Police reported motorcycle crashes in Devon
Paul Hewson
May 30, 2008
1
Contents
I Background 4
1 Executive Summary 5
2 Literature Review 6
2.1 Key Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2.1 Motorcycle injury is inevitable . . . . . . . . . . . . . . 8
2.2.2 Off-road riding . . . . . . . . . . . . . . . . . . . . . . 10
2.3 Road-riding risks . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.1 Alcohol . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.2 Bike types . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4 Young riders . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4.1 Risk taking . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4.2 Socio-economic differentials . . . . . . . . . . . . . . . 14
2.5 Changes in behaviour . . . . . . . . . . . . . . . . . . . . . . . 15
2.5.1 Behavioual research . . . . . . . . . . . . . . . . . . . . 15
2.6 Ongoing U.K. research . . . . . . . . . . . . . . . . . . . . . . 17
2.6.1 “Looked but failed to see” patterns . . . . . . . . . . . 17
2.6.2 Older motorcyclists . . . . . . . . . . . . . . . . . . . . 18
2.6.3 “Multivariate” analysis . . . . . . . . . . . . . . . . . . 18
2.6.4 In-depth study . . . . . . . . . . . . . . . . . . . . . . 19
2.6.5 “On-the-spot” study . . . . . . . . . . . . . . . . . . . . 19
2.7 Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2
3 Overview 21
II Exploratory Data Analysis 25
4 Motorcycle crashes 26
4.1 Weekly and daily patterns . . . . . . . . . . . . . . . . . . . . 26
4.1.1 Seasonal patterns . . . . . . . . . . . . . . . . . . . . . 31
4.2 Road surface . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.3 Text mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.4 Weather . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.5 Road layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.6 Road Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.7 Key points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5 Riders 45
5.1 Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.2 Bike types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.3 Origin of the riders . . . . . . . . . . . . . . . . . . . . . . . . 51
5.4 Analysis of accident types . . . . . . . . . . . . . . . . . . . . 57
5.5 Manoeuvres . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.5.1 Comments . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3
Part I
Background
4
Chapter 1
Executive Summary
This report examines the police reported motorcycle crashes with Devon. As
far as is possible, this exploratory data analysis is carried out with respect
to published research on motorcycle crashes.
Table 1.1 provides summary information on the number of crashes re-
ported in 2006:
Fatal Serious Slight Damage Only
Calendar Year 2006 7 84 383 165
Table 1.1: Sanity check: reported crashes in 2006
5
Chapter 2
Literature Review
2.1 Key Points
� Injury / mortality burden now dominated by more chronologically ma-
ture riders
� Socio-economic differentials are particularly pronounced amongst young
riders
� Females may be at relatively more risk of more serious injury
� There are limits to the role of medicine in mitigating injury conse-
quences
� Motorcycles can cause signficant injury burdens even when not being
used for transport (e.g. exhaust pipe burns among children, off-road
riding)
� Current research concentrates on behavioural issues, but concerns over
highways and violations of right of way abound
� Arguments have been made that powered two wheelersusers need to be
more cautious about the effects of alcohol
� Machine type and size are related to relative injury severity
6
2.2 Overview
The UK has seen a recent increase in the number of motorcycle fatalities, a
feature also seen in Devon. This is not a UK-only phenomenon, for example
Paulozzi (2005) reports a recent increase in fatalities in the US which was
claimed to be largely due to recent increases in sales of powered two wheelers.
There is still relatively little published research in relation to motorcycle
accidents in Britain. Woodward (1983a) considered accidents in Notting-
hamshire, and briefly considered national trends (Woodward, 1983b) in re-
lation to motorcycle accidents. It is noteworthy that this was written at a
time that motorcycle accidents were on the increase nationally. At that time,
motorcycle fatalities were associated with riders from lower Socio-Economic
Status groups, dominated by late night urban accidents with a frequent al-
chohol involvement. Fatalities peaked at age 20. One of the seminal studies
on motorcycle accidents conducted in the U.S. (the “Hurt Study”) dates from
a similar period (Hurt Jr. et al., 1981).
