, (2.3.3) roadway characteristics, (2.3.4)

traffic characteristics, and (2.3.5) environmental characteristics. These groups of

factors have been analysed in many studies in the literature. This review of the

literature attempts to present the most relevant and methodologically rigorous (in terms

of applied statistical models) work in this area. Crash severity is generally defined as


the maximum level of injury sustained by any of the persons in the crash. The number

of categories used to describe crash or injury severity varies across jurisdiction, as does

the labels applied. For example, in Queensland studies injury severity is commonly

categorised as fatal, hospitalised, medically treated, and minor injury. The following

sections discuss five factors that affect cyclist injury severity.

2.3.1 Rider characteristics

Most researchers have agreed that rider characteristics significantly influence

cyclist injury severity. For example, among cyclists in crashes those aged over 60 were

more likely to experience severe or fatal injuries than younger cyclists in Australian

(Boufous, de Rome, Senserrick, & Ivers, 2012), USA (Habib & Forbes, 2014), Chinese

(Wang, et al., 2015), and Italian studies (Prati, Pietrantoni, & Fraboni, 2017). Kröyer

(2015) also found an extremely high increase in fatality risk between the age groups

of 55-64 and 65-74 years in a study conducted in Sweden. The underlying reasons

could be that older cyclists do not react as quickly as younger cyclists and their

physical fragility could also contribute to their risk of fatality and serious injury (Eluru,

et al., 2008; Rivara, Thompson, & Thompson, 2015). A study in Spain found that riders

between the ages of 10 and 19 had a higher likelihood of severe injury (Martínez-Ruiz

et al., 2013). Loo and Tsui (2010) in Hong Kong similarly observed that child cyclists

were more likely to incur severe injuries. Similar results were reflected in a Victorian

study, which indicated that more than one third of child cyclists admitted to hospital

had sustained head injuries (Boufous, et al., 2011). This may be because cyclist age is

associated with unobserved risk factors such as travel speed, distance perception,

reaction time, and risk-taking behaviour.

Many studies have examined the association between cyclist gender and injury

severity. For example, Hagel, et al. (2015) found that male cyclists had an increased


probability of severe injury, which is in line with the findings of previous research

(Bíl, Bílová, & Müller, 2010; Eluru, et al., 2008). Female cyclists were found to have

an increased likelihood of severe injury in a few studies (Klassen, El-Basyouny, &

Islam, 2014; Moore, et al., 2011), however other evidence shows that fatally and

seriously injured cyclists were usually male (Boufous et al., 2010; Watson & Cameron,

2006). A possible explanation may be a difference in levels of exposure, as female

cyclists may prefer cycling on off-street bicycle paths or tracks to feel safe (Aldred &

Dales, 2017; Garrard, Rose, & Lo, 2008) and so may be less involved in BMV crashes

which are likely to result in less severe outcomes.

Alcohol and drug use affect both crash risk and injury severity in cyclist crashes.

For instance, one-third of the fatal BMV crashes in the USA involved intoxicated

cyclists or motorists (Li, Baker, Smialek, & Soderstrom, 2001), while cyclist

intoxication has been shown to significantly increase cyclist injury severity in Japan

(Homma et al., 2017) and the USA (Kim, et al., 2007). Cyclists under the influence of

alcohol were more likely to be riding at night (Sethi et al., 2016; Twisk & Reurings,

2013), and therefore poor visibility might contribute to the injuries among intoxicated

cyclists. Furthermore, it was found that, intoxicated cyclists were less likely to wear

helmets (Crocker, Zad, Milling, & Lawson, 2010).

Numerous studies and meta-studies have suggested that bicycle helmets use

reduces the risk of fatal injuries (e.g., Dinh et al., 2015; Gomei, Hitosugi, Ikegami, &

Tokudome, 2013; Rivara, et al., 2015), head injuries (Berg & Westerling, 2007;

Persaud, Coleman, Zwolakowski, Lauwers, & Cass, 2012), and facial injuries

(Fitzpatrick, Goh, Howlett, & Williams, 2018; Stier et al., 2016). For example, Hagel,

et al. (2015) found that not using a helmet on the road increased the probability of

severe injury, while, Kim, et al. (2007) another study identified that helmet use


decreased the probability of fatal injury. Some studies, however, pointed out the

inadequate protection of helmets (Curnow, 2007; Hooper & Spicer, 2012). Helmet use

has also been found to be associated with more severe injuries to body regions other

than the face, head, and neck (Amoros, Chiron, Martin, Thélot, & Laumon, 2012).

There could also be confounding factors that produce such contradictory results. For

instance, cyclists who wear a helmet may take more risks than non-helmeted cyclists

(Thompson, Rivara, & Thompson, 1996).

2.3.2 Driver characteristics

Driver demographic factors such as age, gender, and behaviour are expected to

influence cyclist injury severity. For example, BMV crashes involving drivers aged

over 55 showed increased probability of severe and fatal injuries to the cyclist (Wang,

et al., 2015). Male drivers were found to increase the likelihood of severe cyclist

injuries relative to their female driver counterparts (Behnood & Mannering, 2017;

Fruhen & Flin, 2015).

Many studies have reported that drivers were more often at-fault than cyclists in

BMV crashes and that the driver being at fault is associated with increased cyclist

injury severity (e.g., Kim, et al., 2007). Bíl, et al. (2010) noted that driver mistakes

were associated with a higher level of injury for cyclists travelling in a straight section.

A Queensland study found that when the driver is at-fault, the most frequently recorded

traffic violations were inattention and disobeying give-way signs (Schramm,

Rakotonirainy, & Haworth, 2010). Therefore, a good understanding of the behaviours

and crash characteristics associated with fault may help to understand the determinants

of cyclist injury severity.

The risk of alcohol-attributable cyclist injury from motor vehicle drivers is clear

in the research literature. Drinking and driving has been shown to increases cyclist


injury severity (Moore, et al., 2011; Robartes & Chen, 2017). This finding corroborates

that of Li, Shahpar, Soderstrom, and Baker (2000), who noted that intoxicated drivers

are more likely to have been driving at higher speeds at the time of a crash. The

evidence presented in this section suggests that it is important to understand the role

of driver characteristics on cyclist injury severity.

2.3.3 Road characteristics

The effect of road geometry on cyclist injury severity is complicated and varies

between study contexts. For instance, cyclist crashes occurring on curves were

associated with an increased probability of severe injury and fatal injury in the USA

(Chimba, Emaasit, & Kutela, 2012; Kim, et al., 2007; Robartes & Chen, 2017), but a

study in the Czech Republic found that curved roads decreased the probability of fatal

injury (Bíl, et al., 2010). This may be a result of drivers and cyclists being unaware of

one another at curved sections of a road. There is no clear evidence in the Australian

context, that explores whether and to what extent the existence of curves increase

cyclist injury severity.

With regards to vertical alignment, crashes that occur on roads with a grade were

not significantly more serious, but the combined effect of curves with grades increased

cyclist injury severity in a USA study (Moore, et al., 2011). Robartes and Chen (2017)

reasoned that roads with grades reduce the sight distance of the motor vehicle driver,

resulting in a greater difficulty avoiding potential crashes. Grades may influence

cyclist and motor vehicles speeds in crashes, and therefore severity, but there have

been few in-depth studies able to quantify whether this is indeed the case.

Extensive research has focused on the features of road networks, such as

intersections and roundabouts, which contribute to BMV crashes. Complex

intersections (with large numbers of road legs, road users, or signs) and complex traffic


situations increase the risk of injury for cyclists. For instance, Strauss, Miranda-

Moreno, and Morency (2013) found a positive relationship between crash occurrence

and intersection density. However, these results are inconsistent with those of a recent

study by Chen (2015) which found that three-way, four-way and complicated

intersections decreased the probability of bicycle crashes, and hypothesised that this

was due to lower driving speeds. Researchers have also identified that roundabouts do

not reduce cyclist injury severity. For instance, Sakshaug, Laureshyn, Svensson, and

Hydén (2010) found that dangerous interactions were associated with roundabouts.

Daniels, Nuyts, and Wets (2008) concluded that roundabouts with a cycle path

(separated) were safer than roundabouts without a cycle facility (integrated).

2.3.4 Traffic characteristics

Vehicles have higher kinetic energy when travelling at higher speed limits,

which tends to increase the injury severity, however, comparatively few studies have

examined the association between posted speed limits and cyclist injury severity (Chen

et al., 2017; Eluru, et al., 2008; Moore, et al., 2011). For instance, when the vehicle

speed exceeds 65 km/h, the probability of fatal cyclist injury increases more than 11

fold (Kim, et al., 2007). Wang, et al. (2015) found at unsignalised intersections with

speeds less than 30 mph decreased the probability of cyclist injury severity. Another

study, Yan, Ma, Huang, Abdel-Aty, and Wu (2011) found that posted speed limit

above 50 km/h increase the injury severity at unsignalised intersections. The effect of

the posted speed limit on cyclists is not consistent across studies, but most studies

commonly concluded that higher speed limits increase the injury severity. Posted

speed limit, type of collision partner (e.g., van, car), and location characteristics (e.g.,

intersections, non-intersections) may lead to different injury severity outcomes (Peng,

Chen, Yang, Otte, & Willinger, 2012). Given the differences in infrastructure and


posted speed limits between crash locations, it is expected that interventions targeting

safe speed limits specific to each locale will be most effective.

Numerous studies have been conducted in recent years to determine the crash

type factors that are associated with more severe injuries with strong agreement that

head-on, (Bíl, et al., 2010; Boufous, de Rome, et al., 2012; Ma, Ma, Liu, & Shi, 2014),

angle (Yan, et al., 2011), and rear end collisions (Moore, et al., 2011) are the most

common types of bicycle crashes that result in high levels of injury severity. Cyclists

riding against the direction of traffic and irregular manoeuvres may result in head-on

collisions and angle collisions, respectively (Kim, et al., 2007). Most angle crashes

occur at intersections and researchers have speculated that drivers approaching

intersections are less likely to notice cyclists in the conflicting traffic stream (Yan, et

al., 2011). However, such information does not provide a sufficient understanding of

the crash pattern which might lead to the development of ineffective countermeasures.

(Hauer, et al., 1988) focused on aggregate crashes and categorised them by the

movements of the involved vehicles before the crashes. Later Summala, Pasanen,

Räsänen, and Sievänen (1996) classified the bicycle crashes patterns at unsignalised

intersections into eight types and analysed the driver visual search tasks for the cyclist.

A recent Australian in-depth investigation of cyclist crashes (Beck et al., 2016)

highlighted that the majority of the crashes occurred when a motorist turned right into

the path of an oncoming cyclist. However, little research to date has focussed on the

unique crash patterns which influence cyclist injury severity.

Typically intersections are controlled by stop signs, give-way/yield signs, traffic

signals, or have no formal controls. Previous research has shown that traffic control

mechanisms such as all-way stop signs, traffic signals, and bike signs influence cyclist

injury severity. In Kentucky, USA Wang, et al. (2015) found that four-way stop


controls at unsignalised intersections significantly decreased the probability of fatal

injuries to cyclists relative to their no traffic control counterparts. The possible

explanation is that motor vehicles in all directions were required to reduce their speed

at stop control intersections, which reduces the injury severity of cyclists. It is well

established that traffic rules are also important to regulate the interaction between

cyclists and motor vehicles. For example, bike signs (similar to pedestrian crossing

signs) at intersections decreased the severity of injury to cyclists and increased the

awareness of cyclists among drivers (Klassen, et al., 2014). However, these few studies

do not provide a clear or detailed understanding of the contributing factors under

various traffic control measures, which is necessary for the implementation of

appropriate countermeasures.

Drivers failing to give-way has been identified as a common contributing factors

in many bicycle and motorcycle safety studies (Haworth & Debnath, 2013; Pai,

Hwang, & Saleh, 2009), which further implies that there might be similarities between

bicycle and motorcycle crashes. The most relevant study that has explored such issue,

but not for cyclists, was by Pai and Saleh (2007), who found that motorcyclist injuries

tended to be much more severe in approach-turn collisions at signalised junctions than

at unsignalised junctions. Another study (Lee & Abdel-Aty, 2005) concluded that there

was an increased pedestrian injury severity at the intersections that were not controlled

by any traffic control measure.

2.3.5 Environmental characteristics

Many studies have shown that roadway lighting conditions affect cyclist injury

severity (e.g., Eluru, et al., 2008; Habib & Forbes, 2014; Klop & Khattak, 1999).

Cyclists riding in daylight conditions have lower severity of injuries (Ma, et al., 2014).

Moreover, there are some other studies that have found that dark conditions were


associated with cyclist injury severity. For example, riding in darkness without street

lights increased the probability of serious injuries (Kim, et al., 2007; Wang, et al.,

2015). Two studies (Boufous, de Rome, et al., 2012; Yan, et al., 2011) also found that

riding a bicycle in dark conditions increased the possibility of severe injuries, with

limited lighting conditions being positively associated with fatal injuries (Chimba, et

al., 2012). Cyclists in Australia often overestimated their visibility in terms of driver

perception (Wood, Lacherez, Marszalek, & King, 2009), which might be a potential

barrier to the use of visibility aids. Hagel et al. (2007) provides further evidence that

cyclists in Canada do not wear high visibility clothing on a regular basis.

In recent years, increased consideration has been directed at determining the

effects of weather on cyclist injury severity. For instance, Wang, et al. (2015) found in

USA that wet roads were associated with increased probability of severe or fatal

injuries. They observed that riders were more likely to lose control on wet roads than

dry roads. Conversely, Sze, Tsui, Wong, and So (2011) found in Hong Kong that

adverse weather conditions were less likely to be associated with severe injuries due

to awareness and caution in unfavourable conditions. However, adverse weather

reduces visibility and makes roads more slippery, leading to more severe injuries. For

example, one study (Chimba, et al., 2012) found that rainy or foggy conditions

increased the probability of fatal injury to cyclists, though at-fault status was not

included in such analysis. Therefore, it is essential to understand the environmental

factors to compensate for higher injury risk by maintaining safe spacing, reducing

speeds, and driving/riding more carefully.


2.4 STATISTICAL METHODS APPLIED IN INJURY SEVERITY

ANALYSIS

The earlier review of the literature presented many studies that examined the

contributing factors to cyclist injury severity. This section discusses the

methodological issues regarding the statistical models used in prior studies.

Statistical regression modelling has become a preferred method for analysing the

relationship between cyclist injury severity and one or more explanatory variables.

Injury severity is typically compiled in police-reported crash databases as an ordinal

scale, such as minor injury, medically treated injury, hospitalised injury, and fatal

injury. Savolainen, Mannering, Lord, and Quddus (2011) provided an assessment of

the characteristics of crash severity data and methodological alternatives, and

limitations for each approach. They also reported that the selection of discrete response

models is based upon the nature of the data (generally classified as either nominal or

ordinal). Consequently, discrete models such as logit and probit are the most suitable

for the analysis, based on the size of the data and computational requirements. The

following sections provide an overview of statistical models considered in bicycle

injury severity studies.

Table 2.1 shows that logistic regression models are most commonly used due to

the binary nature of how traffic crash data is being recorded (such as fatal versus non-

fatal injury). Similarly, ordered models were very commonly used in previous studies

of bicycle crashes due to the models’ ability to examine discrete choices, which are

the levels of severity of the crash (e.g., Kang & Lee, 2012; Kaplan, Vavatsoulas, &

Prato, 2014b). The ordered model is suitable for modelling with a categorical

dependent variable. For example, Amemiya (1985) found that to model ordered data,

if an unordered model is used where a parameter is estimated to remain constant, there


is a loss of efficiency. However, it might not be appropriate for injury severity data

because ordered probit models can restrict the influence of explanatory variables.

Suppose that the air bag deployment indicator variable in an ordered model is

constrained to either increase the probability of hospitalised injury (subsequently

decreasing the probability of minor injury) or decrease the probability of minor injury

(subsequently increasing the probability of hospitalised injury) (see Washington,

Karlaftis, & Mannering, 2010). But the reality may be that the deployment of an airbag

increases the probability of an injury by reducing hospitalised injury but increasing

minor injury to a larger extent (from airbag deployment).

When the dependent variable (injury severity) is discrete in nature and comprises

more than two categories, the multinomial logit model (MNL) is a popular discrete

choice model (e.g., Chimba, et al., 2012; Lee, Underwood, & Handy, 2015; Yan, et

al., 2011). This model can be used to explore relationships between explanatory

variables and a nominal outcome variable, a discrete categorical outcome with no

ranking. However, the MNL model does not take ordered outcomes into account and

it requires the estimation of more coefficients, hence demanding larger samples.

Additionally, this approach cannot model the unobserved factors, such as rider or

driver behaviour and failure to estimate these factors can introduce unobserved

heterogeneity. Ignoring the potential heterogeneity across observations can lead to

biased results and incorrect inference (Washington, et al., 2010).

To address the above issue, researchers have used various types of sophisticated

discrete outcome modelling techniques for cyclist injury severity. For example, Eluru,

et al. (2008) developed a mixed generalised ordered response model to determine the

significant variables influencing cyclist and pedestrian injury severity using the 2004

General Estimates System (Desapriya, Pike, Brussoni, & Han) database in the USA.


The result of the study shows that inclusion of the mixed logit in the ordered response

model would be able to capture the unobserved heterogeneity. Another study

conducted by Behnood and Mannering (2017), demonstrated that the inclusion of

random parameters in the multinomial logit model was able to capture variation across

observations. Similarly, Moore, et al. (2011) showed that a mixed logit model can

account for the unobserved factors influencing the cyclist injury severity resulting

from crashes at intersection and non-intersection locations. However, the inclusion of

random parameters (mixed) logit in bicycle injury severity models has rarely been

applied.

Table 2.1 Summary of statistical approaches in studies of cyclist injury severity

Statistical approach Past studies

Ordered logit and ordered

probit

Klop and Khattak (1999), Zahabi, Strauss,

Manaugh, and Miranda-Moreno (2011),

Robartes and Chen (2017), Stipancic,

Zangenehpour, Miranda-Moreno, Saunier, and

Granie (2016)

Multinomial logit Kim, et al. (2007), Yan, et al. (2011), Rifaat,

Tay, and de Barros (2011)

Weighted multinomial logit Kröyer (2015)

Mixed logit Moore, et al. (2011), Pai (2011), Klassen, et al.

(2014)

Random parameters (mixed)

logit model

Behnood and Mannering (2017)

Mixed generalized ordered

response

Eluru, et al. (2008), Chen and Shen (2016),

Generalized ordered logit/

probit

Kaplan, et al. (2014b), Habib and Forbes (2014)

Logistic regression model Chong, et al. (2010), Bambach, Mitchell,

Grzebieta, and Olivier (2013), Siman-Tov, Jaffe,

Peleg, and Group (2012), Kim and Li (1996),


Statistical approach Past studies

Boufous, de Rome, et al. (2012), Bíl, et al.

(2010)

Binary logit Yan, et al. (2011)

Multivariate log-binomial

regression

Asgarzadeh, Verma, Mekary, Courtney, and

Christiani (2017)

There are several commonly used discrete choice models for cyclist injury

severity such as the multinomial logit model, binary logit model, and ordered probit

model. Moreover, advanced statistical modelling frameworks have been utilised to

provide further insight into the study of cyclist injury severity modelling. This

dissertation also uses advanced statistical modelling techniques to identify the factors

that influence severe cyclist injuries.

