Toshio Yoshii Ehime Univ.

Leeds University, 10 July 2014

Accident Risk Simulation

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1992 graduate from Department of Civil Eng., The Univ. of Tokyo 1994 Master degree 1999 Ph.D supervised by Prof. Kuwahara

1994-1999. Research associate of The Univ. of Tokyo 1999-2003. Associate professor of Kochi Univ. of Technology 2003-2010. Associate professor of Kyoto Univ. with Prof. Kitamura 2010- Professor of Ehime Univ.

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CV

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Researches

1. Traffic control - Dynamic network traffic simulation SOUND(1995): Simulation On Urban expressway Networks with Dynamic route choice Meso-scopic simulation based on Block Density Method (Cell Transmission Model) About 20 years before, I visited LEEDS for getting a information about CONTRAM,SATURN and DRACULA. In order to develop a simulation model, we have to solve various issues…

1. Traffic control - Dynamic network traffic simulation - Demand estimation

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Researches

T. Yoshii，M. Kuwahara：Estimation of a Time Dependent OD Matrix from Traffic Counts Using Dynamic Traffic Simulation，Proceedings of the 8th WCTR Vol.2，pp.163-174，1998.7.

1. Traffic control - Dynamic network traffic simulation - Demand estimation - Dynamic information provision

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Researches

T. Yoshii & M. Kuwahara：An Evaluation method on Effects of Dynamic Traffic Information, The 7th Annual World Congress on Intelligent Transport Systems 00’ Torino, CD-ROM, 2000.11.

1. Traffic control - Dynamic network traffic simulation - Demand estimation - Dynamic information provision - Traffic flow/ Network theory

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Researches

Y. Shiomi, T. Yoshii and R. Kitamura: Platoon-based traffic flow model for estimating breakdown probability at single-lane expressway bottlenecks, Transportation Research Part B: Methodological, Volume 45, Issue 9, pp.1314-1330, 2011.11 T. Yoshii, M. Kuwahara and K. Kumagai: A theory on dynamic system optimal assignment, Proceedings of the Third International Symposium on Transportation Network Reliability, CD-ROM, 2007.7.

1. Traffic control - Dynamic network traffic simulation - Demand estimation - Dynamic information provision - Traffic flow/ Network theory - Driver’s behavior

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Researches

R. Kitamura and T. Yoshii: Rationality and heterogeneity in taxi driver decision: An application of a stochastic-process model of taxi behavior. In H.S. Mahmassani (ed.) Transportation and Traffic Theory: Flow, Dynamics and Human Interaction, Elsevier, Oxford, pp.609-628，2005.7 Now, I am investigating the driver’s route choice behavior when they will get the information about traffic accident risk.

1. Traffic control - Dynamic network traffic simulation - Demand estimation - Dynamic information provision - Traffic flow/ Network theory - Driver’s behavior - MFD

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Researches

Toshio Yoshii, Yuji Yonezawa & Ryuichi Kitamura: Evaluation of an Area Metering Control Method Using the Macroscopic Fundamental Diagram, The 12th World Conference on Transport Research, Lisbon, Portugal, July 11-15, 2010.7.

1. Traffic control - Dynamic network traffic simulation - Demand estimation - Dynamic information provision - Traffic flow/ Network theory - Driver’s behavior - MFD - Traffic safety

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Researches

Toshio Yoshii and Yuki Takayama: Development of a Traffic Accident Simulation Model on Urban Expressway Networks, OPTIMUM 2013 – International Symposium on Recent Advances in Transport Modelling, Kingscliff, Australia, 2013.4

1. Traffic control - Dynamic network traffic simulation - Demand estimation - Dynamic information provision - Traffic flow/ Network theory - Driver’s behavior - MFD - Traffic safety 2. Others - Traffic guide signs

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Researches

T. Yoshii: Symbolization of Intersections using Alphabet Signs, Proceedings of Workshop on Transportation Researches for Urban Safety, CD-ROM, 2008.12.

By using these guide signs for route guidance, drivers can find the intersections earlier where they should make a turn.

1. Traffic control - Dynamic network traffic simulation - Demand estimation - Dynamic information provision - Traffic flow/ Network theory - Driver’s behavior - MFD - Traffic safety 2. Others - Traffic guide signs - Demand estimation model of first-aid transportation service - etc.

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Researches

Today, I will show you about the research on Accident Risk Simulation, which consists of two parts, - network traffic simulation model - accident risk estimation model

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Traffic Control Measures (ramp metering, signal control, etc.)

Traffic States(Q,K,V) can change

Predicted by network traffic simulation

Accident Risk Simulation

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Traffic Control Measures (ramp metering, signal control, etc.)