More recent work in Germany (Wick et al., 1998), based on an analysis of
hospital admissions, suggested that accidents were peaking amongst 25 - 29
year olds, and now these were predominantly 3pm - 10pm at weekends, and
that over 23
of the bikes were in excess of 500cc. In this particular work, 40% of
the drivers felt responsible for the accident, mainly through speeding or risky
overtaking, although 75% of the accidents appeared to involve violations of
the motorcyclists right of way. Significantly, there was only one case recorded
where alchohol seemed to contribute. About a third of the motorcyclists had
passed their test within the last two years, but amongst the third who had
held a licence for over 8 years few rode a bike regularly.
The current situation both in the UK and in Devonappears very different
to that depicted by Woodward. Recently in the U.K. (Lynam et al. (2001),
examined 717 police reports of fatal motorcycle accidents suggested that a
high proportion were single vehicle loss of control, often linked with excessive
speed, alchohol or careless behaviour. Where other road users were judged
responsible, the most common factors were “failed to give way”, “poor turn
7
or manoeuvre”, often associated with a failure to judge the riders path or
speed. There was an interesting contrast between accidents when the motor-
cyclist was judged primarily responsible, in that mean speed was estimated
at 57mph, and accidents where the other road user was primarily responsi-
ble, where the mean speed was estimated at 43 mph. Lynam et al. (2001)
noted that the age of “other” road users responsible for motorcycle accidents
peaked between 30 and 60, but the scant evidence in this study suggests that
may be artefactual. However, what was noted was that in lesser severity
accidents, excessive speed on the part of the motorcyclists was less likely to
be recorded, and “looked but did not see” more likely to be reported.
Data issues dominate this subject whereever it is studied. Whilst issues
around STATs19 are well documented, the US Fatal Accident Record System
(FARS) is not perfect, for example Lapidus et al. (1994) noted that 42% of
US reported fatal motorcycle crash records had errors. There are therefore
a number of valuable hospital based studies in the literature which provide
detail not available from police based crash reporting systems. The United
States has been source of much of the exisiting literature, in particular the
National Highways Transport Safety Agency. Of specific interest on this
site are details of rider attitude research and OECD collision data collection
recommendations. However, in practice, police collision data in the UK will
remain confined the instructions given in STATs 20 and there may be little
scope to influence collection requirements.
2.2.1 Motorcycle injury is inevitable
Hinds et al. (2007) essentially consider motorcycle injury inevitable, and high-
lighted a number of medical necessities essential for dealing with the kinds
of trauma seen from motorcycle injuries. This would imply that personal
protective equipment is essential. Ankarath et al. (2002) notes that head
injury was the most common cause of death. Whilst helmets are mandated
in the UK (and the relevant literature not reviewed here, e.g. Greenlan-
der (1994), Weiss (1994)) the same is not true of other personal protective
8
equipment. Haasper et al. (2006) reports that knee injury was particularly
common amongst motorcyclists. Matzavakis et al. (2005) comments that
burn injuries from exhausts can be prevented by suitable clothing. Jeffers
et al. (2004) comments on foot injury and Suri et al. (2007) reports on heel
flap injury both of which could be avoided by the use of suitable clothing.
Indeed, Peek et al. (1994) highlighted that in addition to the design of the
other vehicle, personal protective equipment may be particularly important
in mitigating lower limb damage in multiple vehicle collisions. Of interest to
some riders may be the work of Horvath et al. (1993); Mulhall et al. (1995);
Ko et al. (2004) who report on occurences of testicular dislocation following
a motorcycle crash. Whilst motorcycle injury predominantly affects males,
de lapparent (2006) is a particularly interesting study as they found that
women 30-50 were at greater risk of more serious injury relative to the total
number of injuries suffered by any age/gender group.
Apart from consideration of trends, there is ample evidence that mo-
torcycle casualties represent a disproportionately high injury burden. For
example in hospitals in Spain (Plasencia et al., 1995) or Singapore Quddus
et al. (2002). Motorcycle injury is not confined to “road users”, for example
Roberts et al. (2002) commented on a significant number of serious exhaust
burns suffered by children.
One issue that perhaps should be highlighted surrounds the potential for
improved health care. Sinha et al. (1995) for example noted that specialist
trauma centre (which do not necessarily have universally close geographical
proximity to a casualty throughout the UK) are important in reducing mor-
tality. Indeed, some of the trends in the number of fatalities on the roads in
the last few decades may indeed be due to better emergency care. However,
in a Scottish study, Wyatt et al. (1999) argued that there was little potential
to reduce fatalities by better treatment and therefore argued strongly for an
increased emphasis on preventative measures.