2.5 CHAPTER SUMMARY

The above review of the literature has clearly indicated that the analysis of cyclist

injury severity is vast and growing. It is evident that bicycle crash injuries are the result

of a complex interaction of numerous factors. Contributing factors associated with the

road characteristics, traffic characteristics, bicyclist/driver characteristics, and

environmental characteristics are also frequently explored in injury severity

modelling. Therefore, in this study, the aforementioned contributing characteristics are

used.

Although an extensive body of knowledge exists in the bicycle safety area, there

are still some inconsistent results about contributing factors (cross-intersections,

roundabouts, gender, rider age, curved road, posted speed limit, dark conditions,

inclement weather, driver age, and driver intoxication) which warrant further research.

It is important to note that contradictory results can come from differences in study

settings, data collection, and various methodological approaches.


As discussed in Section 2.3.4 on the influence of traffic characteristics, many

bicycle crash injury studies have ignored some key explanatory variables (e.g.,

vehicles pre-crash movement manoeuvres and travelling direction) in modelling crash

data. These variables, however, have important implications for safety and may have

significant influences on bicycle crash injury outcomes. Therefore, in injury severity

modelling, considering the event or action that puts a vehicle manoeuvre on the course

that makes the crash unavoidable might lead to finding the potential relationships

between contributing factors and injury severity.

Another major concern is the persistence of increased injury risk for cyclists that

occurs at intersections. Traffic control devices are often implemented to regulate

vehicular traffic on the roadway on which a vehicle is travelling just prior to the critical

pre-crash event. However, fatalities and injuries seem to remain constant at

intersections, despite years of research for intervention and guidance with regard to

factors that are likely to influence resulting cyclist injuries. Failure to make clear

connections between certain intersection crash types, and traffic control measures

suggests insufficient identification of causal factors for BMV crashes at intersections.

It is essential, therefore, to have a good knowledge of traffic control measures that

affect cyclist injury severity.

There are also some general concerns and limitations when working with bicycle

crash data. In the bicycle safety literature, much of the attention has focused on the

safety measures, but very little research has been done to develop alternative model

settings to adjust for the effects of the under-reporting on model parameters when

modelling bicycle crash data. The weighting method provides a flexible framework

that can adjust for the effect of under-reporting, such that the model fit is not

completely dependent on the police-reported data.


In most bicycle safety studies, all explanatory variables are analysed in a single

equation, ignoring unobserved heterogeneity (unobserved factors may vary between

individual crashes) and bringing large measurement errors in statistical modelling.

Many researchers have applied unordered discrete data models in injury severity

analysis to allow the explanatory variables to have a non-monotonic effect on the

dependent variables. Therefore, unordered discrete models need to be developed to

capture the interaction between factors and cyclist injury severity while accounting for

unobserved heterogeneity within the crash data.

This chapter has provided a review of the bicycle safety literature on several

issues related to modelling of bicycle crash data. The next chapter describes the dataset

used for this study and the selection of an appropriate methodology to develop a

statistically validated model.


Chapter 3: Research Design 35

Chapter 3: Research Design

The forgoing chapter showed that while there is a considerable number of

bicycle safety literature from both Australian and international research settings, little

is known about how to adjust for the effects of under-reporting in bicycle crash data

modelling. Consequently, there is limited understanding about the factors contributing

to cyclist injury severity. The current program of research sought to remedy some of

these gaps in the knowledge of bicycle safety.

This chapter is divided into five sections. It begins with the characteristics of the

study setting, while Section 3.2 describes the research methodology adopted to answer

the research questions explored in the four studies, and the discrete choice models

developed to achieve the aim of Studies 3 and 4. Section 3.3 describes the conceptual

framework adopted for the research. Sections 3.4 and 3.5 describe the datasets and the

ethical considerations.

3.1 STUDY SETTING

This research was conducted in the second largest Australian state, Queensland.

The climate in Queensland is temperate, with warm summers and mild winters, which

enables cycling all year round. The most recently published survey in Queensland

reports that 16.6 % of residents ride a bicycle in a typical week, which is marginally

higher than the Australian average (Austroads, 2017b). Cyclists are allowed to ride on

sidewalks unless they are signed “no bikes” and helmet wearing laws are mandatory

for bicycle riders and pillions of all ages in Queensland. There were 2,756,944 cars

registered in Queensland at 30 June 2016, comprising 72% of registered vehicles

(TMR, 2017). Australians drive on the left-hand side of the two-way roads, and a 50

36 Chapter 3: Research Design

km/h is the default speed limit for built-up areas in Queensland unless otherwise

indicated by signs. Queensland’s school zones are 40km/h during certain hours,

generally 7am to 9am and 2pm to 4pm.

3.2 RESEARCH DESIGN

Four separate studies (see Figure 3.1) were designed to answer the research

questions in Section 1.3. Each study focuses on one research question. Study 1

examined the common issues associated with police-reported crash data from the

literature, and then identified suitable data subsets to explore the characteristics of

bicycle crashes. Using police-reported data on bicycle-involved crashes collected from

Queensland Department of Transport and Main Roads (TMR), this study comprises

the following steps:

a) Review the Queensland crash data, which includes checks for quality and

consistency. The data must be cleaned and reviewed for any errors that may

have been made (such as cyclists “wearing seat belts”).

b) Understand the data limitations of different types of crash in police-reported

data.

c) Conduct a descriptive analysis of multi-vehicle bicycle crashes throughout the

state of Queensland. This exploration included an analysis of potential

contributing factors of bicycle crashes, such as crash characteristics,

environmental conditions, rider characteristics, and driver characteristics, as

well as roadway characteristics.

Study 1 informed the later studies by selecting appropriate datasets and

identifying the potential factors (i.e., variables in the dataset) that could be included in

the injury severity models developed in Studies 3 and 4. For example, Study 1 found

that approximately 63 percent of BMV crashes occurred at intersections in


Queensland, even though intersections make up only a small portion of the entire

roadway system. Other studies (e.g., Robartes & Chen, 2017) also reported large

proportion of BMV crashes occurring at intersections. Therefore, the results indicate

that it is worthwhile to study the intersection crashes of cyclists to identify contributing

factors affecting the injury severity of such bicycle motor-vehicle crashes.

Figure 3:1 Research steps

Study 2 developed a simple method to adjust for the effects of under-reporting

using the police-reported data and aggregate hospital admissions data as follows:

a) Police-reported and hospital databases were compared with respect to

demographics of road users involved in crashes and the crash circumstances

using cross tabulations of corresponding variables.

b) Weights were generated from the hospital database to apply to the police-

reported data base.

c) Descriptive analysis was performed to compare the crash patterns in the

weighted and unweighted data.

Based on the results obtained from Studies 1 and 2, Study 3 examined the effects

of traffic control characteristics on cyclist injury severity in BMV crashes. A mixed

logit modelling approach was utilised in this study following the tasks below:

a) A descriptive analysis was created to understand where and why bicycle

motor-vehicle crashes occurred.

STUDY 1 Understanding

crash

characteristics

and data

limitations

STUDY 2 Adjusting for

the effect of

under-

reporting

STUDY 3

The influence

of traffic

control

characteristic

on cyclist

injury severity

STUDY 4

The influence

of motor-

vehicle

trajectory on

cyclist injury

severity


b) Mixed logit models were developed in order to incorporate the unobserved

heterogeneity in injury severity modelling.

c) A likelihood ratio test was performed to check the suitability of separate

models under various traffic control measures compared with one aggregate

model.

d) Marginal effects were estimated in the mixed logit model to identify the

impacts of variables at each traffic control intersection.

Similar to Study 3, Study 4 used the results obtained from Studies 1 and 2 to

comprehensively and holistically explore how motor vehicle trajectory types

influenced cyclist injury severity in intersection crashes. The statistical modelling

approach was similar to the one used in Study 3 but focused on bicycle-light passenger

vehicle crashes. This study was achieved through the following process:

a) Mixed logit models were developed for trajectory types and crash type to

model cyclist injury severity in intersection crashes.

b) The likelihood ratio test was used to assess model performance.

c) Marginal analysis was conducted to quantitatively interpret the marginal

effects of contributing factors on cyclist injury severity.

3.2.1 Modelling approaches

Discrete choice modelling techniques were used to model the cyclist injury

severity at intersections. The detailed description of the variables will be given in

Chapters 6 and 7. The current section provides a brief description of existing

methodological problems of discrete choice modelling, followed by a section that

develops a mixed logit model to be used in Studies 3 (Chapter 6) and 4 (Chapter 7) to

overcome these methodological problems.


In traffic safety research, the discrete ordered probit (OP) model is one of the

most popular techniques for modelling injury severity due to its efficiency, consistency

in estimation and because it can accommodate a natural order of injury severity levels

(e.g., Abdel-Aty & Keller, 2005; O'Donnell & Connor, 1996; Siddiqui, Chu, &

Guttenplan, 2006; Tay & Rifaat, 2007; Wang & Abdel-Aty, 2008; Yamamoto, et al.,

2008; Zhu & Srinivasan, 2011). This modelling technique is widely used because

severity outcomes are discrete and ordered from low severity to high severity (e.g.,

minor injury, medically treated, hospitalised and fatality). However, traditional OP

models restrict the influence of explanatory variables to either increase the probability

of individual sustaining injury, for instance, hospitalised injury (and subsequently

decrease the probability of minor injury) or decrease the probability of hospitalised

injury (and subsequently increase the probability of minor injury) . Washington, et al.

(2010) provide the following example. Consider if an airbag is activated in a crash, it

is expected that there would be a simultaneous increase (or decrease) in the probability

of a hospitalisation or minor injury, because airbag deployment may cause complaints

of pain or minor injuries.

In order to address the limitations of ordered choice models, unordered choice

models have become popular in injury severity modelling (Eluru, 2013). However, one

of the main problems of unordered choice models is the assumption that the alternate

severity outcomes are independent and, if these are not taken into account in the

analysis, the model can lead to erroneous estimations of injury likelihood (Mannering

& Bhat, 2014). Each of these methods has strengths and limitations, which are

documented with possible solutions in a review by Savolainen, et al. (2011). It is

believed that advanced models such as the mixed logit model (MXL) could ease such


issues by allowing for a more general error-correlation structure to averts this error

term problem (Hensher & Greene, 2003).

3.2.2 Model development

To provide some insight into cyclist-injury severity in this context, three discrete

severity levels are considered: fatal/hospitalised injury, medical treatment injury, and

minor injury. As found later in the analysis (section 7.1), the numbers in fatal and

hospital injury levels warranted combining two highest severity levels for a sound

statistical analysis. Following the previous work of Milton, Shankar, and Mannering

(2008) and Washington, et al. (2010), model development starts with equation 3.1,

which constructs a severity function for each injury outcome:

𝑊𝑖𝑛 = 𝛽𝑖𝑋𝑖𝑛 + 𝜀𝑖𝑛 (3.1)

where 𝑊𝑖𝑛 is a severity function that determines injury 𝑖 severity for crash-involved

cyclist 𝑛, 𝛽𝑖 is a vector of estimable parameters for injury severity 𝑖 which may vary

across observations, 𝑋𝑖𝑛 is a vector of explanatory variables (driver, rider, and

environmental attributes) associated with injury severity 𝑖 for observation 𝑛, and 𝜀𝑖𝑛

is the error term which is assumed to be distributed as a random variable following

Gumbel distribution (McFadden, 1981). Thus the standard multinomial logit model

(MNL) of the probability of the 𝑖𝑡ℎ injury outcome is (see Greene & Hensher, 2003;

McFadden & Train, 2000):

𝑃𝑛(𝑖) = exp[𝛽𝑖𝑋𝑖𝑛]

∑ exp[𝛽𝑖𝑋𝑖𝑛]∀𝐼 (3.2)

where 𝑃𝑛(𝑖) is the probability of the 𝑖𝑡ℎ injury severity outcome for the 𝑛𝑡ℎ

observation in a MNL model. The MXL is generated from MNL by allowing the

parameter 𝛽𝑖 to vary across individual cyclists. To allow for parameter variations

across observations, a mixing distribution is introduced to the model formulation for

crash-involved cyclist injury severity:


𝑃𝑛(𝑖|𝜑) = ∫exp[𝛽𝑖𝑋𝑖𝑛]

∑ exp[𝛽𝑖𝑋𝑖𝑛]∀𝐼 𝑓(𝛽|𝜑) 𝑑𝛽 (3.3)

where, 𝑃𝑛(𝑖|𝜑) is the probability in mixed logit injury severity analyses, with the

density function 𝑓(𝛽|𝜑) used to determine 𝛽 with parameter vector 𝜑, and all other

terms are previously defined by Chen and Chen (2011).

3.2.3 Impact of model parameters

Mixed logit model results are not sufficient to understand the magnitude of the

impact of explanatory variables on the outcome probabilities. In addition, the sign of

the coefficient for an explanatory variable does not always determine the direction of

its effect on injury severity outcomes. Standard elasticity values show how a

percentage change in an independent variable will affect the likelihood of the three

injury severity outcomes. However, it can’t be interpreted with respect to a 0 or 1

indicator variable. Therefore, the pseudo-elasticities are introduced to measure the

effect of the percentage change in average probability of a particular injury-severity

category when an indicator variable is switched (i.e., from 0 to 1 or from 1 to 0):

𝑃𝐸𝑋𝑛𝑘

𝑃𝑛(𝑖)=

𝑝𝑛 (𝑖|𝑋𝑛𝑘 = 1)− 𝑃𝑛(𝑖|𝑋𝑛𝑘 = 0)

𝑃𝑛(𝑖|𝑋𝑛𝑘 = 0) (3.4)

where 𝑃𝐸𝑋𝑖𝑛

𝑃𝑛(𝑖) is the direct pseudo-elasticity of the kth variable from the vector 𝑋𝑛,

denoted 𝑋𝑛𝑘, with respect to the probability, 𝑃𝑛𝑖, of person 𝑛 experiencing outcome 𝑖.

For further discussion on elasticities, see Washington, et al. (2010). The direct pseudo-

elasticity is calculated for each individual by averaging the simulation-based

elasticities over all observations, which has been widely utilised due its computational

efficiency in previous studies (Kim, et al., 2007; Moore, et al., 2011).

3.2.4 Parameter selection criteria

The data from Chapters 6 and 7 are used to develop the mixed logit model. Steps

in parameter selection are shown in below:


1. Explanatory variables were chosen if they were shown to have a relationship

with dependent variables (e.g., injury severity) in the univariate analysis

(p<0.2)

2. These explanatory variables were included in the mixed logit model. A

backward elimination procedure (Haque, Chin, & Debnath, 2012) was adopted

to remove the non-significant explanatory variables one by one so that the AIC

was minimised in the most-parsimonious model.

3. Estimated parameters were found to be random which produced statistically

significant standard errors for their assumed distribution. If the standard error

was not found to be significantly (p < 0.05) different from zero, it was assumed

that the parameter becomes fixed across all observations. Based on previous

research (Anastasopoulos & Mannering, 2009; Bhat, 2003; Gkritza &

Mannering, 2008; Russo, et al., 2014), a simulation-based maximum likelihood

method with 200 Halton draws was used for accurate parameter estimates.

Regarding random parameters’ density functional forms, the normal

distribution gave the best fit results among the normal, uniform, and lognormal

distributions for the injury severity data, which is in line with past studies

(Moore, et al., 2011; Pai, et al., 2009). The statistical software tool, NLOGIT

6, was used for model parameter estimation.

4. Rotation of variables in or out of the mixed logit model was continued until

best possible fit model can be found with minimum AIC value.

5. Chi-square p-value was used to test if the model significantly fits better than

the other model. Recorded the model AIC value and compared it with the other

candidate model AIC values. The preferred model was the one with minimum

AIC value among all candidate models.


3.2.5 Model fitness

Likelihood ratio tests were performed to examine if the explanatory effects were

consistent for different categories of a variable (e.g., traffic control type) and

accordingly determine if a combined or disaggregate model is necessary. The test

statistic of the likelihood ration test is:

𝑋2 = −2[𝐿𝐿𝐹𝑢𝑙𝑙(𝛽𝐹𝑢𝑙𝑙) − ∑ 𝐿𝐿𝑗(𝛽𝑗)𝐽𝑗=1 ] (3.5)

where 𝐿𝐿𝐹𝑢𝑙𝑙(𝛽𝐹𝑢𝑙𝑙) is the log likelihood at convergence of the mixed logit model,

𝐿𝐿𝑗(𝛽𝑗) is the log likelihood at convergence of subgroup j using the same variables

included in the mixed logit model. The χ2 statistic, with degrees of freedom equal to

the summation of the number of estimated parameters in aggregate models minus the

number of estimated parameters in the disaggregate model, provides the confidence

level at which the null hypothesis can be rejected. The null hypothesis is that full model

parameters estimates are no better than the separate model estimate.

3.3 CONCEPTUAL FRAMEWORK

The Safe System Approach (SSA) was used to structure this research program

(Figure 3.2). Theoretically, the SSA as adopted in the National Road Safety Strategy

in Australia consists of four key elements: Safe people, Safe road infrastructure, Safe

speeds, and Safe vehicles (NRSS, 2011). The combination of vehicle safety features,

road infrastructure safety features, and travel speed(s) of crash-involved vehicles

determines the impact forces that road users are subjected to in any crash (Larsson,

Dekker, & Tingvall, 2010). Fatal or serious injury outcomes are largely determined by

these factors.


Figure 3:2 Safe System Approach

Safe roads, the infrastructure element, itself includes many components that

interact together to determine the vulnerability of a road user. For example, a crash can

occur when two or more road users (e.g., a cyclist and a vehicle driver) occupy the

same space in the road (e.g., a vehicle intruding into a cycling facility) (Harkey &

Stewart, 1997). Therefore, it is important to consider variables related to the road

network and the environment in this thesis because cyclist/driver actions on the road

are facilitated and shaped by the design of the road.

Given the risk of severe injury in a crash between a vehicle and cyclist, it is

necessary that drivers and cyclists interact safely to improve the safety of on-road

cyclists. For instance, the interactions between vehicle and infrastructure involve how

vehicle dynamics behave under different road surface conditions (e.g., wet and

slippery roads, dry roads, uneven roads) (Klop & Khattak, 1999). A low coefficient of

friction between vehicle tyre and road surface under wet and slippery conditions could

contribute to crashes, and particularly for bicycles, which have relatively less tyre area

in contact with the road surface than other types of vehicles.


Safer speeds generally refer to both the speed of the vehicle and the posted speed

limit, with lower speed limits on high risk roads being synonymous with safer speeds.

Road safety researchers have clearly found that increased vehicle speed is associated

with increased injury severity in the event of a crash between a cyclist and a motor

vehicle (Boufous, Rome, Senserrick, & Ivers, 2012). However, it is argued that safe

speeds vary depending on the road environment and individual skills of the road user

(Wilmot & Khanal, 1999). Therefore, it is a challenging area because of the variation

in mass and speed of bicycles and motor-vehicles.

Safe people mainly relates to the behaviour of road users which can influence

injury severity. It is essential for road users of the systems to behave in ways that will

allow the system to mitigate the injury impacts of a crash. For example, the effect of

alcohol involvement can influence injury severity, while not wearing helmets can

increase injury severity. With regards to rider’s age, the higher probability of injury

among child cyclist may reflect the inability of the rider to take evasive manoeuvres

to reduce the crash impact because of the lack of riding experience. Therefore, driver

and cyclist characteristics were considered as part of the research.