Traffic States(Q,K,V)

Traffic Accident Risk (likelihood)

Geometric Design (road alignment, merging/diverging, etc)

Road Environment (precipitation, etc)

can change

Accident Risk Simulation

Accident risk simulation is developed, which can estimate the likelihood of occurrence of traffic accidents considering the traffic states.

After developing the Accident Risk Simulation, it must be useful for carrying out effective traffic control measures.

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Traffic States(Q,K,V)

Traffic Accident Risk Geometric Design

(road alignment, merging/diverging, etc)

Road Environment (precipitation, etc)

Accident Risk Estimation Model

The traffic states at each link can be estimated by previous traffic simulations.

Accident risk estimation model should be established in order to develop the accidenr risk simulation.

Accident Risk Estimation Model

Rij：Traffic accident risk for accident type j on state category i [/108 veh*km] αij,βijk：parameters xk：factors

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Linear regression model

nijnijijijij xxxR βββα ++++= ...2211

What is the state categories ? For example, 3 time mean speed : [1-29km/h] , [30-59km/h] , [60km/h-] 2 gradient : [>=+5%] , [<+5%]) 3 road section : [merging], [Toll plaza], [others] → 3*2*3=18 categories of the states

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Data Analysis

Study road network (Hanshin Expressway)

CBD of Osaka

- Traffic counts (volume, time mean occupancy, time mean speed) are observed by 10 detectors at every 5 min. - Accident record (accident type, place, occurrence time, etc) includes 747 accidents in total from 2006 to 2008 - Weather record provides hourly precipitation around the study area - Road alignment (gradient, radius of curvature and Geometric Design merging/diverging, toll plaza) are determined every 100m

3 accident types - Rear-ender collision - Minor collision - Own-crash accident

12km

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Estimation Results(rear-ender collision)

説明変数等 偏回帰係数 t値 P値

低速度ダミー 587.3*** 30.03 0.000

中速度ダミー 163.4*** 15.55 0.000

下り勾配・平坦ダミー 39.6*** 2.50 0.000

分流部手前 48.7* -2.60 0.062

料金所ダミー 113.2*** 1.92 0.000

データ数 5061

R2 0.23

修正R2 0.23

***　有意水準1% **　有意水準5% *　有意水準10%

Traffic accident risk becomes higher in lower speed flow.

Coefficient t-value prob.

speed D ( < 30km/h) speed D (30-60km/h) grade D ( < 0.5%) upstream diverging D toll plaza

Samples R2 adjusted R2

***1%significant **5%significant *10%

1.87

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説明変数等 偏回帰係数 t値 P値

低速度ダミー 88.3*** 9.39 0.000

直線・緩カーブダミー 9.5*** 3.10 0.002

急カーブダミー 15.0** 2.41 0.016

合流部奥ダミー 35.0*** 3.01 0.003

合流部ダミー 37.8*** 3.59 0.000

料金所ダミー 239.6*** 15.97 0.000

データ数 5061

R2 0.08

修正R2 0.08

***　有意水準1% **　有意水準5% *　有意水準10%

speed D ( < 30km/h) curve D ( r > 500m) curve D ( r < 500m) downstream merging D merging D toll plaza D

Coefficient t-value prob.

Traffic accident risk becomes higher in lower speed flow and at toll plaza.

Estimation Results(minor collision) Coefficient t-value prob.

Samples R2 adjusted R2

***1%significant **5%significant *10%

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説明変数等 偏回帰係数 t値 P値

降雨ダミー 34.6*** 4.77 0.000

急カーブダミー 30.3*** 6.42 0.000

合流部ダミー 44.8*** 5.97 0.000

合流部手前ダミー 26.0*** 2.96 0.003

データ数 5059

R2 0.03

修正R2 0.03

***　有意水準1% **　有意水準5% *　有意水準10%

rainfall D curve ( r < 500m) merging D upstream merging D

Traffic accident risk becomes higher in case of rain and at merging section.

Estimation Results(own-crash accident ) Coefficient t-value prob.

Samples R2 adjusted R2

***1%significant **5%significant *10%

From these 3 results, “Rainfall” significantly only affects the traffic accident risk for own-crash accident.

Evaluation of a Ramp Metering Control

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Average Speed at each Link

9:00 – 9:05 a.m.

NO control with control

You can see the traffic improvement by carrying out the control.

Evaluation of a Ramp Metering Control

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Average Speed at each Link

9:00 – 9:05 a.m.

NO control with control

Accident Risk at each Link

You can see the improvement on traffic accident risk by carrying out the control.

In addition to these results the simulation can estimates the accident risks at each links.