In the traffic safety literature, modelling injury severity relates to an individual

crash or injured person. Therefore, location-based exogenous factors, such as

characteristics of road sections, weather, lighting, posted speed limit etc. also influence

the injury severity of crashes. Therefore, all system factors were considered in injury

severity modelling.

3.4 CRASH DATA ELEMENTS

The genesis of the description of a crash occurs at the crash site itself. After the

crash occurs and police are notified, a police officer must come to the scene of the


crash and record the details. In Queensland, every crash that meets the following

criteria must be reported:

The crash occurred on a public road, and

A person was killed or injured, or

At least one vehicle was towed away, or

There was $2500 damage to property other than vehicles

Table 3.1 Crash database structure

(Source: Adapted from data analysis road crash glossary, February, 2014)

Categories Variables Description

Crash Atmospheric condition e.g., Clear, fog, rain, smoke

Day of week e.g., Monday, Tuesday

DCA group Definition for coding accidents (e.g.,

U-turn, rear-end)d

Horizontal road

alignment

e.g., Curved, straight

Impact allocation e.g., Off-road, on road

Lighting condition e.g., Darkness, daylight

Month e.g., January, February

Crash nature e.g., Angle, head-on

Crash type e.g., Single vehicle, multi-vehicle

Road surface e.g., Sealed-dry, sealed wet

Road features e.g., Intersection-cross

Speed limit e.g., 60 km/h

Time e.g., Midnight, 1am, 2am

Traffic control e.g., Stop sign, no traffic control

Vertical road alignment e.g., Crest, dip

Casualty Injury severity e.g., Fatal, hospitalised

Unit

/controller

Alcohol/ drug related e.g., Condition under influence of

drug

Atmospheric conditions e.g., heavy rain, smoke


Categories Variables Description

Controller conditions e.g., driver distracted

disobey road rules e.g., dangerous driving

Disobey traffic

light/sign

e.g., disobey red traffic signals

Fail to give-way or stop e.g., disobey giveaway sign

Fatigue related e.g., driver fatigue

Lighting conditions e.g., headlight glare

Rain/wet/slippery road e.g., heavy rain

Road conditions e.g., road narrow, potholes

Speed related e.g., exceeding speed limit

Every crash that is reported to the police has some characteristics which are

grouped into three categories: ‘Crash’, ‘Unit/Controller’, and ‘Casualty’. Within each

category, there are many characteristics that explain various attributes of that

Category, as seen in Table 3. A unit is defined as any motor vehicle, bicycle, pedestrian

conveyance, animal or trailer, attended or unattended, involved in a crash. Controller

can be defined as a person who exercise control over the movements of a vehicle at

the time of a crash (i.e. driver, rider or pedestrian). Passengers and pillions are not

considered controllers (TMR, 2014). ‘At-fault’ status in the current analysis refers to

the unit deemed ‘most at-fault’ in a road traffic crash. This is either determined by

police or attributed to the unit committing a traffic violation. Injury severity is derived

from the most severe casualty resulting from the crash.

The age intervals for drivers were determined based on Queensland driving

licensing system, while cyclist age group were based on school groups (e.g., pre-

school, primary, and secondary) which differ significantly from each other in terms of

kinematics, nature of injuries, and their outcomes. This age grouping strategy is similar


to that used in other research in Queensland (Haworth & Debnath, 2013;

Rakotonirainy, Steinhardt, Delhomme, Darvell, & Schramm, 2012).

This analysis uses the same severity groupings specified in the Queensland Road

Crash Glossary (TMR, 2014), which includes:

1. Fatal crashes (person died within 30 days of the crash from resulting injuries).

2. Hospitalisation crashes (injury crash requiring hospitalisation).

3. Medical treatment crashes (injury crash requiring medical treatment).

4. Minor injury crashes (injury crash requiring no medical treatment, i.e., minor

injury, first-aid only or extent of injury unknown).

It is important to note that although police officers do acquire personal

information from the parties involved, such as name and address, this information is

not available publicly and the database does not contain any information to link the

crash to the parties involved. The de-identified data is intended only for statistical and

research purposes in compliance with the Transport Operation (Road Use

Management) Act, 1995.

3.4.1 Merging files

Each of the crash files (e.g., Crash’, ‘Unit/Controller’, and ‘Casualty) contains a

unique crash reference number, which relates all records to their respective crash

events. All the variables were gleaned from the ‘Crash’ and ‘Unit/Controller’ files,

then merged into the ‘Casualty’ file. This merging was done by using the crash

reference number. STATA (version 13.1) software was used to merge the files. All the

records were checked systematically to keep the data effective and consistent. This

consistency was checked by two steps. Firstly, each category of the variables were

checked in detail to identify the missing values. Secondly, aggregated casualty records

were cross-checked again for consistency.


3.5 HEALTH RISK ASSESMENT AND RESEARCH ETHICS

There is no explicit requirement for ethics approval to request data from

Queensland Department of Transport and Main Roads (TMR), but ethics approval is

required to access the de-identified data as a researcher within a university context.

This research was conducted in accordance with the health and safety policy of QUT.

The major portion of this research utilised secondary data such as maps, databases

related to infrastructure and asset management at TMR and Brisbane City Council.

The ethics exemption application for the research was approved on 15 September,

2015. Ethics approval was obtained for hospital data from the Queensland University

of Technology’s Human Research Ethics Committee (#1100001065).

3.6 CHAPTER SUMMARY

In this chapter, the relationships between the research aims, research questions

and research studies have been described, and a rationale provided for each of these

research components. Mixed logit models were also proposed and developed to

identify the factors that influence cyclist injury severity. Model calibration, model

fitness criteria and the effects of the parameter estimate techniques of those models are

briefly described. The next chapter presents Study 1, which addresses the first research

question, and identifies the appropriate datasets for the injury severity models.


Chapter 4: Bicycle crash patterns and trends 51

Chapter 4: Bicycle crash patterns and

trends

This chapter documents the first study undertaken as part of the research

program. It provides a foundation for the later studies by identifying the appropriate

datasets for the injury severity models. Thirteen years of data for bicycle-involved

crashes reported to police in the Queensland were analysed in this study. The data were

descriptively analysed to understand the patterns of bicycle crashes by examining

issues related to under-reporting, availability of variables in the dataset, and the extent

of missing data for the variables. While the analyses in this study started with all

bicycle involved crashes recorded in the police-reported dataset (note that off-road

crashes are not reportable), potential under-reporting issues identified from the

literature indicated that the following studies should include only multi-vehicle bicycle

crashes in major cities and inner/outer regional areas. These data subsets were further

analysed descriptively to identify the potential explanatory variables (i.e., factors

affecting injury outcomes) to be used in modelling injury severity in Studies 3 and 4.

4.1 CRASH DATA SOURCES

Police-reported crashes which involved at least one cyclist were obtained from

Transport and Main Road (TMR) for the thirteen-year period January 2002 to

December 2014. The data before 2002 were not considered because of inconsistency

in the data coding, while the data after 2014 were not available during the time of

analysis. These data were provided as three separate datasets: crash-related

information, crash-involved road user related information, and crash-casualty related

information. The first dataset provided information related to the crash location, time,

52 Chapter 4: Bicycle crash patterns and trends

type, weather etc. The second dataset provided information related to the road users

involved in crashes (referred to as ‘Unit/controllers’ in the data), including their age,

gender, at-fault status etc. The final dataset provided information on the injury severity

levels of all road users involved in crashes. The data only includes crashes that

occurred in public roads, i.e., it excludes crashes that occur on private property, in car

parks, or in similar off-road environments.

To understand the limitation of police-reported data, the aggregate hospital

admission dataset was considered as an alternative sources of data on bicycle related

injuries. Records from all causalities of traffic crashes admitted to hospital between

2009 and 2010 were extracted from the Queensland Hospital Admitted Patients Data

Collection (QHAPDC).

4.2 DATA ANALYSIS

The police-reported crash data were analysed to identify data subsets for

inclusion in the next studies by understanding issues related to under-reporting of

crashes, and identifying the potential variables that could be used in Study 3 and 4 as

explanatory variables in regression models. For identification of crash data subsets,

the crash data were analysed descriptively in order to obtain the counts and percentages

of crashes for various variables of interest (location, crash type, severity levels etc.).

These percentages, in combination with findings from existing studies about crash data

under-reporting, along with comparison of police-reported data and aggregate hospital

data were performed to understand the extent of under-reporting exist in the crash data,

provided the ground for this analysis. To identify the potential variables for Studies 3

and 4, a similar descriptive analysis of the crash data was undertaken where the

potential factors of injury severity (i.e., the variables available in the crash dataset)

were examined for missing values, unknown observations, and incorrect information


(e.g., a child driving a vehicle, or a cyclist wearing a seat belt). The rationale was that

only those factors for which the extent of missing data, unknown observations, and

incorrect information is limited or none could be included in the later studies.

4.3 RESULTS AND DISCUSSION

This section is divided into two main sub-sections based on the study objective.

In the first sub-section, the trends in crash data are presented for the thirteen years

2002 to 2014 and appropriate subsets of police-reported data are identified for the next

studies. The second sub-section presents an analysis of the data subset to understand

the characteristics of crashes, availability of variables in the datasets, and issues related

to missing data.

4.3.1 Selection of data subsets for later studies

The 10,488 police-reported crashes involving bicycles are summarised in Figure

4.1. While cycling has become more popular in the last few years (BITRE, 2016), the

number of bicycle crashes exhibits an overall trend remained relatively constant during

the study period 2002-2014.

Figure 4:1 Bicycle crash type by year

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Single-vehicle 116 68 90 94 72 88 82 90 110 91 107 116 128

Multi-vehicle 757 697 745 714 686 655 728 751 690 645 695 649 656

Hit pedestrian 12 15 9 10 10 4 8 9 9 11 13 17 12

Other 4 9 5 2 1 0 1 1 1 1 4 3 3

0

100

200

300

400

500

600

700

800

900

1000

Nu

mb

er o

f cr

ash

es


There were 1252 single-vehicle (SV) bicycle crashes (11.9% of all crashes)

reported to police in Queensland between 2002 and 2014. The multi-vehicle (MV)

crashes involving at least one bicycle, on the other hand, accounted for 86.5% of all

crashes (n=9,067), indicating a large difference in the proportions of the SV and MV

bicycle crashes. Additionally, other crash types (e.g., a bicycle hitting a pedestrian)

were also low (1.3%) compared to the SV and MV bicycle crashes, which is excluded

from further analysis. These crash numbers need to be interpreted with extreme caution

as existing studies (e.g., Watson, et al., 2015) argued that the actual number of crashes

is likely to be higher due to the under-reporting of bicycle crashes. As discussed in

Section 2.2, bicycle crash data suffers from serious under-reporting issues in many

jurisdictions, including Queensland. The literature showed that SV bicycle crashes

were less likely to be reported compared to MV bicycle crashes. For example, linking

hospital data and police-reported data for 2009 in Queensland, (Watson, 2014) showed

that only 5% of SV bicycle crashes and 35% of MV bicycle crashes were recorded in

the police-reported database. Note that these statistics are only for the crashes resulting

in at least one ‘hospitalised’ injury.

Figure 4:2 Comparison between number of cyclists reported by police as hospitalised

(QRCD) and hospital admissions (QHAPDC) from SV bicycle crashes

2009 2010

QRCD 38 59

QHAPDC 434 426

0

50

100

150

200

250

300

350

400

450

500

Num

ber

of

ho

spit

alis

ed i

nju

ry

SV bicycle crash injuries


As shown in Figure 4.2, during the period 2009-2010, there were 860 records of

cyclists injured in SV bicycle crashes admitted to hospital, whereas the police-reported

data shows only 97 cyclists were recorded as taken to hospital. This clearly indicates

the under-reporting in police data. Since it is understood that SV bicycle crashes are

less likely to be reported that MV bicycle crashes, it is proposed to exclude SV bicycle

crashes from later analysis in this research. The remaining crashes (i.e., MV bicycle

crashes) are not free from under-reporting issues, but at least the extent of under-

reporting is much lower than for SV bicycle crashes. To provide further assurance, the

next study will employ methods designed to adjust for the effects of under-reporting

in the police data.

Figure 4:3 Selection of data subsets

Some other types of crashes (e.g., bicycle hitting a pedestrian, crashes involving

animals) were also excluded from the later studies due to their relatively low

frequency. If retained in the dataset, such low crash counts could have had implications

on statistical modelling of the crashes in Studies 3 and 4. Figure 4.3 shows the dataset

selection steps.

Multi-vehicle bicycle

crashes (n= 9,067)

Excluded:

Remote, very remote, and unknown

areas (n=138)


crashes in non-remote

areas

(n= 6,967)

Crashes involving bicycles

(n= 10,488)

Excluded:

Single-vehicle bicycle crashes

(n=1252)

Hit pedestrian (n=139)

Others (n=30)


The extent of under-reporting in police data varies greatly by the crash locations

specific information. The Accessibility/remoteness index of Australia (ARIA+)

represents one measure of geographical classification of remoteness across Australia.

Figure 4.4 presents the results obtained from the preliminary analysis of police-

reported MV bicycle crashes by ARIA+ classifications to develop an understanding of

the under-reporting issues. Major cities had the highest proportion (65.4%) of crashes.

However, numbers of crashes in very remote and remote areas were very low (.3% and

.8% respectively), indicating that there is possibility of under-reporting in these areas.

Watson, et al. (2015) found that remote, very remote and inner regional areas had

greater levels of under-reporting in police-reported data compared to other areas, and

in Denmark, injuries in rural areas were less likely to be reported than those in urban

areas (Kaplan, et al., 2017). Therefore, this study will exclude numbers of MV bicycle

crashes in remote, very remote, and unknown (0.2%) areas.

Remoteness classification % of injuries

Major cities 65.4

Inner regional 16.0

Outer regional 17.3

Remote 0.8

Very remote 0.3

Figure 4:4 MV bicycle crashes by ARIA+ location, QRCD 2002-2014

4.3.2 Descriptive analysis of selected data subsets

The selected data subset included 8,941 MV bicycle crashes occurring from

January 2002 to December 2014. Most of the MV bicycle crashes resulted in injuries

requiring medical treatment (40.58%) or hospitalisation (37.85%) or minor injury


(20.38%). There were very few fatal (1.19%) crashes (see Figure 4.5). The low number

of minor injuries may reflect under-reporting, as Elvik and Mysen (1999) found that

90 percent of minor injuries were not reported. In the case of hospitalised injury,

Rosman (2001) explains that in Western Australia, because of under-reporting, about

40 to 45 percent of hospital admission records did not have a corresponding police

record. Similarly, in 2009, police data reported that 362 cyclists were injured in

Queensland due to MV bicycle crashes, however, hospitals recorded that 1067 cyclists

were admitted following a MV bicycle crash (Watson, 2014).

Figure 4:5 Injury severity of police-reported MV bicycle crashes by year

MV bicycle crashes are more frequent during daylight hours. Figure 4.6 shows

that the morning (6am-11.59am) and the early evening (12pm-6pm) are the time

periods when the greatest numbers of MV bicycle crashes occurred. Similarly, about

42.5% of crashes occurred during the morning peak hours. The time periods with the

most crashes were 2pm-6pm, followed by 6am-9am. More than half of the MV bicycle

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Fatality 6 5 7 3 8 9 4 6 6 8 8 8 7

Hospitalised 238 210 263 262 233 231 275 306 275 233 289 295 275

Medically treated 294 284 276 273 259 261 295 288 289 275 286 261 288

Minor injury 201 185 190 164 180 146 143 144 112 120 103 77 77

0

50

100

150

200

250

300

350

Nu

mb

er o

f in

juri

es


crashes (84.3%) occurred during these two peak time periods. These results are likely

due to higher volumes of both MV and bicycle traffic during peak hours. Eilert-

Petersson and Schelp (1997) found that both motorists and cyclists tend to drive and

ride more aggressively during the busy morning commute, and such behaviour could

increase cyclist injury severity. The variations in the percentage of crashes over the

time periods indicate that ‘time of day’ might be a useful predictor of crash occurrence

and possibly injury severity.

Figure 4:6 MV bicycle crashes by time of the day

Other variables available in the police-reported crash dataset that might have

potential association with crash occurrence and injury severity levels are presented in

Table 4.1. More multi-vehicle bicycle crashes occur during weekdays (80.3%) than

weekends (19.7%), perhaps due to higher MV traffic volumes during weekdays.

Saturdays had more crashes (11.2%) than Sundays (8.5%). According to MV crash

data, weekdays (79%) were the most common days for MV crash to occur, with MV

crashes being least frequent on weekends. This ‘day of week’ variables appears

promising for inclusion in the later studies.

4.8

42.5 41.8

10.9

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

00 am-5.59 am 6am-11.59 am 12 pm- 5.59pm 06 pm-11.59 pm

% o

f cr

ash

es


Table 4.1 Descriptive statistics for MV bicycle crashes

Variable name Percentage of MV bicycle crashes

(N=8941)

Day

Weekday 80.3

Weekend 19.7

Speed limit zone (Km/h)

0-50 29.3

60 61.8

70 4.50

80-90 3.10

100-110 1.40

Unknown 0.00

Traffic control type

Give-away sign 24.5

No traffic control 58.3

Operating traffic signals 11.2

Stop sign 3.90

Pedestrian crossing sign 1.60

Others 0.50

Road condition

Dry 93.0

Wet 6.97

Unknown 0.03

Horizontal alignment

Curve 12.8

Straight 87.2

Unknown 0.00

Vertical alignment

Crest 2.80

Dip 3.10

Grade 15.6

Level 78.6

Lighting conditions

Darkness-lighted 9.50

Darkness- not lighted 2.20

Dawn/Dusk 8.00

Daylight 80.3

Unknown 0.10

Crash nature

Angle 79.6

Head-on 1.70

Rear-end 5.50

Sideswipe 13.2


Type of traffic control appears to have associations with bicycle crashes,

implying its suitability for inclusion into later studies. Table 4.1 shows that 58.3% of

the MV bicycle crashes occurred where there were no traffic controls, 24.5% where

there were give-way signs, 11.2% where there were operating traffic signals, and 3.9%

where there were stop signs. This trend of MV bicycle crashes were quite similar to

MV crashes. Since, 2002, the trend in the distribution of MV bicycle crashes by type

of control shows an increasing proportion at give-way signs and a decreasing

proportion occurring in areas of no traffic control.

Posted speed limit also appears to have a strong relationship with MV bicycle

crashes. Most (61.8%) MV bicycle crashes occurred on roads with a 60 km/h speed

limit. Similarly, almost 60 % of MV crashes occurred where the posted speed was 60

km/h. Most urban areas and areas in Queensland that have high populations have speed

limits between 0-60km/h. Therefore, it is logical that MV bicycle crashes were most

likely to have occurred at suburban speed limits, with around 90% occurring on

roadway with a speed limit of 0-50 km/h or 60 km/h.

While most MV bicycle crashes occurred on straight sections (87.2%) and level

roads (78.6%), a sizeable proportion occurred while the cyclist was riding on a curve

(12.8%) or grade (15.6%) which could be up or downhill. The horizontal (12.3%) and

vertical (14.6%) road alignment was strikingly similar to MV crash data. As discussed

in Section 2.5, a key gap in the literature is the limited evidence regarding the potential

contribution of horizontal and vertical alignment to bicycle crash outcomes. However,

the above statistics indicate that these variables may have an influence on MV bicycle

crashes, implying that they should be included in the later studies in this research.

As expected, most MV bicycle crashes occurred in daylight conditions (80.3%),

whereas 11.8% of the MV bicycle crashes occurred during the dark. These proportions


seems reasonable due to the number of trips being higher during daylight conditions.

For example, Dill and Gliebe (2008) observed that the purpose of most bicycle trips,

such as commuting to work, school, shopping, and social trips, occur during daylight.

Only 8.0% of MV bicycle crashes occurred during the dawn and dusk periods.

Lighting condition was coded as unknown for .1% of MV bicycle crashes. With

regards to MV crash data, about 20% of MV crashes occurred during dark, dawn, or

dusk conditions.

Most notable in the context of this study is that nearly 80% of the MV bicycle

crashes are angle crashes (see Table 4.1). Most of the angle crashes occurred in speed

zones of 60 km/h and at T-junctions. The next most frequently occurring crash types

were sideswipes (13.1%) and rear-end crashes (5.4%). Further analysis found that the

proportion of angle and rear-end crashes as a percentage of all the MV bicycle crashes

decreased over the last ten year period, while in recent years the proportion of

sideswipe MV bicycle crashes has also increased.

Figure 4:7 MV bicycle crashes by road type

The analysis of crash locations (see Figure 4.7) showed that intersections

(61.9%) and midblock sections (34.7%) account for almost all types of locations.

Among the intersection crashes, 32.5% occurred at T junctions, 15.3% at cross

intersections, 13% at roundabouts, and the remaining 1.1% at other junctions. These

Road type

Intersections

(61.9%)

T junction

(32.5%)

Cross intersection

(15.3%)

Roundabout

(13%)

Other junction

(1.1%)

Midblock

(34.7%)

Other

(3.4%)


results are expected due to cyclists’ higher exposure to traffic (including cross-traffic)

at intersections and are in line with the literature (Moore, et al., 2011; Schepers,

Kroeze, Sweers, & Wüst, 2011). While intersections involve traffic movements from

multiple directions, midblock sections typically involve single-direction or both-way

direction movements. These differences in traffic interactions could essentially

produce different crash causation processes for these two types of road sections.

Therefore, the ‘type of traffic control’ variable should be considered in modelling

injury severity (Study 3). While caution is needed to determine the traffic control

variables underlying MV bicycle crashes, separate models for each traffic control type

might be required. It is to be noted, however, that low crash frequencies for each of

the traffic control intersection types may not allow modelling separately for individual

intersection types.

4.4 CHAPTER SUMMARY

This study identified the data subsets and the potential variables to be included

in modelling injury severity in MV bicycle crashes in Studies 3 and 4. A significant

portion of SV bicycle crashes go unreported and poorly documented in police-reported

data. Thus, any conclusions about characteristics of SV bicycle crashes might be

misleading. For this reason, police-reported MV bicycle crash data was selected for

the descriptive analyses to understand the crash characteristics.

The results of the descriptive analysis were found to be plausible and intuitively

reasonable. The results revealed a few important points that should be considered for

the later studies. While both intersections and midblock are common locations for MV

bicycle crashes, the number of MV bicycle crashes is higher at intersections than

midblock locations. It is reasonable to conclude that the combination of road,

environment, speed, traffic and road user behaviour are likely potential factors for the


higher incidence of serious injury crashes in intersection areas compared to midblock

areas. The findings from this study relating to the availability and completeness of

variables assisted in selecting which data and variables to use in the later studies.

While this chapter has demonstrated the deficiency in police-reported crash data

and characteristics of MV bicycle crashes, earlier research has shown that the extent

of under-reporting differs by injury severity. The next chapter develops an effective

method to adjust for under-reporting that enables an assessment of whether traffic

control measures and trajectory types remain useful constructs to apply to the adjusted

data.


Chapter 5: Weighting as a simple approach to adjust for under-reporting 65

Chapter 5: Weighting as a simple approach

to adjust for under-reporting

The literature review and Study 1 have identified that under-reporting of bicycle

crashes to police is a worldwide problem, which is also evident in Queensland. While

hospital data provide more complete reporting of crashes which result in that level of

severity, they do not provide the types of information about crash locations and

circumstances needed to guide infrastructure solutions. Linkage of individual police

and hospital records can provide a valuable resource, but this is not always feasible for

reasons of data availability or cost. Therefore, this study seeks to address Research

Question 2 by examining whether the use of aggregate hospital data to weight police

data is a useful compromise in improving data quality. The weighted data is then

compared with the unweighted data to assess whether the outcomes of the process call

into question the crash location and characteristics analyses of unweighted data in

Studies 3 and 4.

5.1 METHOD

5.1.1 Data

Two sources of data were utilised in this study. The Queensland Road Crash

Database (QRCD) contained data for 2690 riders in BMV crashes reported to police

in Queensland, Australia in 2005-2014 where the injury severity was coded as

“hospitalised injury”. The Queensland Hospital Admitted Patient Data Collection

(QHAPDC) contains data on all patients discharged, deceased, and transferred from

Queensland public and private hospitals. Transfer to another hospital was excluded to

avoid double counting in QHAPDC. Pedal cyclists (n=1194) injured in a traffic

66 Chapter 5: Weighting as a simple approach to adjust for under-reporting

incident (i.e., that occurred on a public road and involved with motor vehicle; coded

as ICD-10-AM External Cause Codes from V00-V89) were extracted for the period of

2009-2010 for the purpose of comparison with QRCD. This period of data was used

as it was the most recent available aggregate dataset to which access was available.

The proportion of the data relating to remote and very remote areas was lower

in QHAPDC compared to QRCD. A possible explanation is that the QHAPDC

Accessibility/Remoteness Index of Australia (ARIA+) relates to the location of the

hospital, whereas QRCD ARIA+ relates to the location of the crash. Injured persons

may not be treated in a hospital in a remote and very remote area due to the lack of

treatment facilities (Watson, et al., 2015). Therefore, hospitalised injuries in remote

and very remote areas were excluded from both datasets in order to reduce the bias in

the result.

5.1.2 Weighting procedure

Weighting can be used to correct for known inconsistencies between two data

sources (Gelman, 2007; Lohr, 2009) and can be used to adjust for under-reporting to

police of crashes involving cyclists. In weighting, the QRCD formed the data to be

weighted and the aggregate QHAPDC data were used to derive the weights. The

variables used for this weighting process had to be available in both datasets. For this

reason, the weighting process was restricted to three variables, “age,” “gender,” and

“remoteness classification”. The definitions of these values were equivalent to the

police definitions of the same variables. The weighting procedure consisted of a simple

adjustment of the above three dimensional contingency table to the corresponding

table of the crash statistics.

The formula of the proportional weight is, 𝑤𝑗 =

∑ 𝑁𝑗𝐽𝑗=1

𝑁⁄

∑ 𝑅𝑗𝐽𝑗=1

𝑅⁄

(5.1)


The unit is divided into J post stratification cells, with total QHAPDC hospitalised

person N and total QRCD hospitalised person R in each cell j=1,…, J,. For example,

if the QHAPDC dataset is classified by gender (male, female, 4 categories of age, and

3 categories of ARIA+, then J=2*3*4=24. Therefore, the implied weight is in the

following form:

𝐻 = ∑ 𝑅𝑗𝐽𝑗=1 𝑊𝑗 (5.2)

The hospital data was only available for 2009 and 2010. An initial attempt was made

to generate weights based on using police-reported data for the same time period, but

very small frequencies in same cells for the QRCD meant that the weights were large

and unreliable. Therefore, it was decided to apply the proportions of “age,” “gender,”

or “remoteness classification” cyclist in hospital data (2009-2010) to the

corresponding to police-reported data from 2005 to 2014, assuming that the pattern of

under-reporting and involvement in crashes was consistent over time.

5.2 RESULTS

5.2.1 Estimated weights

The age, gender and ARIA+ classifications for the cyclists coded as hospitalised

in police and hospital records are summarised in Tables 5.1 and 5.2. There are more

than twice as many hospitalised cyclists per year in the hospital data, underlining the

extent of under-reporting in the police data. Using the method described in the previous

section, proportional weights were calculated for each of the age, gender and ARIA+

combinations (see Table 5.3). The weights ranged from 0.260 for 17-24 year old

females who had been injured in the city to 2.916 for 60+ females who had been injured

in the city. Most weights were between 0.6 and 1.6.


Table 5.1 Demographic characteristics for QRCD 2005-2014

Age 0-16

years

Age 17-24

years

Age 25-59

years

Age 60+

years

Total

Male Female Male Female Male Female Male Female

City 293 39 216 52 847 204 112 17 1780

Inner

regional

115 22 40 09 139 33 41 10 409

Outer

regional

89 22 61 27 186 67 45 04 501

Total 497 83 317 88 1172 304 198 31 2690

Table 5.2 Demographic characteristics for QHAPDC 2009-2010

Age 0-16

years

Age 17-24

years

Age 25-59

years

Age 60+

years

Total


City 130 22 80 6 313 75 102 22 750

Inner

regional

67 23 36 7 79 15 28 7 262

Outer

regional

49 7 25 4 60 22 13 2 182

Total 246 52 141 17 452 112 143 31 1194

Table 5.3 Weights based on demographic characteristics

Age 0-16 years Age 17-24 years Age 25-59 years Age 60+ years


City 0.9995 1.2708 0.8344 0.2599 0.8325 0.8282 2.0517 2.9155

Inner

regional

1.3125 2.3553 2.0276 1.7520 1.2804 1.0240 1.5385 1.5770

Outer

regional

1.2403 0.7168 0.9233 0.3337 0.7267 0.7397 0.6508 1.1264

5.2.2 Crash patterns in weighted and unweighted datasets

The frequencies for all variables in the weighted and unweighted police data are

compared in Table 5.4. The proportions of children and riders aged over 60 increased

as a result of weighting. The percentage of riders who were judged to have been at-

fault was a little higher in the weighted data (45.3% versus 42.9%, χ2(1)=9.09, P=0.07)

and the percentage who were injured in inner regional areas increased from 15.2% to

21.9% (χ2(1)=8.19, P<0.004). Interestingly, the percentage who were injured in outer


regional areas decreased from 18.6% to 15.2% (χ2(1)=1.36,P<0.002). However,

weighting the data made little change to the gender distribution, or the prevalence of

helmet non-use or alcohol involvement. Similarly, most of the crash characteristics

were unaffected by the weighting process.

Table 5.4 Descriptive statistics for unweighted and weighted QRCD for 2005-2014

Variable name Unweighted Frequency

(%)

Weighted Frequency

(%)

Injury severity

Hospitalised 2,690 (100.0%) 2,690 (100.0%)

Gender of rider

Male 2,184 (81.2%) 2,212 (82.2%)

Female 506 (18.8%) 478 (17.8%)

Age of rider

0-16 580 (21.6%) 671 (25%)

17-24 405 (15.1%) 356 (13.2%)

25-59 1,476 (54.9%) 1,271 (47.2%)

60 and over 229 (8.5%) 392 (14.5%)

Helmet use

Worn 2,192 (81.5%) 2,176 (80.9%)

Not worn 309 (11.5%) 318 (11.8%)

Unknown 189 (7.0%) 195 (7.3%)

Alcohol involvement

Yes 135 (5.0%) 132 (4.9%)

No/unknown 2,555 (95.0%) 2,558 (95.1%)

Day

Weekday 2,110 (78.4%) 2,103 (78.2%)

Weekend 580 (21.6%) 587 (21.8%)

Time period

00.00 am-05.59 am 165 (6.1%) 152 (4.8%)

06.00 am-11.59 am 1,147 (42.6%) 1,178 (43.8%)

12.00 pm-05.59 pm 1,047 (38.9%) 1,047 (38.9%)

06.00 pm-11.59 pm 331 (12.3%) 312 (11.6%)

Road type

Cross-intersection 422 (15.7%) 417 (15.5%)

T junction 919 (34.2%) 895 (33.3%)

Roundabout 315 (11.7%) 322 (12.0%)

Other intersections 38 (1.4%) 35 (1.20%)

Midblock 895 (33.3%) 909 (33.8%)

Others 101 (3.7%) 112 (4.2%)

Road condition

Dry 2,464 (91.6%) 2,472 (91.8%)

Wet 226 (8.4%) 219 (8.2%)



Variable name Unweighted Frequency

(%)

Weighted Frequency

(%)

Straight 2329 (86.6%) 2335 (86.8%)

Curved obscured 72 (2.7%) 77 (2.9%)

Curved open 289 (10.7%) 278 (10.3%)

Vertical alignment

Crest 100 (3.7%) 94 (3.5%)

Dip 104 (3.9%) 105 (3.9%)

Grade 457 (17.0%) 456 (17.0%)

Level 2,029 (75.4%) 2,034 (75.6%)

Speed zone (km/h)

0-50 773 (28.7%) 790 (29.4%)

60 1,647 (61.2%) 1,626 (60.4%)

70 126 (4.7%) 126 (4.7%)

80-90 100 (3.7%) 100 (3.7%)

100-110 44 (1.6%) 48 (1.8%)

Crash type

Intersection from adjacent

approaches

868 (32.3%) 865 (32.2%)

Opposing vehicle turning 335 (12.5%) 317 (11.8%)

Lane changes 195 (7.2%) 197 (7.3%)

Parallel lane turning 164 (6.1%) 153 (5.7%)

Vehicle leaving driveway 608 (22.6%) 643 (23.9%)

Rear-end 124 (4.6%) 122 (4.5%)

Others 396 (14.7%) 393 (14.6%)

Light condition

Daylight 2,076 (77.2%) 2,112 (78.5%)

Darkness-lighted 303 (11.3%) 278 (10.3%)

Darkness- unlighted 71 (2.6%) 71 (2.6%)

Dusk 238 (8.8%) 227 (8.4%)

Unknown 2 (0.1%) 2 (0.1%)

At-fault status

At-fault 1,153 (42.9%) 1,218 (45.3%)

Not at-fault 1,537 (57.1%) 1,473 (54.7%)

Traffic control

Give-way sign 655 (24.3%) 649 (24.1%)

Stop sign 106 (3.9%) 107 (4.0%)

Operating traffic control 305 (11.3%) 288 (10.7%)

No traffic control 1,588 (59.0%) 1,611 (59.1%)

Others 36 (1.5%) 35 (2.1%)

Atmospheric condition

Clear 2,516 (93.5%) 2,524 (93.8%)

Raining 163 (6.1%) 155 (5.7%)

Others 11 (0.4%) 12 (0.4%)

Remoteness classification

Major city 1,780 (66.2%) 1,690 (62.8%)

Inner regional 409 (15.2%) 590 (21.9%)

Outer regional 501 (18.6%) 410 (15.2%)


5.3 DISCUSSION

Under-reporting of bicycle crashes in police data has been a long-standing

international concern, and is confirmed in this study by the finding that the number of

cyclists coded as “hospitalised” by police following bicycle-motor vehicle crashes per

year is less than half the number indicated by hospital admission records.

A growing body of literature in epidemiology has shown that not accounting for

under-reporting in different contexts leads to biased estimates (Dvorzak & Wagner,

2016; McDonald, Davie, & Langley, 2009; Yamamoto, et al., 2008). However, in the

current study, proportional weighting had little effect on the distributions of most of

the variables in the police dataset. The time periods, the types of locations and the

types of crashes seemed largely unaffected by weighting. This suggests that while the

extent of under-reporting is considerable, and it varies by age, gender and location

combinations, it is largely unbiased in relation to crash factors. Therefore, while the

weighting suggests that more emphasis needs to be placed on measures to improve the

safety of child cyclists and older (60+) cyclists, and on cycling in inner regional areas,

it is reassuring that it confirms that the police data can be used to identify the types of

locations and crashes that need to be examined.

The finding of higher percentages of hospitalised injuries among older cyclists

and child cyclists appear to be somewhat paradoxical. It may reflect the fact that older

cyclists and children may be less coordinated and frailer than the middle aged cyclists.

Therefore, this observation must be interpreted with caution because older and child

cyclists were more likely to sustain serious injuries, whereas middle aged cyclists were

less likely to be severely injured in the same type of crash event (Chen, Dunn, Chen,

& Linakis, 2013; Gaudet et al., 2015). Given that data on cycling exposure, including

distance travelled, number of trips, and number of cyclists, are not readily available in


Queensland, the extent of age differentials in injury risk could not be determined.

Therefore, the absence of exposure data limits the conclusion that can be drawn about

the age, perhaps to some extent skewing the findings of weighted data in that age

group.

Recommending the use of an unweighted dataset in this study setting might seem

odd, given that the proportion of under-reporting that exists in the data. The key point,

however, is that unweighted data are more robust than other data precisely because

they provide the potential information on these levels of precision. In the unlikely case

that the weights are known for different levels of variables, there is no doubt that the

weighted dataset is the preferred data.

In spite of significant differences between the results of the hospital admissions

data and police-reported data, the study results suggest that the unweighted data

provides reasonably accurate estimates in terms of crash characteristics. If the crash

patterns in two sources of data are almost similar, with some exceptions, the beta

coefficients signs in the regression analysis are expected to be consistent. As shown in

a study by Gebers (1998) based on count data, ordinary least squares (OLS) and

weighted least squares (WLS) coefficients were the same sign and quite similar in

magnitude.

5.4 CHAPTER SUMMARY

Recent literature has suggested that under-reporting in crash data, may

undermine the validity of results on bicycle crash outcomes. If the data linkage is not

feasible or possible, the weighting methods used in this study appear to be an

economical alternative and enable researchers to adjust for the effects of under-

reporting. Thus, the current research contributes to the bicycle safety analysis literature

from a methodological standpoint.


Weighted and unweighted estimates showed that time periods, the types of

locations, and the types of crashes are largely unaffected by under-reporting in this

study context. Child cyclists, older (60+) cyclists, and cycling in inner regional areas

are more important in the weighted data. Other crash factors are largely unaffected by

weighting.

These results confirm that the location type characteristics derived from police-

reported data are largely unaffected by under-reporting, and therefore, the problematic

attributes that affect crash incidence and severity level at intersections can be identified

and effective strategies formulated. An explicit study to identify the significant factors

that affect cyclist injury severity resulting from bicycle-motor vehicles crashes under

various traffic control measures at intersections is presented in the next chapter.


Chapter 6: Influence of type of traffic control on injury severity 75

Chapter 6: Influence of type of traffic

control on injury severity

The previous chapters in this thesis identified the data subsets and the potential

variables to be included in modelling injury severity. The difference in crash type

distribution between police-reported data and hospital data in Study 1 indicated that

the under-reporting is more common in SV crashes compared to MV crashes. This

study also found that under-reporting still exists in MV crashes, but Study 2 revealed

that location type characteristics derived from police-reported data is largely

unaffected by under-reporting. The findings of the previous two studies provide further

assurance that the MV crash-dataset is less likely to produce a biased estimate.

The analysis of the police-reported crash data in Study 1 further revealed that the

overall number of MV crashes and injuries is still disproportionately high at

intersections, given that intersections constitute a small segment of the entire roadway

system. Intersections vary in their complexity, and more complex intersections have

been demonstrated to be associated with increased severity of injury for cyclists

involved in crashes with motor vehicles (Kim, et al., 2007). There is some evidence in

the literature that the cyclist’s characteristics, geometric characteristics, and traffic

characteristics under various traffic control measures are different at intersections

(Prati, Marín Puchades, De Angelis, Fraboni, & Pietrantoni, 2017; Wang, et al., 2015).

If this is the case, perhaps cyclist injury severities are more likely to be influenced by

type of traffic control at intersections.