Traffic Accident Simulation Model estimates traffic accident risk at each link at each time interval, which shows expected number of accident occurring at a link per 108 veh*kms.

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The Next Study

This study established the accident risk simulation which includes the accident risk estimation model. However, The traffic risk estimation model includes only SPEED. At the next model, the experimental variables determined by TRAFFIC STATES(Q,K) are included. When the simulation estimates the traffic accident risks, it has to use the aggregated data. Because the accident risk estimation model uses the experimental variables determined by aggregated data such as 5min. average speed.

Traffic States

Traffic states must appear on or near the Fundamental Diagram on the Q-K plane.

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交通

流率

(台/h

)

交通密度(台/km)10 20 30 40 50 60 70 80 900

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Q

K

Flow

rate[veh/h]

Density[veh/km]

Free Flow

Congested Flow

Fundamental Diagram

This research uses the aggregated data, which is 5min. average of detector data. In such an aggregated data, at the time interval when the traffic state is changing, traffic states can appear far from the Fundamental Diagram.

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交通

流率

(台/h

)

交通密度(台/km)10 20 30 40 50 60 70 80 900

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Q

K

Flow

rate[veh/h]

Density[veh/km]

Transition of the Traffic State

Free Flow

Congested Flow

Fundamental Diagram

accident

space

0

B.N.

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accident

congestion

Transition of the Traffic State

consider the aggregating time interval of observation by traffic sensors.

Free Cong. Cong. Free

About these two time intervals, both Free Flow and Congested Flow are observed.

time

1 2 3 4 5 6

The traffic states has been changed in the time interval at the observed section, from Free to Congested.

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accident

space

0

B.N.

accident

congestion

Free Cong.

Transition of the Traffic State

time

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1500

1200

900

600

300交

通流

率(台

/h)

交通密度(台/km)10 20 30 40 50 60 70 80 900

When the traffic states has been changed during the aggregated time interval, the traffic state appear far from the Fundamental Diagram.

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accident

space

0

B.N.

accident

congestion

FreeCong.

Q

K

Flow

rate[veh/h]

Density[veh/km]

Free

Cong.

The traffic states in this region are named as “Mixed flow”, which indicates the transition of the traffic state between Free Flow and Congested Flow.

Transition of the Traffic State

Mixed Flow

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交通

流率

(台/h

)

交通密度(台/km)10 20 30 40 50 60 70 80 900

Flow

rate[veh/h]

Density[veh/km]

Heterogeneous Mixed Flow

Heterogeneous mixed flow appears under the situation that the lane traffic states have heterogeneity.

Congested flow

Free flow

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Q

K

Free Cong.

The traffic state far from the Fundamental Diagram appears.

Mixed Flow

2 types of the Mixed Flow is established.

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交通

流率

(台/h

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交通密度(台/km)10 20 30 40 50 60 70 80 900

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Q

K

Flow

rate[veh/h]

Density[veh/km]

TMF : Transitional Mixed Flow HMF : Heterogeneous Mixed flow

Mixed Flow

It is hard to distinguish these 2 Mixed flow perfectly, TMF and HMF.

Impacts on the Accident Risk

Impacts of the 2 types of the Mixed Flow on the traffic accident risk is investigated using actual data.

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Loop 2-lanes

Study network Hanshin Expressway. In this analysis, ・Loop section The mixed flow must include the HMF because it has 4 lanes and it has higher share of weaving section. ・2-lanes section Almost of the mixed flow must be considered as TMF because it has only 2 lanes.

Results ～Rear-ender Collision

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～1 accidents/108 veh・km

1～100 accidents/108 veh・km

100～1,000 accidents/108 veh・km

1,000～ accidents/108 veh・km

Loop (Heterogeneous)

2-lanes (Transitional)

Flow

rate

[veh

/h]

Density[veh/km]

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1500

1200

900

600

300

10 20 30 40 50 60 70 80 900

Mix

Free Congestion

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1200

900

600

300

10 20 30 40 50 60 70 80 900

Flow

rate

[veh

/h]

Density[veh/km]

Mix

Free CongestionCongested Congested

Mixed Mixed

The risks of Mixed Flow and Congested Flow are higher than Free flow in both section.

The risks of Mixed Flow on the loop section are much higher than that of on the 2-lanes section.

These results imply HMF has higher risk than that of TMF.

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Summary

This study - established the 3rd traffic flow state, “Mixed Flow” .

- did a comparison analysis to evaluate the relationship

between accident risks and these 3 traffic states.

⇒ Generally, the risks of the Mixed Flow are higher than that of Free Flow state. ⇒ The risks of Heterogeneous Mixed Flow are higher than those of Transitional Mixed Flow.