This chapter addresses Research Question 3, how do traffic control measures

influence cyclist injury severity at intersections? It examines the significant factors

that affect cyclist injury severity in BMV crashes under various traffic control

76 Chapter 6: Influence of type of traffic control on injury severity

measures at intersections in order to better inform measures to improve the safety of

cyclists. An understanding of the determinants of cyclist injury severity under various

traffic control measures can help to inform transport agencies to reduce the severity of

cyclist-involved motor vehicle crashes.

This study has been published in Transport Research Record: Journal of the

Transportation Research Board. The chapter is organised as follows. In Sections 6.1.1

and 6.1.2, the characteristics of the data sources and the methodology used in the

analysis are described. The model results and interpretation of parameter estimates are

presented in Sections 6.2 and 6.3 respectively.

6.1 METHOD

6.1.1 Data description

The analysis of the police-reported crash dataset in Study 1 contained both

intersection and non-intersection related MV crashes. The non-intersection related MV

crashes were excluded to keep the analysis focus on intersection related MV crashes.

Therefore, this study has prepared a dataset of police-reported multi-vehicle bicycle

crashes (n = 5,772) at intersections between January 1, 2002 and December 31, 2014

in Queensland. It contains data on crash-involved cyclists and drivers, crash

characteristics, roadway geometry, and environmental conditions associated with each

crash. Crashes occurring on public roadways leading to injury or property damage

greater than $2,500 or a vehicle being towed away are required to be reported to Police

in Queensland. Crashes occurring on private property (e.g., car parks) and road

reserves (e.g., off-road paths) are not included. BMV crashes are under-reported in

police data compared to hospital data (Watson, et al., 2013), but the police data is the

only source of information about the crash itself (e.g., crash location, type of traffic

control, road surface condition). BMV crashes are more likely to be reported than


single bicycle crashes due to their high impact force, higher likelihood of causing

injuries, and associated compensation issues (Kaplan, et al., 2014b; Vandenbulcke, et

al., 2009).

The unit judged by the police officer to be most at-fault is labelled as “Unit 1”.

Alcohol involvement by rider was coded as a contributing circumstance, but there was

no distinction between “alcohol not involved” and “not reported”.

Figure 6:1 Selection of data subsets

As shown in Figure 6.1, bicycle-bicycle crashes (n = 62) were excluded as the

severity of collisions between non-motorised vehicles was expected to be different

from that of collisions with motorised vehicles. Bicycle-pedestrian crashes were not

included in the dataset of bicycle motor-vehicle crashes. BMV crashes which occurred

in very remote or unknown locations (n=49) were also excluded due to high levels of

under-reporting (Watson, et al., 2015). BMV crashes involving more than two units (n

= 197) were excluded from the dataset to keep the analysis focused on two-unit

crashes. Where a bicycle pillion passenger was injured, their data was excluded.

Cyclist casualties were excluded (n=76) in cases where an injury had occurred other

Bicycle motor-vehicle

crashes (n=5,661)

Excluded:

Two units (n = 197)

Other traffic control (n=76)

Bicycle motor-vehicle

crashes (n=5,388)

Excluded:

Bicycle-bicycle crashes (n = 62)


crashes


than under the three different traffic control types. The resulting dataset contains 5,388

two-vehicle crashes involving a bicycle and a motorised vehicle (car, bus, truck,

motorcycle, utility or panel van) resulting in 1,086 minor injuries, 2,182 medical

treatment injuries, 2,081 hospitalised injuries, and 39 bicyclist fatalities.

6.1.2 Analysis approach

A mixed logit approach was utilised in this study to help identify the

contributing factors of BMV crashes by allowing the effects of the variables to vary

randomly across observations. Detailed discussion about this model with standard

formulation can be found in Section 3.2.1. Although the operating traffic signals

sample size is smaller than the other traffic control categories, it is a sufficient size to

produce a reliable result using the mixed logit model. The severity of the BMV crashes

was divided into three discrete categories (minor, medically treated, and hospital/fatal)

due to the low frequency of fatality.

Mixed logit models were estimated for intersections controlled by three

different control measures - Model 1: Stop/Give-way sign; Model 2: No traffic control;

and Model 3: Operating traffic signals. In all models, a backward elimination fitting

method was performed to drop the non-significant variables in an iterative process so

that the Akaike Information Criteria (AIC) was minimised. When the estimated

random parameters were non-significant, then the parameters become fixed

parameters if their standard deviations are not statistically significant (p < 0.05). As

demonstrated in previous research (Bhat, 2003; Gkritza & Mannering, 2008), a

simulation-based maximum likelihood with 200 Halton draws was used for accurate

parameter estimates. Regarding the random parameters’ density functional forms, the

normal distribution gave the best fit results among the normal, uniform, and lognormal

distributions for injury severity data, which is in line with past studies (Moore, et al.,


2011; Pai, et al., 2009). In this study, the statistical software tool, NLOGIT 6, was used

for model parameter estimation.

6.2 RESULTS

6.2.1 Descriptive analysis

A total of 58 explanatory variables were examined in the mixed logit model. The

details of these variables, including frequencies for each type of traffic control, are

presented in Table 6.1. The final merged dataset includes 5,388 crashes, with 2,459

(45.6%) at stop/give-way sign controlled intersections; 1,971 (36.5%) at intersections

with no traffic control, and 958 (17.7%) at signalised intersections. About 80% of the

riders were male and almost one-third were aged 25 to 39 years (29.5%). Just over half

of the crashes (52.5%) occurred at T-intersections. About two-thirds (65.4%) of the

crashes occurred in areas with a speed limit of 60 km/h, and almost all (94.4%)

occurred while the cyclist was riding on a straight road. The most common crash type

involved adjacent approaches (32.1%). Further examination of the ‘other’ crashes

revealed they were composed of less than 50 of each of the following crash types: hit

parked vehicle, head-on, off-carriageway on curve, off-carriageway on straight, rear-

end, U-turn, and other. Daytime (78.5%) crashes resulted in more severe injuries than

night-time crashes. A total of 64.7% of all crashes occurred in major cities, followed

by 19.1% in outer regional areas. Drivers were most at-fault in 63.6% of the crashes,

while cyclists were at-fault in 36.3% of the crashes. Few (1.6%) of the cyclists were

intoxicated, but this may be because of few cyclists were tested.


Table 6.1 Descriptive characteristics of BMV crashes under different traffic control

measures at intersections. Variable name Stop/Give-way

Sign- Frequency

(%)

No Traffic

Control-

Frequency (%)

Operating Traffic

Signals- Frequency

(%)

Injury severity

Fatal & Hospitalised 908 (36.93%) 815 (41.35%) 397 (41.44%)

Medical treatment 1,051 (42.74%) 767 (38.91%) 364 (38.00%)

Minor 500 (20.33%) 389 (19.74%) 197 (20.56%)

Gender of rider

Male 1,966 (79.95%) 1,606 (81.48%) 771 (80.48%)

Female 490 (19.93%) 364 (18.64%) 184 (19.21%)

Unknown 3 (0.12%) 1 (0.05%) 4 (0.42%)

Age of rider

0-15 274 (11.14%) 464 (23.54%) 192 (20.04%)

16-20 214 (8.70%) 200 (10.15%) 137 (14.30%)

21-24 195 (7.93%) 166 (8.42%) 98 (10.23%)

25-39 749 (30.46%) 523 (26.53%) 308 (32.15%)

40-49 475 (19.32%) 314 (15.93%) 106 (11.06%)

50-59 309 (12.57%) 168 (8.52%) 59 (6.16%)

60 and over 231 (9.39%) 118 (5.99%) 31 (3.24%)

Unknown 12 (0.49%) 18 (0.91%) 17 (1.77%)

Helmet use

Worn 2,060 (83.73%) 1,514 (76.81%) 718 (74.95%)

Not worn 158 (6.38%) 218 (11.06%) 113 (11.80%)

Unknown 241 (9.80%) 239 (12.13%) 127 (13.26%)

Alcohol involvement

Yes 28 (1.14%) 43 (2.18%) 19 (1.98%)

No/Unknown 2,431 (98.86%) 1,928 (97.82%) 939 (98.02%)

Day

Weekday 1,989 (80.89%) 1,615 (81.94%) 785 (81.94%)

Weekend 470 (19.11%) 356 (18.06%) 173 (18.06%)

Time period

00.00 am-5.59 am 147 (5.98%) 94 (4.77%) 40 (4.18%)

6.00 am-11.59 am 1,208 (49.13%) 794 (40.28%) 357 (37.27%)

12.00 pm- 5.59 pm 845 (34.36%) 838 (42.52%) 424 (44.15%)

6.00 pm- 11.59 pm 259 (10.53%) 245 (12.43%) 137 (14.30%)

Road type

Cross-intersection 463 (18.83%) 253 (12.84%) 604 (63.05%)

T junction 923 (37.54%) 1,578 (80.01%) 330 (34.45%)

Roundabout 1,041 (42.33%) 104 (5.23%) 0 (0.00%)

Other intersections 32 (1.30%) 36 (1.73%) 24 (2.40%)

Road condition

Dry 2,256 (91.74%) 1,836 (93.15%) 888 (92.69%)

Wet 202 (8.21%) 134 (6.80%) 69 (7.20%)

Unknown 1 (0.05%) 1 (0.05%) 1 (0.10%)


Variable name Stop/Give-way

Sign- Frequency

(%)

No Traffic

Control-

Frequency (%)

Operating Traffic

Signals- Frequency

(%)


Straight 2,311 (93.98%) 1,871 (94.93%) 925 (96.56%)

Curved obscured 16 (0.65%) 16 (0.81%) 3 (0.31%)

Curved open 132 (5.37%) 84 (4.26%) 30 (3.13%)

Vertical alignment

Crest 65 (2.64%) 58 (2.94%) 18 (1.88%)

Dip 67 (2.72%) 73 (3.70%) 16 (1.67%)

Grade 321 (13.05%) 383 (19.43%) 127 (13.26%)

Level 2,006 (81.58%) 1,457 (73.92%) 797 (83.19%)

Speed zone (km/h)

0-50 707 (28.75%) 658 (33.38%) 143 (14.93%)

60 1,604 (65.23%) 1,214 (61.59%) 708 (73.80%)

70 71 (2.89%) 54 (2.74%) 80 (8.35%)

80-90 69 (2.81%) 32 (1.62%) 22 (2.40%)

100-110 8 (0.33%) 13 (0.66%) 5 (0.52%)

Crash type


approaches

956 (38.88%) 542 (27.50%) 238 (24.84%)

Opposing vehicle turning 189 (7.69%) 228 (11.57%) 123 (12.84%)

Lane changes 167 (6.79%) 128 (6.49%) 70 (7.31%)

Parallel lane turning 170 (6.91%) 150 (7.61%) 85 (8.87%)

Vehicle leaving driveway 447 (18.18%) 411 (20.85%) 210 (21.92%)

Others 530 (21.55%) 512 (25.98%) 232 (24.22%)

Light condition

Daylight 1,916 (77.92%) 1,558 (79.08%) 759 (79.23%)

Darkness-lighted 252 (10.25%) 199 (10.10%) 134 (13.99%)

Darkness- unlighted 47 (1.91%) 51 (2.59%) 3 (0.31%)

Dusk 243 (9.88%) 160 (8.12%) 60 (6.26%)

Unknown 1 (0.04%) 3 (0.15%) 2 (0.21%)

At-fault status

Cyclist at-fault 671 (27.31%) 814 (41.32%) 471 (49.16%)

Cyclist not at-fault 1,788 (72.69%) 1,157 (58.68%) 487 (50.84%)


Major city 1,511 (61.45%) 1,342 (68.09%) 635 (66.28%)

Inner regional 403 (16.39%) 286 (14.51%) 133 (13.88%)

Outer regional 524 (21.31%) 327 (16.59%) 183 (19.10%)

Remote 21 (0.85%) 16 (0.81%) 7 (0.73%)


6.2.2 Model estimation

Table 6.2 shows the estimation results of the stop/give-way sign, no traffic

control, and operating traffic signals mixed logit models, including parameter

coefficients and P-values, i.e., levels of significance of a parameter different from zero.

Table 6.2 Mixed Logit Injury Severity Models for BMV Crashes under Various

Traffic Control Measures at Intersections

Variable name Stop/Give Way

Sign Coefficients

(P-value)

No Traffic

Control

Coefficients (P-

value)

Operating Traffic

Signals

Coefficients (P-

value)

Dependent variable a

Constant

[Fatal/Hospitalised]

-0.255 (0.360) -0.073 (0.368) -0.049 (0.868)

Constant [Minor] -0.367 (0.542) -0.559 (0.000) 0.002 (0.987)

Age of rider

16-20 [M] - 0.362 (0.018) -

40-49 [F/H] 0.334 (0.007) 0.384 (0.003) 0.764 (0.002)

50-59 [F/H] - 0.489 (0.003) -

60 and over [F/H] 0.765 (<0.001) - 1.195 (<0.001)

60 and over [MI] - -0.966 (0.006) -

Helmet use

Worn [MI] -1.287(<0.001) - -0.932 (<0.001)

Worn [M] -0.414 (0.033) - -

Not worn [F/H] 0.989 (<0.001) 0.685 (0.000) 0.844 (<0.001)

Alcohol involvement

Yes [F/H] 0.961 (0.038) - -

Road condition

Wet [F/H] 0.483 (0.019) - -

Vertical alignment

Crest [F/H] - 0.587 (0.031) -

Dip (F/H) - - 1.328 (0.043)

Speed zone (km/h)

0-50 [M] 0.575 (0.009) - -

60 [F/H] -0.564 (0.005) - -

Crash type


approaches [F/H]

0.354 (0.009) - -

Vehicle leaving driveway

[F/H]

- - 0.355 (0.032)

Others [M] - 0.246 (0.036) -

Light condition

Daylight [F/H] -0.409 (<0.001) - -


Variable name Stop/Give Way

Sign Coefficients

(P-value)

No Traffic

Control

Coefficients (P-

value)

Operating Traffic

Signals

Coefficients (P-

value)

At-fault status

At-fault [F/H] 0. 582 (<0.001) - 1.042 (<0.001)

Model statistics

Log-likelihood -2488.11 -2039.50 -951.29

Restricted log-likelihood -2690.50 -2163.16 -1050.27

McFadden, ρ2 0.07 0.05 0.09

Number of observations 2459 1971 958

-, not found significant; a Letters in parentheses indicate variable coefficients are significant

specific to: [MI] Minor injury, [M] Medical treatment injury, and [F/H]

Fatality/Hospitalised injury. For the constants, the medical treatment outcome has, without

loss of generality, its coefficient normalised to zero.

The three categories of injury severity, minor injury, medical treatment injury,

and fatality/hospitalised injury, are labelled as MI, M, and F/H, respectively. The

severity category, medical treatment, is used as the estimation base, by assuming the

constant specific to medical treatment is equal to 0. The log-likelihood values of the

stop/give-way sign, no traffic control, and operating traffic signals mixed logit models

are -2,488.11, -2,039.50, and -951.29, respectively. The corresponding McFadden

pseudo- ρ2 values are 0.07, 0.05, and 0.09; indicating a reasonable level of statistical

fit for the injury severity models. All of the estimated parameters included in these

three models are statistically significant at a 0.05 significance level or better.

Once the mixed logit models were developed, likelihood ratio tests (Washington,

et al., 2010) were performed to justify the necessity to estimate different traffic control

measures in this study. The χ2 statistic (χ2=237.84), with degrees of freedom equal to

the summation of the number of estimated parameters in all traffic control measures

models minus the number of estimated parameters in the overall model, provides the

confidence level at which we can reject the null hypothesis. The null hypothesis states

that full model parameters estimates are no better than the separate model estimate.

The Chi square statistics for the likelihood ratio test with 58 degrees of freedom


resulted in a value greater than the 99.99% confidence limit (χ2=97.03), suggesting

that the estimation of separate models is statistically warranted.

Table 6.3 Comparisons of variable elasticities between different traffic control

measures at intersections

Explanatory variables Stop/Give Way

Sign

No Traffic

Control

Operating Traffic

Signals

Age of rider

16-20 [M] - 1.95% -

40-49 [F/H] 3.55% 3.26% 3.85%

50-59 [F/H] - 2.11% -

60 and over [F/H] 3.39% - 1.50%

60 and over [MI] - -5.30% -

Helmet use

Worn [MI] -86.39% - -54.71%

Worn [M] -17.65% -

Not worn [F/H] 2.89% 3.62% 4.32%

Alcohol involvement

Yes [F/H] 0.36% - -

Road condition

Wet [F/H] 1.61% - -

Vertical alignment

Crest [F/H] - 0.78% -

Dip [F/H] - - 0.67%

Speed zone (km/h)

0-50 [M] 8.31% - -

60 [F/H] -22.2% - -

Crash type

Intersection from

adjacent approaches

[F/H]

8.58% - -

Vehicle leaving

driveway [F/H]

- - 4.32%

Others [M] - 2.07% -

Light condition

Daylight [F/H] -18.4% - -

At-fault status

At-fault [F/H] 7.89% - 24.15%

At-fault [MI] 3.15% - -

-, not found significant; Variables are defined for outcomes: [MI] Minor injury, [M] Medical

treatment injury, [F/H] Fatality/Hospitalised injury

Direct pseudo-elasticities are computed to estimate the impact of a particular

explanatory variable on the injury severity outcome. For example, a pseudo-elasticity

of 15% means that when the value of the variable in the subset observations are


changed from 0 to 1 (and 1 to 0), the probability of the severity outcome for these

observations in the sub-set is increased by 15% on average.

6.3 DISCUSSION

The significant variables for each severity outcome are discussed in the following

sections, along with the elasticities of each explanatory variable with respect to the

injury severity outcomes presented in Table 6.3.


There were a number of significant results related to age group of the rider, but

the elasticities were generally less than 5%, indicating relatively small effects. Young

adult riders (aged 16-20) were associated with an increased probability of medical

treatment injury (1.95%) at no traffic control intersections, but not at stop/give-way

and signalised intersections. This result is consistent with earlier findings in Taiwan

(Doong & Lai, 2012). Riders aged 40-49 were associated with an increased probability

of fatal/hospitalised injury at stop/give-way sign, un-controlled, and signalised

intersections by 3.55%, 3.26%, and 3.85%, respectively. The marginal effects further

show that cyclists aged 50-59 years had a 2.11% increased probability of

fatal/hospitalised injury at no traffic control intersections. These results indicate that

riders aged 40-59 are especially vulnerable. This may reflect the greater propensity for

riders of this age group in Queensland to ride racing bikes (Schramm, Haworth,

Heesch, Watson, & Debnath, 2016), with faster riding speeds resulting in less time to

respond by both rider and driver, as well as higher impact speeds, both of which

increase the injury severity. Older cyclists (aged 60 and over ) were 3.39% more likely

to experience fatal/hospitalised injury severity at stop/give-way signs; and 1.50% more

likely to experience fatal/hospitalised injury severity at signalised intersections. This

http://www.sciencedirect.com/science/article/pii/S0001457514001870#tbl0015


could be because some older cyclists have delayed perceptions, slower reaction time,

and physical frailty, which increase the risk and injury severity (Maring & Van

Schagen, 1990). Older cyclists (aged 60 and over) were 5.30% less likely to experience

minor injury at uncontrolled intersections. The result from the descriptive analysis

identified that drivers were more often at-fault (70%) in such locations in crashes

involving a vehicle that was leaving a driveway.

Helmet use is mandatory in Queensland and observed wearing rates are in

excess of 98% (Debnath, Haworth, Schramm, & Williamson, 2016). The results show

that not wearing a helmet increased the likelihood of fatal/hospitalised injury by

2.89%, 3.62%, and 4.32% at stop/give-way sign, uncontrolled, and signalised

intersections, respectively. Conversely, wearing a helmet reduced minor injury by

86.39% and 54.71% at stop/give-way sign and signalised intersections, respectively.

It is well established that helmets protect against head, brain, neck, and facial injuries

for cyclists of all ages (Attewell, Glase, & McFadden, 2001) and these results are

consistent with those of numerous past studies (Boufous, Rome, et al., 2012; Haworth,

Schramm, King, & Steinhardt, 2010; Moore, et al., 2011).

Based on police officers’ judgement of at-fault status, the most-at-fault party

involved in a crash was considered as the at-fault party, whereas the other parties were

considered as ‘not-at-fault’. Table 6.1 shows that cyclists are less likely to be at-fault

in BMV crashes at stop/give-way intersections than other intersections. When cyclists

were at-fault, the probability of fatal/hospitalised injury increased by 7.89% and

24.15% at stop/give-way and signalised intersections, respectively. In addition, when

cyclists were at-fault in BMV crashes at stop/give-way intersections the probability of

minor injury increased by 3.15%. Schramm, et al. (2010) noted that when the cyclist

was found to be at-fault in BMV crashes, the most frequent contributing factors were


inattention, inexperience, and disobeying traffic signals. This suggests it is important

to decrease illegal behaviour among riders and drivers, as well as improving levels of

attention and experience.

As shown in Table 6.3, cycling under the influence of alcohol was found to

increase the probability of fatal/hospitalised injury by 0.36% at stop/give-way

intersections. When cyclists have been drinking, they may be more likely to ride

without lights at night or behave unpredictably which can increase the probability of

injury (Crocker, et al., 2010). The small elasticity effect found for the presence of

alcohol may have resulted from few people being tested (Ferris et al., 2013; Juhra, et

al., 2012), because breath testing for alcohol use of injured persons is not mandatory

in Queensland. The police-reported crash data not distinguishing between alcohol

absent and alcohol status unknown. It is recommended that roadside alcohol breath

tests (i.e., random breath tests) should be conducted frequently and reported for all

cyclists, which can provide greater insights into the prevalence and consequences of

these behaviours.

6.3.2 Roadway characteristics

Roadway conditions also had an impact on cyclist injury outcomes. The

elasticity estimates suggest that at stop/give-way intersections, wet surfaces increased

the risk of fatal and hospitalised injury (1.61%). Given that two-thirds of the BMV

crashes occurred where the speed limit was 60 km/h, the combination of wet roads and

relatively high speeds may be particularly perilous. On roads with a relatively high

posted speed limit (compared with the default 50 km/h urban speed limit), it would be

difficult for the driver and cyclist to stop or slow down shortly before entering or

approaching intersections due to the high probability of skidding. This is also

consistent with previous studies (Kaplan, Vavatsoulas, & Prato, 2014a; Klop &


Khattak, 1999). Typically, skidding is more likely to happen on a wet surface because

of the decreased friction between tyre and road surface (Mayora & Piña, 2009).

The models developed in this study show that crest alignment increases (0.78%)

the probability of fatal and hospitalised injury at un-controlled intersections, possibly

due to insufficient sight distance to avoid potential conflicts between vehicles turning

or crossing. Dips also increased (0.67%) the probability of fatal and hospitalised injury

at signalised intersections. Most BMV crashes occurred in city areas which are often

hilly (Haworth & Debnath, 2013), and so drivers and cyclists cannot be sure whether

or not there is an oncoming vehicle hidden beyond the rise.

6.3.3 Environmental characteristics

The results of the models show that daylight was associated with reduced

likelihood of fatal injury and severe injury at stop/give-way intersections. Generally

the flow of traffic is higher during daylight (Austroads, 2017b), so travel speeds are

lower and also visibility is better.

6.3.4 Crash characteristics

BMV crashes at stop/give-way signs in 0-50 km/h speed limit zones were

associated with an 8.31% increased probability of medically treated injury. Further

analysis showed that most of the BMV crashes (58.22%) in 50 km/h speed limit zones

or lower occurred at T-junctions. This finding reinforces the need to provide an

unobstructed view to drivers and cyclists approaching a give-way condition or leaving

from a stopped position at a T-junction to avoid potential BMV crashes

(Vandenbulcke, Thomas, & Panis, 2014). The road with posted speed limit of 60 km/h

was decreased the probability of fatal/hospitalization injuries by 22.1% at stop/give-

way intersections. Additional analysis identified about half of the crashes in 60 km/h

zones (53%) occurred at roundabouts, a particular type of stop/give-way intersection.


Roundabouts provide drivers with more time to react and stop in a shorter distance

(Räsänen & Summala, 1998), which is likely to reduce the occurrence and injury

severity. However, it is also argued that roundabouts with a cycle path (separated) were

safer than roundabouts without a cycle facility (integrated) (Daniels, et al., 2008). It

has been recommended that speed limits in areas of high cycling activity should be set

at 30 km/h, because in lower-speed streets motor vehicles would stop more quickly,

and the injury severity would be lower (Steriu, 2012).

The modelling results also revealed that vehicles leaving driveways at signalised

intersections increased the probability of hospitalised/fatal injury by 4.32%. Cyclists

were more likely to be at-fault (65.5%) in crashes that involved leaving driveways and

may have failed to notice motorists on the road. As noted by Haworth and Debnath

(2013), who found that child cyclist are often involved in vehicle leaving driveway

types of crashes. Restricting turns or improving visibility at certain signalised

intersections may be useful to avoid potential conflict points between motorists and

cyclists. In this study, the most common crash type was collision at an intersection

between vehicles from adjacent approaches. This variable is found to increase the

probability of hospitalised/fatal injury by 8.58% at intersections controlled by

stop/give-way signs. In this cyclist-motorist crash pattern, approximately 63% of

cyclists with hospitalised/fatal injury had exposure to speeds between 50 km/h and 60

km/h. These results are consistent with past research (Haworth & Debnath, 2013),

which shows that cyclists have difficulties in judging gap sizes and speed when

deciding whether to initiate a roadway entry or a turning manoeuvre.

6.4 CHAPTER SUMMARY

More than half of the BMV crashes analysed occurred at signalised intersections

or intersections without traffic control, but few factors were identified as significant


contributors to cyclist injury severity at these locations. Despite similar distributions

of injury severity across the three types of traffic control, more factors were identified

as influencing cyclist injury severity at stop/give-way controlled intersections than at

signalised intersections or intersections with no traffic control. Increased injury

severity for riders aged 40-49 and 60+ and those not wearing helmets were the only

consistent findings across all traffic control types, although the effect of not wearing

helmets was smaller at uncontrolled intersections. Cyclists who were judged to be at

fault were more severely injured at stop/give-way and signalised intersections. Speed

zone influenced injury severity only at stop/give-way signs and appears to reflect

differences in intersection design, rather than speed limits per se. Injuries tended to be

more serious on wet surfaces, and therefore providing skid resistant pavements on

approach at an intersection controlled by stop/give-way signs would likely be an

effective countermeasure. Given that cyclist alcohol use also significantly influenced

injury severity, enforcement and education about the potentially devastating effect of

alcohol use could be effective. Regarding geometric variables, dip and crest were

found to increase the probability of cyclist injury severity.

This chapter answered Research Question 3 by exploring differences in the

models of injury severity for BMV crashes under various traffic controls at

intersections. The findings suggest that not all intersections are equal in terms of cyclist

safety and that type of traffic control is an important factor influencing cyclist safety.

The crash patterns of cyclists at intersections did not provide enough information to

understand the crash mechanisms. Therefore, next the chapter examines the crash

pattern of BMV crashes at intersections more comprehensively and holistically in

order to provide a more detailed picture.

Chapter 7: The influence of motor vehicle trajectory on injury severity 91

Chapter 7: The influence of motor vehicle

trajectory on injury severity

The evidence about BMV crash mechanisms provided in the previous chapter

indicated that further research is necessary to understand which and whether particular

crash types are more injurious to cyclists at intersections. While many studies have

evaluated the association between cyclist injury severity and crash type, few have

posited any clear mechanism underlying the relationship. Therefore, the current study

addresses Research Question 4 by investigating the factors that are significantly

associated with cyclist injury severity in collisions with motor vehicle severity for

trajectory types and crash types at intersections. This study classified the trajectories

based on the hypothesis that a collision between a motor vehicle moving straight ahead

along a roadway and a cyclist will have a higher likelihood of severe injury occurring

to the cyclist, compared with collisions between turning motor vehicles and cyclists,

as the turning motor vehicles are likely to be travelling at a lower speed. This study

addresses Research Question 4: Can motor vehicle trajectory better explain injury

severity in crashes with bicycles than crash type?

Through the use of Definitions for Classifying Accidents (DCA) codes (for

details, see appendix), the combined data was split into five crash groups, namely

turning opposite direction, straight opposite direction, turning same direction, straight

same direction, and straight right angle for cases where the trajectory cannot be easily

defined as either same or opposite direction. Here after, these are referred to as

“trajectory types”.

The rest of this chapter is organised as follows: the data preparation and analysis

approach are discussed in Sections 7.1.1 and 7.1.2, respectively. Section 7.2 describes

92 Chapter 7: The influence of motor vehicle trajectory on injury severity

the statistical measures, analysis results, and elasticity effects. Finally, the discussion

section examines the major findings of the study.

7.1 METHOD

7.1.1 Data description

This study examined police-reported crashes involving a bicycle from 2002-

2014 in Queensland. The details are provided in Section 6.1. However, in Australia in

2016, 68% of cyclist road fatalities resulted from crashes between bicycles and Light

Passenger Vehicles (LPVs) (DITRLDG, 2016). There is an ongoing debate regarding

whether the type of injury depends on the size and mass of the motor vehicle

(Desapriya, et al., 2004). Consequently, the potential countermeasures developed for

LPV crashes might be different from those developed for crashes involving larger

motor vehicles, and so this study focused on bicycle-LPV crashes to reduce the

external effect of difference in motor vehicle size and mass.

Intersections are the most dangerous areas of the road network due to several

conflicting movements, resulting in traffic complexity and large variations in

interactions between cyclists and motor vehicles. A total of 3,135 bicycle-LPV crashes

occurred at intersections over the study period. Cyclist–pedestrian crashes, cyclist–

cyclist crashes, and single bicycle crashes were excluded from the analysis. Because

the aforementioned bicycle crash types and injury severity are often underestimated

in police-reported crash databases (Watson, et al., 2015). However, police-reported

crash data is still preferred for crash analysis due to the richness of the data in terms of

the crash location information, whereas hospital data is limited to their personal

information of the patients. Bicycle–LPV crashes that occurred in remote areas (n=22)

were excluded due to substantial level of under-reporting to police (Watson, et al.,

2015).


Another noteworthy limitation of the police-reported dataset concerns whether

the police officer reported that either driver or rider was affected by alcohol or not.

The coding does not distinguish between “not alcohol involved” and “not reported”.

Traditional crash type analyses are categorised based on initial point of impact

of the target and striking vehicles in a motor-vehicle crashes. There are four different

types of crashes at intersections: angle, head-on, rear-end, and sideswipe. Based on the

hypothesis, this study categorised the trajectory type based on the reported movements

and direction of travel of the LPV, as illustrated in Figure 7.1. This study attempts to

develop the following trajectory type categories: (a) a straight same direction collision

is defined as a collision which takes place when a LPV proceeding in a straight ahead

collides with a cyclist who is travelling in the same direction; (b) a straight opposite

direction collision is defined as a collision which occurs when a cyclist and a LPV

approach each other in a straight ahead but travelling from opposite directions; (c) a

turning same direction collision is defined as a collision which occurs when a turning

LPV collides with a cyclist travelling in the same direction; (d) a turning opposite

direction collision is defined as a collision where a turning motorist strikes a cyclist

travelling in the opposite direction; (e) a straight right angle collision is defined as a

collision which the colliding motor vehicle driver and cyclist were travelling straight

ahead from two intersecting roadways. A few of the crashes in the DCA could not be

categorised according to trajectory types because it was too difficult to determine the

reported movements and direction of travel of the LPV.


* the red arrow is the direction of travel of the light passenger vehicle

Figure 7:1 Trajectory types of Bicycle-LPV crashes


7.1.2 Analysis approach

This study also employed mixed logit model to overcome the limitations of

traditional multinomial models. Mixed logit model (MXL) formulations, assumptions,

and applications are discussed in Section 3.1.2. Separate mixed logit models were

estimated for crash types and trajectory types using the LIMDEP software package

(Greene, 2007). Based upon past research (Bhat, 2003), two mixed logit models were

computed using 200 Halton draws to establish the lowest log-likelihood value at

convergence, and compared according to their Akaike Information Criteria (AIC). The

abbreviation of the corresponding severity category to which each parameter belongs

is listed in brackets: [MI] minor injury, [M] medically treated injury, [H/F] hospitalised

injury/fatal. Since the number of fatal injuries (n=37) is low, the two highest severity

levels were combined into one category to avoid computational issues in the mixed

logit modelling.

The crash type model considered the variables of crash type (e.g., angle) along

with variables of roadway, rider, and driver characteristics. Similarly, the trajectory

type model incorporated the variables of trajectory type (e.g., straight same direction)

along with variables of roadway, rider, and driver characteristics. Tables 7.2 and 7.3

present the model estimates of the crash type model and the trajectory type model,

respectively.

7.2 RESULTS

7.2.1 Descriptive analysis

Table 7.1 gives an overview of the sample characteristics, which are similar to the

characteristics described in the previous chapter, except for the trajectory types.

Straight same direction and turning opposite direction were the most frequently


occurring conflict types, accounting for 30.6% and 25.1% of all bicycle-LPV crashes,

respectively. Angle crashes (88.5%) were the most frequent bicycle-LPV crashes.

Table 7.1 Descriptive statistics for the bicycle-LPV crashes at intersections

Explanatory variables Dependent variables Total

Fatal &

Hospitalised

(N=1188)

Medical

treatment

(N=1321)

Minor

injury

(N=626)

Gender of rider

Male 967 1,038 531 2,536

Female 221 283 95 599

Age of rider

0-15 126 145 81 352

16-29 325 358 182 865

30-59 628 717 316 1,661

60 and over 108 99 27 234

Unknown 1 2 20 23

Helmet

Worn 1,007 1,137 430 2,574

Not worn 109 70 36 215

Unknown 72 114 160 346

Alcohol involvement

Yes 45 19 6 70

No/unknown 1,143 1,302 620 3,065

Gender of driver

Male 611 615 285 1,511

Female 533 643 293 1,469

Unknown 44 63 48 155

Age of driver

16-20 124 129 66 319

21-29 209 228 123 560

30-59 585 638 279 1,502

60 and over 207 216 93 516

Unknown 63 110 65 238

Day

Weekday 932 1,069 509 2,510

Weekend 256 252 117 625

Time period

00.00 am-05.59 am 75 70 32 177

06.00 am-11.59 am 505 603 270 1,378

12.00 pm-05.59 pm 422 491 255 1,168

06.00 pm-11.59 pm 186 157 69 412

Intersection type

Cross-intersection 326 308 157 791

T junction 604 645 294 1,543



Fatal &

Hospitalised

(N=1188)

Medical

treatment

(N=1321)

Minor

injury

(N=626)

Roundabout 240 349 168 757

Other intersections 18 19 7 44

Road surface condition

Dry 1,087 1,216 585 2,888

Wet 100 104 41 245

Unknown 1 1 0 2


Straight 1,037 1,124 555 2,716

Curved 151 197 71 419

Vertical alignment

Crest 38 33 11 82

Dip 51 44 15 110

Grade 205 205 79 489

Level 894 1,039 521 2,454

Speed zone (km/h)

0-50 342 386 170 898

60 765 878 415 2,058

70 50 32 23 105

80-110 31 25 18 74

Crash type

Angle 1,068 1,176 533 2,777

Rear-end 32 37 24 93

Head-on 8 5 3 16

Sideswipe 80 103 66 249

LPV trajectory

Straight opposite direction 236 257 110 603

Turning opposite direction 340 312 133 785

Straight right angle 262 274 113 649

Straight same direction 310 419 232 961

Turning same direction 40 59 38 137

Light condition

Daylight 868 1,038 497 2,403

Darkness 203 161 71 435

Dusk 114 120 58 292

Unknown 3 2 0 5

At-fault status

At-fault 341 281 161 783

Not at-fault 847 1,040 465 2,352


Major city 799 923 422 2,144

Inner regional 191 168 92 451

Outer regional 212 251 124 584

Remote 8 10 5 23



Fatal &

Hospitalised

(N=1188)

Medical

treatment

(N=1321)

Minor

injury

(N=626)

Traffic control characteristics

No traffic control 442 431 227 1,110

Give-way sign 488 623 268 1,379

Operating traffic signals 180 171 90 441

Stop sign 78 91 37 219

Others 0 5 4 9

Atmospheric condition

Clear 1,106 1,239 597 2,942

Raining 74 78 27 179

Others 8 4 2 14

Chi-square tests were used to find the independent variables influencing the

dependent variables (injury severity variables). Factors such as day of week, road

surface condition, remoteness classification and atmospheric conditions were not

found to be significant at p > 0.2 using chi-square tests and therefore these variables

were excluded from the mixed logit models.

7.2.2 Model estimation

The log-likelihood values at convergence are as follows: crash type-MXL (with

23 parameters) is -3110.46 and trajectory type-MXL (with 30 parameters) is -3127.52.

The mixed logit model with trajectory type performed better with a smaller AIC (AIC

= 6,280.9 vs 6,298.5). The lower AIC values suggest a slightly better fit of the data by

the mixed logit model with trajectory type factors. In addition to using AIC, a

likelihood ratio test was performed (see more details in Washington, et al., 2010) to

select the preferred model. The resulting LR test for the comparison of crash type

model/trajectory type model is 34.12 (7 df). The mixed logit model with trajectory type

factors outperforms the mixed logit model with crash type factors at the 0.001

significance level indicating that the mixed logit model with trajectory type factors has


a better fit with the data. Therefore, the mixed logit model with trajectory type factors

is preferred to the model with crash type factors.

Table 7.2 Mixed logit severity model results for crash type at intersections

Explanatory variables Crash type Coefficients

(P-value)

Elasticity/ pseudo-

elasticity (%)


Constant [Fatal/Hospitalised] -0.844 (0.007)

Constant [Minor injury] -0.085 (0.732)

Crash type

Sideswipe [MI] 0.449 (0.016) 2.32

Intersections type

Roundabout [M] 0.425 (0.014) 2.92

Roundabout [MI] 0.393 (<0.001) 5.98

Speed zone (km/h)

50 [M] 0.632 (0.011) 6.32

Light condition

Darkness [F/H] 0.500 (<0.001) 2.56

Vertical alignment

Level [M] -0.499 (0.007) -14.97

Std. dev. of parameter distribution 2.654 (<0.001)

Level [F/H] -0.402 (<0.001) -1.12

Gender of rider

Female [F/H] 0.294 (0.041) 2.75

Female [M] 0.599 (0.002) 3.40

Age of rider

16-29 [F/H] 0.452 (0.041) 5.87

30-59 [F/H] 0.650 (0.002) 16.72

60 and over [F/H] 1.395 (<0.001) 4.18

Helmet

Worn [MI] -1.656 (<0.001) -103.61

Not worn [F/H] 1.936 (<0.001) 4.24

Not worn [M] 1.139 (<0.001) 3.21

At-fault status

At-fault [F/H] 0.411 (0.012) 4.58

Std. dev. of parameter distribution 1.574 (0.015)

Gender of driver

Male [F/H] 0.221 (0.041) 4.83

Model statistics

Log-likelihood -3127.526

Restricted log-likelihood -3403.500

McFadden, ρ2 0.09

Number of observations 3113 a Letters in parentheses indicate variable coefficients are significant specific to: [MI] Minor

injury, [M] Medical treatment and [F/H] fatality & hospitalisation. For the constants, the

medical treatment outcome has, without loss of generality, its coefficient normalised to zero.


Table 7.3 Mixed logit severity model results for trajectory type at intersections

Explanatory variables Trajectory type

Coefficients (P-value)

Elasticity/ pseudo-

elasticity (%)


Constant [Fatal/Hospitalised] -0.043(0.481)

Constant [Minor injury] 1.260 (<0.001)

Trajectory type

Turning opposite direction [F/H] 0.503 (<0.001) 5.10

Straight right angle [F/H] 0.497 (0.004) 4.24

Straight same direction [MI] 0.289 (0.049) 5.80

Intersections type

Roundabout [M] 0.489 (0.008) 3.11

Roundabout [MI] 0.456 (<0.001) 7.40

Speed zone (km/h)

50 [M] 0.658 (0.028) 6.07

60 [M] 0.607 (0.038) 4.97

Light condition

Darkness [F/H] 0.421 (0.003) 2.29

Vertical alignment

Level [M] -0.532 (0.012) -12.76

Std. dev. of parameter distribution 3.208 (<0.001)

Level [F/H] -0.366 (0.016) -1.79

Gender of rider

Female [F/H] 0.359 (0.021) 3.09

Female [M] 0.648 (<0.001) 3.39

Age of rider

16-29 [F/H] 0.480 (0.032) 6.28

30-59 [F/H] 0.799 (0.004) 20.49

60 and over [F/H] 1.586 (<0.001) 4.43

60 and over [MI] 0.721 (0.028) 1.60

Helmet

Worn [MI] -1.733 (<0.001) -105.29

Not worn [F/H] 1.995 (<0.001) 4.03

Not worn [M] 1.114 (<0.001) 3.01

At-fault status

At-fault [F/H] 0.399 (0.023) 4.14

Std. dev. of parameter distribution 1.988 (0.008)

Gender of driver

Male [F/H] 0.213 (0.029) 4.52

Driver age

30-59 [MI] -0.223 (0.044) 7.68

Model statistics

Log-likelihood -3110.461

Restricted log-likelihood -3403.500

McFadden, ρ2 0.09

Number of observations 3113


a Letters in parentheses indicate variable coefficients are significant specific to: [MI] Minor injury, [M]

Medical treatment, and [F/H] Fatality/Hospitalisation. For the constants, the medical treatment

outcome has, without loss of generality, its coefficient normalised to zero.

Explanatory variables which meet the conventional 0.05 level of statistical

significance are retained in the final mixed logit models. The ρ2 of both models is equal

to 0.08, which indicates that the models fit the data satisfactorily. The marginal effects

for some key severity determinants are shown in Tables 7.2 and Table 7.3 for cyclist

injury severity. It should be noted that average direct pseudo-elasticity for each

variable is coded as a binary indicator. For example, a pseudo-elasticity of 25% means

that when the value of the variable in the subset observations are changed from 0 to 1

(and 1 to 0), the probability of the severity outcome for these observations in the sub-

set is increased by 25% on average. A large effect is defined here as an average direct

pseudo-elasticity a value greater than 1, which means that the percentage of change is

greater than 100% (doubling of the probability of the certain level of injury severity).

See Washington, et al. (2010) and Kim, Ulfarsson, Shankar, and Kim (2008) for further

discussion on elasticities. The interpretation of the significant variables are discussed

in the next sections.

7.3 DISCUSSION

Two general similarities were observed between crash type and trajectory type

MXL models. Firstly, the direction effect and magnitude of the probability changes

were similar for the majority of the explanatory variables, except for two indicator

variables, which were driver age and speed limit 60 km/h (not significant in crash type

MXL model). Secondly, two independent variables were found to be random, namely,

when crashes occurred on level road and cyclist being at-fault. A random parameter

means that a portion of the observations may have decreased the probability of cyclist

injury severity and the remaining portion of the observations have increased


probability. The significant explanatory variables for each severity outcome are

discussed explicitly in the following sections.

7.3.1 Crash characteristics

A straight same direction crash occurs when a vehicle proceeding straight

collides with a cyclist who is travelling in the same direction as the motor vehicle. The

findings in the Trajectory type model showed that straight same direction crashes

increase the likelihood of minor injury by 5.8%, which is aligned to some extent with

the proposed hypothesis that a collision between a LPV moving straight ahead along

a roadway and a cyclist will have a higher likelihood of severe injury occurring to the

cyclist.

A turning opposite direction crash occurs when a turning motorist strikes a

cyclist travelling in the opposite direction. In contrast to the research hypothesis,

turning opposite direction crashes increased the likelihood of fatal/hospitalisation

injury at intersections by 5.1% in the trajectory type model. This might reflect that

motorists focus on finding a gap in opposing automobile traffic rather than detecting

cyclists (Koustanaï, Boloix, Van Elslande, & Bastien, 2008). Turning restrictions and

additional information for motorists to expect bicycles on the roadway might increase

bicycle safety in this situation.

Straight right angle crashes are typically those angle crashes in which the

colliding motor vehicle driver and cyclist were travelling straight ahead from two

intersecting roadways. The results confirm the hypothesis that straight right angle

crashes increase (4.2%) the likelihood of fatal/hospitalisation injury in trajectory type

model. The analysis further identified that straight right angle crashes (61.1%)

occurred at intersections controlled by give-way signs. This may result from a situation


where a motor vehicle driver or cyclist does not have sufficient visibility or

misjudgement of the speed of the incoming vehicles (Habibovic & Davidsson, 2012).

If adequate sight distance cannot be maintained, it may be necessary to consider

appropriate advance warning signs or stop signs.

For the crash type model, the result shows that most of the crash type variables

were not statistically significant. Only sideswipe crashes were associated with an

increased probability (2.3%) of minor injury. Sideswipe crashes typically occur when

a motorist or cyclist is weaving or changing lanes. About 88% of all reported bicycle-

LPV crashes were angle crashes, but it remained insignificant across all injury

severities in the crash type model.

Intuitively, this study demonstrates that trajectory type can provide additional

information regarding the BMV crash mechanism at intersections. It is noteworthy

here that the research hypothesis does not completely align with the trajectory type

result. However, this outcome was not surprising from an engineering perspective,

since two unobserved factors may play an important role in determining cyclist injury

severity. This findings could be explained that many LPV drivers do not follow the

posted speed limit, and the geometry of the intersections and surrounding areas has

more influence on speed reduction than speed limit postings. Almost two-thirds of

turning opposite direction collisions occurred at T-intersections. When a LPV turns

out from the side road, if the side road angle of approach to an intersection exceeds

120°, it can result in the LPV driver losing stereo vision. In short, it reduces the sight

distance of the LPV driver, which increases the impact speed and also increases the

probability of cyclist injury severity. Isaksson-Hellman and Werneke (2017) also

pointed out that, based on insurance claim data, injury severity does not only depend

on the impact direction but also depends on the impact speed and impact point. Before


a conclusion can be drawn from this finding, further investigation is necessary. With

data about the real operating speed at the time of crash and the road geometry of the

location, a more realistic relationship could be established.

7.3.2 Roadway characteristics

Generally, roundabouts in Queensland are designed with tangential entries to

control traffic flow. The results from both the crash type and trajectory type models

suggest that the likelihood of a minor injury (by 5.9% and 7.4%, respectively) and

medically treated injury (by 2.9% and 3.1%, respectively) are higher in a bicycle-LPV

collision at a roundabout than at any other intersection type. This result is not

surprising, as previous studies found that roundabouts have more tendency to increase

the number of BMV crashes with serious injuries (Daniels, et al., 2008; Kaplan &

Giacomo Prato, 2015).

Roads with speed limits of 50 km/h or lower were found to increase the

likelihood of crashes resulting in medical treatment (by 2.9% and 3.1% in the crash

type and trajectory type models, respectively). Similarly, a higher speed limit (60 km/h

rather than 50) increased the probability of medically treated injury by 4.9% in the

trajectory type model, but was not significant in the crash type model.

The variable of level road was found to be random and normally distributed in

both the crash type and trajectory type models. This random variable accounts for the

unobserved heterogeneity, which indicates that the effect of the variable is varied

across observations. In the trajectory type model, the parameter of the level road

indicator for possible medically treated injury is normally distributed with a mean of

−0.53 and standard deviation of 3.21. With this variable, 56.7% of the distribution is

less than 0 and 43.2% of the distribution is greater than 0. This implies that half of the

bicycle-LPV crashes which occurred on a level road resulted in a decrease in medically


treated injury while the rest of the crashes resulted in an increase in medically treated

injury. The effect of a level road on cyclist injury severity outcomes was mostly

consistent across the two models.

Darkness was the only environmental characteristic found to be statistically

significant. The probability of fatal/hospitalisation injury increased by 2.2% and 2.5%

at night in the trajectory type and crash type models, respectively. These findings were

also confirmed by a previous study (Zahabi, et al., 2011) and indicate that bicycle

crashes that occurred after sunset result in a greater likelihood of severe injury.


Female riders were found to experience higher injury severity than males in both

the crash type and trajectory type models. The magnitudes of the parameter among

females was greater in the trajectory model. Adult, middle, and older aged cyclists

experienced more severe injury in both the crash type and trajectory type models.

Results from the trajectory type model showed a 1.6% increase in the probability of

minor injury if older cyclists are involved in the crash. Similar results were found in

the crash type model, however this effect is not statistically significant at the 95%

level.

Due to the mandatory helmet law in Queensland, helmet use is very high among

riders (Debnath, et al., 2016). Helmet use was found to reduce the probability of minor

injury by 103.6% and 105.2% in the crash type and trajectory type models,

respectively; and helmet non-users exhibited an increased probability of fatal/hospital

injury outcomes by 4.2% and 4.1%, respectively. Similar findings have been

confirmed in previous studies (Behnood & Mannering, 2017; Moore, et al., 2011).


From the results presented in Tables 7.2 and 7.3, it appears that at-fault cyclists

are more likely to be associated with fatal/hospitalised injuries in both models. In the

trajectory type model, the cyclist being at-fault is found to be a normally distributed

random parameter specific to hospital/fatal injury, with a mean of 0.399 and a standard

deviation of 1.988. With these values, the normal distribution curve implies that 57.9%

of the observations increase the probability of fatal/hospitalised injury, while 42.1%

of the observations decrease the likelihood of fatal/hospitalisation injury. A similar

distribution applied to the crash type model.

7.3.4 Driver characteristics

Results from both models revealed that male drivers were more likely than

female drivers to cause fatal or hospitalised cyclist injury. Drivers aged 30-59 were

associated with a 7.68% increase in the likelihood of minor cyclist injury in the

trajectory type model, but this was not statistically significant in the crash type model.

7.4 CHAPTER SUMMARY

This chapter has described the Study 4, which provided new insights regarding

how cyclists can easily be seriously injured in crashes depending on the vehicle

movements (e.g., straight ahead, making turn) and direction of travel at intersections.

A mixed logit model was used to examine bicycle-LPV crashes from Queensland

police crash report data from 2002 to 2014. The information in the Definitions for

Coding Accidents (DCA) was used to distinguish five different trajectory types:

straight same direction, straight opposite direction, turning same direction, turning

opposite direction, and straight right angle. A likelihood ratio test revealed that injuries

are better explained by classifying crashes using their trajectory types than the

traditional way of classifying crashes by crash types.


The results may help design intersections to increase clarity and safety for

cyclists; for example, bicycle-LPV crashes were significantly more likely to result in

fatal/hospitalised injury when a turning motorist struck a cyclist travelling in the

opposite direction. Bicycle crashes were significantly more likely to result in minor

injury when they involved motorists travelling straight in the same direction as the

cyclist. Right-angle crashes are prone to be severe at intersections when a straight

travelling vehicle strikes a cyclist.

By addressing Research Question 4, this study investigated whether the light

passenger vehicle trajectory better explains injury severity in crashes with bicycles

than crash type. This study, therefore, helps to address the knowledge gaps in analysis

and concludes that trajectory type is a more promising classification than crash type

when explaining patterns of cyclist injury severity in cyclist-LPV crashes at

intersections.

The next chapter concludes this dissertation by summarising the key findings of

the four studies followed by a discussion of the implications of this dissertation. The

strengths and limitations are also discussed along with possible future research

directions on this topic.


Chapter 8: Discussion and Conclusions 109

Chapter 8: Discussion and Conclusions

This chapter begins with a rationale of on-road bicycle safety research, along

with a brief discussion of the research questions examined. This is followed by a

discussion that offers potential safety countermeasures which are obtained from the

results. Finally, the relevance of the obtained results highlights the contribution to the

current state of knowledge, and then concludes by documenting research limitations

and providing directions for further research to improve on-road bicycle safety.

8.1 RESEARCH BACKGROUND

Despite low levels of cycling, cyclists comprised about 2.5% of all road fatalities

both in the United States (NCSA, 2017) and Australia (BITRE, 2016) in 2015. Bicycle

crashes with motor vehicles are the major concern because they often result in severe

injuries to cyclists, and the fear of such collisions can prevent many people from taking

up cycling. Many studies have demonstrated that bicycle crashes are under-reported in

police data, yet police data remain the best source of information about crash locations

and circumstances. The objective of this dissertation is to develop statistical models

using existing best possible police-reported data to obtain a better understanding of the

factors contributing to severe injuries in bicycle-motor vehicle crashes. To achieve it,

the current dissertation addresses the gap in three directions: adjustment for under-

reporting where linkage with hospital data are not possible or are uneconomic;

influence of traffic control measures; and the role of pre-crash trajectory.

As discussed in Section 1.3, this dissertation seeks to answer the following four

research questions:

RQ1: For what types of bicycle crashes is police-reported crash data most adequate?

110 Chapter 8: Discussion and Conclusions

RQ2: How does the pattern of police-reported bicycle crashes change when adjusted

for under-reporting?

RQ3: How do traffic control measures influence cyclist injury severity in crashes with

motor vehicles at intersections?

RQ4: Can motor-vehicle trajectory better explain injury severity in crashes with

bicycles than crash type?

To answer the RQs, bicycle crash data for thirteen years (2002-14) was obtained

from the Queensland Department of Transport and Main Roads, along with aggregate

data from the Queensland Hospital Admitted Patient Data Collection (QHAPDC) for

2009-10. Mixed logit modelling techniques were applied to analyse the data. The

subsequent section outlines the major findings in relation to the four research

questions.

8.2 REVIEW OF MAJOR FINDINGS

8.2.1 RQ1: For what types of bicycle crashes is police-reported crash data most

adequate?

Despite the number and severity of bicycle crashes in most countries,

quantification of the effects of possible countermeasures has been surprisingly limited

due to data quality issues (e.g., under-reporting). Under-reporting can lead to

erroneous inferences in both identifying intervention priorities and evaluating their

effectiveness. It is difficult to obtain unbiased results when under-reporting exists in

police data. Therefore, this study compared aggregate hospital admission records with

police data (recorded as ‘hospitalised’) to understand the extend of under-reporting of

bicycle crash injuries to police in the Australian state of Queensland. Study 1

demonstrated that the data deficiency in bicycle crash information is two-fold: firstly,

only about 10% of cyclists admitted to hospital as a result of single vehicle crashes

featured in police-reported hospitalisation data. The percentage of under-reporting in


police data found in Study 1 is similar in other jurisdictions (Alsop & Langley, 2001;

Ward, et al., 2006). Secondly, there were about 1200 cyclists admitted to hospital after

multi-vehicle collisions, but less than half of this number were reported to police.

These results indicate that multi-vehicle bicycle crashes are more likely to be reported

than single vehicle bicycle crashes due to the high impact force, higher likelihood of

causing injuries, and associated compensation issues (Vandenbulcke, et al., 2009;

Winters & Branion-Calles, 2017). Despite these factors half of MV bicycle crashes are

still missing from the police-reported data.

The comparison of police-reported data and hospital admission data further

confirmed the findings of previous research that the extent of under-reporting was

greater for lower severities and in remote and very remote locations (Watson, et al.,

2015). Although hospital admission data provide a more complete source of

information on bicycle injuries, they provide very limited information on crash

characteristics (e.g., crash location, crash type), which can only be found in police-

reported crash data. Therefore, subsequent analysis focused on multi-vehicle bicycle

crashes in major cities and inner/outer regional areas using police-reported crash data.

8.2.2 RQ2: How does the pattern of police-reported bicycle crashes change

when adjusted for under-reporting?

One of the motivations for this analysis was to assess whether weighting would

affect the distribution of crashes in terms of their infrastructure characteristics. Using

analyses of poor quality data to decide on bicycle safety programmes may result in

misdirected allocation of resources. For example, if bicycle-motor vehicle crashes are

less likely to be reported to police in rural areas, then reliance on police data may result

in fewer resources being allocated to rural areas. While the linkage of individual police

and hospital records can maximise the use of available information, this is not always


feasible for reasons of data accessibility or cost. Therefore, in this study, aggregated

hospital data were used to weight police data for bicycle-motor vehicle crashes in order

to correct for biases resulting from differential under-reporting.

The calculated weights revealed that crashes involving child cyclists and older

(60+) cyclists and cycling in inner regional areas were under-represented in the police

data. However, the infrastructure characteristic did not change when the data was

weighted, suggesting that there was negligible endogeneity in these characteristics. If

the crash data sampling probabilities vary exogenously (i.e., independent from other

variables in the system) instead of endogenously, then both weighted and unweighted

estimations are consistent for the explanatory variable, but in such cases the

unweighted estimates may be more precise. For example, in a regression model with

weighted data that overrepresents a certain type of intersection factors, but includes an

intersections type dummy variable among the explanatory variables, if the regression

model is correctly specified, the error term is not related to the sampling condition,

and weighting is unnecessary in this instance.

8.2.3 RQ3: How do traffic control measures influence cyclist injury severity in

crashes with motor-vehicles at intersections?

Many studies have looked at different factors that contribute to bicycle-motor

vehicle crashes (e.g., Chong, et al., 2010; Yan, et al., 2011), however little is known

about the determinants of cyclist injury severity under different traffic control

measures at intersections. The most common types of traffic control measures at

intersections, are stop signs, give-way signs, and traffic signals, while some

intersections have no traffic control. In the third study, aggregate and disaggregate

mixed logit models were developed to identify the significant factors that affect cyclist

injury severity. The tests showed that the explanatory effects were not consistent for

the types of traffic controls, so disaggregate models were used. Therefore, mixed logit


models of cyclist injury severity with possible outcomes of fatal, hospitalised,

medically treated, and minor injury were estimated to independently assess the impact

of each type of traffic control at the intersection.

Six variables were found to be significant for the model for intersections

controlled by stop and give-way signs, but not for the other traffic controlled

intersection models. Similarly, five variables were found to be significant only for the

model for intersections without traffic control and two variables for the model for

intersections with operating traffic signals. For example, wet surfaces increased the

cyclist injury severity at intersections controlled by stop/give-way signs, although this

variable was not statistically significant at un-controlled intersections and signalised

intersections. This finding is consistent with Wang, et al. (2015), whose study focused

on un-signalised intersections. Crests and dips were associated with higher levels of

cyclist injury severity at un-controlled intersections and signalised intersections,

respectively. These results are similar to past bicycle safety research at intersections

(Moore, et al., 2011). The results regarding rider characteristics highlights that older

cyclists (60+) involved in bicycle motor-vehicle crashes at intersections controlled by

traffic signals and stop/give-way signs are more likely to be severely injured, which is

in line with results in past research (Boufous, de Rome, et al., 2012; Kim, et al., 2007).

Older cyclists were often involved in crashes involving vehicles leaving a driveway

but sustain only minor injury at uncontrolled intersections. The combination of being

more experienced and the relatively low speeds may clarify the less serious injuries at

uncontrolled intersections. Consistent with expectation, helmet usage was found to

reduce the degree of cyclist injury severity at each type of traffic control intersection.

With regard to roadway characteristics, roads with speed limits exceeding 50 km/h had


higher injury severity levels. This is consistent with the findings of many previous

studies (Eluru, et al., 2008; Robartes & Chen, 2017).

8.2.4 RQ4: Can motor vehicle trajectory better explain injury severity in

crashes with bicycles than crash type?

Evidence in the literature suggests that the severity of cyclist injury depends on

the vehicle manoeuvre and travelling direction (Hauer, et al., 1988; Isaksson-Hellman

& Werneke, 2017). According to scientific context, when the motor-vehicle is moving

in a straight ahead on the roadway, it is likely to have higher speed. If the cyclists are

struck at a high impact speed, they tend to sustain higher injury severity because the

kinetic energy during a crash increases more due to velocity than mass (Badea-Romero

& Lenard, 2013). However, no previous study has investigated the effects of various

confounding variables on cyclist injury severity as a function of the impact of vehicle

manoeuvres and travelling direction. There is a need to understand the existing safety

problem at intersections to provide a new insight into potential prevention strategies

that might help moderate cyclist injuries.

The purpose of this study was to comprehensively explore the contributing

factors significantly associated with cyclist injury severity for trajectory types

compared with crash types at intersections. Mixed logit models were used to examine

bicycle-Light Passenger Vehicle (LPV) crashes in Queensland police crash report data

from 2002 to 2014. Definition for Classifying Accidents (DCA) codes were used to

distinguish five different trajectory types: straight same direction, straight opposite

direction, turning same direction, turning opposite direction, and straight right angle.

A likelihood ratio test revealed that injuries are better explained by classifying crashes

using their trajectory types than the traditional approach of classifying crashes by crash

types.


Three trajectory types were identified as affecting cyclist injury severity at

intersections: straight right angle, turning opposite direction, and straight same

direction. The modelling results showed an increased higher level of injury severity

when both the cyclist and the LPV are travelling straight ahead. Bicycle-LPV crashes

were significantly more likely to result in hospitalisation/fatality when a turning

motorist struck a cyclist travelling in the opposite direction.

The results indicated that there were differences in the results obtained from the

crash type and trajectory models. For example, crash type and trajectory type models

suggested that cyclists were more likely to sustain minor (by 5.98% and 7.40%,

respectively) and medically treated injuries (by 2.92% and 3.11%, respectively) in

bicycle-involved LPV crashes at a roundabout than any other intersection type. The

findings suggest that crash type variables appear to have a relatively small effect on

other explanatory variables in the probability change when varying from 0 to 1. The

results of the analysis provide evidence that trajectory type is a more promising

classification to explain crash severity than the traditional crash types in the context of

cyclist injury severity at intersections.

8.3 IMPLICATIONS

The findings of this research have important implications for better

understanding cyclists’ injury severities and for improving the safety of cyclists. The

implications are discussed in this section in terms of road and roadside engineering,

education and engagement, enforcement, and evaluation.

8.3.1 Road and roadside engineering

Eliminating fatalities and serious injuries requires a data-driven approach to

better understand the role of supporting road infrastructure features in the Safe System

Approach (SSA). More than half (61%) of the severe bicycle motor-vehicle crash


injuries occurred at intersections. These findings are similar to previous studies from

other countries (e.g., Kaplan, et al., 2014b), probably due to failure of the driver of the

other vehicle to see the cyclist approaching (Herslund & Jørgensen, 2003). This

proportion demonstrates why intersection safety and understanding the mechanism of

bicycle motor-vehicle crashes are priorities to improve safety performance of

intersections towards the safe system vision.

It is also recommended to check for potential sight distance problems at

intersections, particularly at intersections controlled by stop/give-way signs, where the

number of bicycle motor-vehicle crash injuries are abnormally high. Despite similar

distributions of injury severity across the 3 types of traffic controls, more factors were

identified as influencing cyclist injury severity at stop/give-way controlled

intersections than at signalised intersections or intersections with no traffic control.

Traffic volume, visibility, speeds, and crash history are the most common factors

considered when selecting appropriateness of stop/give-way signs for a given

intersection. Although it appears simple on the surface, the high number of BMV

crashes indicate that the decision to install these signs requires more careful

consideration in order to improve the safety of cyclist at stop/give-way controlled

intersections. For example, the presence of on-street parking and vegetation may also

reduce the sight distance of cyclists and drivers entering an intersection. Restricting

the parking space near intersections and properly trimming vegetation are also

necessary to improve the sight distance at intersections (Austroads, 2017a).

The results of the study indicate that uncontrolled intersections are associated

with a higher risk of severe cyclist injuries than signalised intersections. Almost 80%

of BMV crashes took place at T-intersections with no traffic control. T-intersection

design tends to have stronger effect on cyclist injury severity. This is also consistent


with previous studies (Haleem & Abdel-Aty, 2010), who examined safety unsignalised

intersections, and found that intersection angle was influences injury severity. When a

minor or side road angle of approach to an intersection is not close to 90°, it can restrict

the driver line of sight. It is also argued that the ability of drivers to detect any

conflicting vehicles diminishes at such skewed intersections, which lead to these

intersections being less safe for cyclists (Gattis & Low, 1998). A recent study by

Asgarzadeh, et al. (2017) found that bicycle motor-vehicle crashes at non-orthogonal

intersections (i.e., more or less than 90°) are more likely to result in severe injury.

Therefore, it might be essential to check the intersection angles when developing

effective interventions to protect cyclists on the road. All approaches that should be

considered to promote safe interactions between cyclists and motor-vehicle at

uncontrolled intersections, some of which are inexpensive and short term, while others

are expensive and long term. For example, retroreflective pavement markings with

highlighting of the areas where cyclists and motorists cross each other’s path are

inexpensive and short term (Van Houten & Seiderman, 2005). A more expensive and

long term solution would be separated bicycle crossings near intersections (Madsen &

Lahrmann, 2017).

Crests and dips are also associated with an increase in the probability of fatal or

severe injuries. Typically, cyclists are slower in climbing a crest than other vehicles

and can lose control when doing so (or when riding downhill, when speed is higher as

well). Adequate warning signs may be a practical solution. Regardless, warning signs

on roads must be simple enough to be easily understandable before the driver/cyclist

receives the next message.

It is recognised that vehicle speed is a critical component of the Safe System

Approach. If the impact speed of the vehicle is above 50 km/h, the cyclist’s chance of


surviving reduces dramatically (Siddiqui, Abdel-Aty, & Choi, 2012). Similar results

were found in the current research, although posted speed limit was used, rather than

impact speed. This implies that higher vehicle speeds prior to impact increased the

cyclist injury severity at intersections (Kim, et al., 2007). The cities of Queensland and

their surrounding areas are hilly, and downhill locations may contribute to higher

cyclist and vehicle speeds. This implies a need to reduce speed limits in targeted areas

with a combination of signage, pavement markings, and vertical deflection devices in

order to enhance safety for cyclists. It has been also recommended that cyclists should

be allowed to mix with motorised vehicles up to 30 km/h; above this, separate bicycle

lanes should be provided to avoid conflicts (Johansson, 2009).

Straight right angle crashes were found to increase the probability of severe

injury at intersections. Half of the straight right angle crashes took place at cross-

intersections. This is to be expected because motor vehicles travelling straight are

likely to be travelling at a higher speed than those turning. In Australia, cross-

intersection designs typically allow drivers to pass through at the posted speed limit,

resulting in impact speeds several times greater than the biomechanical tolerance of a

human body (Tingvall & Haworth, 2000). The findings suggest that the current design

of cross-intersections is inconsistent with the principles of Safe System Approach. In

light of SSA, current infrastructure design should incorporate features that can restrict

travel speed to more acceptable levels of speed (Candappa, Logan, Van Nes, &

Corben, 2015). Raised safety platforms (refers to a flat top speed hump) are a common

road infrastructure treatment at cross-intersections that could be a potential speed

reducing countermeasure, which may lower the overall speed of vehicles to the Safe

System suggested collision speed (Austroads, 2016). Unfortunately, however, such

traffic claiming measures are only suitable for roads with low traffic volume.


8.3.2 Education and engagement

Child (age<16) and adolescent (age 16-20) cyclists were more likely to be

severely injured at locations with no traffic control. More than 80% of child and

adolescent cyclists were at-fault in BMV crashes, which is consistent with findings in

Canada (Rowe, Rowe, & Bota, 1995) and the USA (Wang, et al., 2015). In

Queensland, an observational study has demonstrated that children were less

compliant than adults with helmet laws (Debnath, et al., 2016). Although traffic

educations programmes show a minor positive effect, community-wide interventions

could be an alternative option to improve the understanding of traffic rules

(Lachapelle, Noland, & Von Hagen, 2013). Therefore, the study results endorse

continuous collaboration with community partners in order to increase awareness

among parents and children about the importance of, for example, the bicycle helmet

law, wearing light coloured clothing, and using a rear reflector while riding. There is

sound evidence that these safety measures lower the likelihood of serious cyclist

injuries (Chen & Shen, 2016; Tin, Woodward, & Ameratunga, 2014). Improving road

safety requires the cooperation of the wider community and road users of all ages.

8.3.3 Enforcement

Enforcement of road rules is as important as educations program (Wegman, et

al., 2012). In Study 3, the police identified the driver as the at-fault party in 72% of

reported BMV crashes at intersections controlled by stop/give-way signs. An older

study (Schramm, Rakotonirainy, & Haworth, 2008) in Queensland found that failure

to give-way and disobeying a give-way sign were the most commonly reported

contributing factors where the driver was considered at-fault. Few cyclists are BAC

tested in Queensland, but cyclists under the influence of alcohol were associated with


an increased risk of injury. This result shows that targeted enforcement for both

motorists and cyclists at potential locations is essential to reduce traffic violations. It

has also been shown that highly visible active enforcement increases the risk of

detection, which results in safer road user behaviour (Gilchrist, Schieber, Leadbetter,

& Davidson, 2000).

8.3.4 Evaluation

Comprehensive data collection is essential for designing effective safety

strategies and determining intervention priorities. Despite the systematic process for

handling crash reporting within police departments, the results of this research suggest

that only about 10% of cyclists admitted to hospitals as a result of crashes featured in

police-reported hospitalisations data. This finding is consistent with previous studies

(Aptel, et al., 1999; Sciortino, Vassar, Radetsky, & Knudson, 2005). Another study

provided evidence that strengths and threats for handling traffic crash reporting within

police have different importance depending on characteristics such as age and crash

location (Janstrup, Kaplan, Barfod, & Prato, 2017). This may require investment in

police agencies, as police officers should spend more time developing skills for crash

reporting, along with education about fairness, and a follow up group to improve

accuracy of the crash reporting.

Another significant improvement would be the introduction of an integrated

system to facilitate seamless crash reporting. This could be done with technology that

is currently available, such as the Italian ReGIS software (Montella, Chiaradonna,

Criscuolo, & De Martino, 2017). An evaluation of the available data carried out before

and after the introduction of the software showed that the amount of collected

information increased from 82 variables to 268 variables. This web-based portal also

guides the police officer step-by-step to detect technical road information (e.g., cross


sections, horizontal alignment). Another suggestion with the potential to improve the

crash data available was suggested by Watson (2014) who proposed that databases of

QRCD could be linked with the QHAPDC and Emergency Department Information

Systems (EDIS) to better quantify the serious injury outcomes. Such improvements, if

implemented in Queensland, could significantly improve the quality of the data

available, and allow for much more comprehensive analysis.

8.4 STRENGTHS AND LIMITATIONS

Before concluding the discussion, understanding the strengths and limitations of

the research is necessary to reach true conclusions. The following section starts with

the strengths, followed by the limitations of this research, along with possible future

research directions on this topic.

As discussed in previous research (Ye & Lord, 2014), the performance of injury

severity models can be heavily influenced by small sample size, which can limit the

ability to estimate the true value of parameters. Therefore, a strength of this research

is that it uses a large number of BMV crash data from Queensland to explore the

relationship between cyclist injury severity and its contributing factors.

Police crash data provides only limited detail about the explanatory variables

associated with injury severity, such as the characteristics of crashes, roadway

geometrics, and riders. Therefore, it can introduce additional heterogeneity across the

observed crashes. For example, features of roadway design (e.g., presence of shared

lanes or shoulder, turning radius), road rules (e.g., warning signs, advisory signs), and

rider behaviour related information (e.g., visual search skills, fluorescent clothing)

might influence the rider characteristics, but this information is not recorded. Taking

into account such problems, the use of mixed logit models is a real strength of this

research, as it accounts for unobserved heterogeneity that allows explanation of more


of the variance in the results, while using the same number of observations and quality

of information (see for example Milton, et al. (2008) and Wu et al. (2014)).

Another strength of the cyclist injury severity analysis is that it enables a

comprehensive analysis of the different aspects of injury severity and their relationship

with the 68 variables examined in the cyclist injury severity models, particularly at

intersections.

The two sources of data sets, the police-reported crash data and aggregate

hospital admission data are used to determine some of the strengths and weaknesses

of the individual datasets, which is another strength of this study. Therefore, the

findings of this research confirm the validity of police-reported data for providing a

complete picture of cyclist injuries in Queensland.

This research had several limitations. Despite having a systematic process of

crash data collection and processing, the police-reported crash data suffer from issues

related to under-reporting and misclassification of the injury severity, which may lead

to inaccurate model estimates. Crash data typically depends on the subjective

judgments of the police officer who attempts to collect the crash scene information,

which is shown to be less than perfectly reliable (Farmer, 2003). The proportion of

under-reporting is significantly high when a motor vehicle is not involved in a crash

with a bicycle (Sciortino, et al., 2005). In addition, the analysis of police data shows

that, compared with other locations, single vehicle crashes were less (24%) likely to

occur at intersections, which is also found in previous study (Boufous, et al., 2013).

Considering all these issues, this study does not include single bicycle crashes, cyclist-

cyclist crashes, and cyclist-pedestrian crashes due to under-reporting issues but it

would be interesting to examine how these crashes occur at intersections.


Another limitation of the third and fourth studies is that only two-unit crashes

are included: bicycle-motor vehicle and bicycle-LPV, respectively, in the analysis.

This is because the number of crashes involving more than two units is low. However,

it is recommended that future study should consider more than two units to examine

the risk of cyclist injury severity more extensively.

Study 4 provides a starting point for a more focused investigation of MV

trajectory and their influences upon on cyclist injury severity. Separate model for the

LPV categories were not estimated because of the low number of observations for

turning same direction. It would be interesting to see in future study whether the

outcomes of each trajectory type model show major differences in both the

combination of variables included in each model and the magnitude of impact of those

variables.

In Study 3, driver characteristics were not considered, which can potentially have

an impact on the analysis of cyclist injury severity (Robartes & Chen, 2017). A future

study with more information regarding driver characteristics however would be a

worthwhile research pursuit.

Although the longitudes and latitudes of origin obtained from the crash dataset

and incident location were checked by the author still it is difficult to determine the

direction from which a motorist travelling in a straight line or turning motorist strikes

a cyclist from the DCA codes. Therefore, the trajectory type categorisation in Study 4

may misclassify based on incorrect original DCA coding.

In Chapter 5, the weightings are generated from hospital databases only, which

are person-based for the period of 2009-2010. The accuracy of these weightings would

be more precise if they were generated from longitudinal hospital datasets. Another

issue that needs to be addressed is that the weightings generated in this geographical


area may not be appropriate elsewhere. In addition, although the adjusted police-

reported data enriched the data record, caution is still required for issues such as

identification of black spots and other dangerous locations.

An additional key area of bicycle safety research is the use of alternative sources

such as Injury Surveillance Systems, electronic Ambulance recording form, and

Emergency Department Data Systems. Combining these data sources with police-

reported data may build comprehensive datasets that provide a more accurate picture

of cyclist injuries (Watson, 2014).

Road safety research has been broadly divided into micro and macro level. The

first category is a micro-level safety analysis which focuses on specific roadway

entities such as roadway segments, intersections, and midblock. Further research

should also investigate bicycle crashes that occurs at midblock sections. On the other

hand, the second category is macro-level, which concentrates on spatial aggregation.

Injury severity studies with micro and macro levels are still rare and further research

is needed to understand and quantify the influence of contributing factors.

8.5 CONCLUDING REMARKS

In low-cycling countries such as the United States and Australia, collisions with

motor vehicles are the major cause of severe injuries to cyclists and fear of collisions

prevents many people from taking up cycling. These are cause for concern, given

current efforts to increase cycling participation, and the increasing number of severe

injuries among cyclists. Numerous studies and meta-studies have suggested that these

crashes are under-reported in police data, yet police crash data remain the only

available source of information about crash locations and circumstances on a large

scale. Therefore, this research aimed to develop statistical models that best utilise


police-reported data to obtain a better understanding of the factors contributing to

severe injuries in BMV crashes.

Data from the state of Queensland was obtained from the Department of

Transport and Main Roads, along with hospital admissions data from Queensland

Health, as described in Chapter 3. Mixed logit models were formulated and used in

this research to identify the factors that influence cyclist injury severity. The mixed

logit model predicts the probability of four injury severity outcomes: fatal,

hospitalised, medically treated, and minor injury. The results of this research sheds

new light on the significant factors contributing to cyclist injury severity.

The contribution of this research are twofold. In terms of knowledge, this

research presents a new way to regularly adjust police data for the effects of under-

reporting (in terms of both numbers of crashes and their patterns) in jurisdictions where

aggregate hospital data is available but data linkage is not allowed or is impractical.

Reassuringly, this research has shown that the infrastructure factors in the police data

that influence severity are robust to the effects of under-reporting. That is, even though

the police-reported crash data under-reports the numbers of crashes unevenly across

severity, collision type and other variables, under-reporting does not seem to provide

a biased account of the infrastructure factors involved in bicycle crashes with motor

vehicles. Therefore, in terms of knowledge contribution, the research provides the

confidence to use the under-reported bicycle crash data to identify the important

infrastructure factors.

For road safety practitioners, the findings of this research provide practical

guidance for the design of interventions in roadway design, and engineering solutions

that will potentially help in decreasing related injuries and fatalities resulting from

bicycle crashes with motor vehicles. For example, it shows that trajectory is an


important determinant of injury severity in BMV crashes at intersections, and points

to the need for a Safe System Approach to take into account the likely trajectory and

speed combinations at intersections. It also shows that the decision to install stop/give-

way signs at intersections require more careful consideration to create a safer non-

motorised traffic environment.

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Appendix 145

Appendix

Definition for Coding Accidents (DCA) group in Queensland

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