9
2.2.2 Off-road riding
The burden of off-road riding has been examined, for example by Pomerantz
et al. (2005) but is not considered further here, suffice to say that Colburn
and Meyer (2003); Gobbi et al. (2004); Gorski et al. (2003) claim that off-road
motorcycing is relatively “safe” in terms of the severity of injury incurred by
riders (i.e. there may be a large number of injuries but they are usually of
low severity). This report only considers on-road injuries.
2.3 Road-riding risks
In the road context, it may be noted that Horswill and Helman (2003) suggest
that motorcyclists experience 9.3 times more risk per unit time than car
drivers and 7.9 times more risk per unit distance than car drivers (based on
analysis of STATs 19 data for 1997 - 1999 and National Travel Survey data
for a similar period). They suggest three reasons for the disparity in risk:
� Physical vulnerability (both due to less mechanical protection and less
machine stability)
� Behaviour of others (where both sensory conspicuity and cognitive con-
spicuity are components)
� Behaviour of themselves
There are a number of features which deserve closer examination, for
example Chang and Wang (2006) noted that vehicle type could be dominant
explanation behind injury severity. However, motorcycle research currently
tends to concentrate on behavioural issues (Chesham et al., 1993).
Although motorcyclist issues will be identified (for example conspicuity
and daylight running lights are promoted as protective for the rider), issues
around the “other” participant are important, and there is work indicating
that drivers may need some training in this regard (Horswill et al., 2005).
10
In terms of behavioural issues, risk taking is a commonly identified feature
of motorcycle injury at all ages. What is most interesting is that there has
been a change in the demographics. More recent work, such as Savolainen
and Mannering (2007) demonstrates that increasing age is now associated
with increasing severity risk. This contrasts with the situation seen 20 years
ago, where young riders dominated the injury toll. For example, Braddock
et al. (1992) indicated that the death rates was highest amongst 20-24 year
olds, in New Zealand, Langley et al. (1994) highlighted the injuries incurred
by young riders and in the US, Bueno et al. (1992) also highlighed young
males, and suggested that risk taking behaviours may have been associated
with the injuries.
Most countermeasures have concentrated on factors that can be changed
by the motorcyclist. Wells et al. (2004) reported that conspicuity was im-
portant,Yuan (2000) reports on a Taiwan based study concerning daylight
running lights which was not statistically significant. Nevertheless, sensory
conspicuity (making the motorbike easier to distinguish from its background
by daylight running lights or clothing choice) have been investigated in some
depth, whereas cognitive conspicuity, the ability of others to interpret the
actions of a motorcyclist have not been much influenced. The Hurt study
showed that the car involved in a car - motorcycle collision was less likely to
be familiar with motorcycles.
This exploratory data analysis will confirm that violations of a motorcy-
clists right of way (by vehicles turning into or emerging from a side road)
contribute considerably to the accident toll within Devon. Ideally the coun-
termeasures considered will examine the potential of both potential collision
participants to reduce the likelihood of a crash.
2.3.1 Alcohol
Nelson et al. (2006) conducted a medical study of crashes involving deer in
the US. The suggestion was made that riders should be subject to a lower
alcohol limit due to the additional complexities of their transport. Sun et al.
11
(1998) has also suggested that troublesome levels of blood alcohol tended
to be lower amongst injured motorcyclists and suggested lowering the limit.
Holubowycz et al. (1994), based in Southern Australia noted that a higher
alcohol was seen in single vehicle and weekend crashes. This is an issue that
cannot be considered with STATs19 data as these only contain information on
breath tests (i.e. pass fail relative to a threshold rather than absolute alcohol
concentrations). Certainly, high levels of alcohol have been identified as a
risk factor in many motorcycle crashes. Savolainen and Mannering (2007)
found alcohol, speed, and roadway characterists were important risk factors.
Peekasa and Kraus (1996a) highlighted alcohol content in injured Californian
riders. Bolhofner et al. (1994) noted that 90% of injured motorcyclists in
Florida were male, but that overall 36% had high blood alchohol levels.
2.3.2 Bike types
Arzemanian et al. (1993) examined crash locations for racing and street
type motorcyles, and found that they were similar. Although the number
of crashes for racing type bikes is high, only 13 / 441 deaths were recorded
as being related to a racing activity. Again, the STATs19 contain limited
information on the type of motorcycle, and this is not an aspect that is eas-
ily studied. Quddus et al. (2002) noted risk factors for injury which also
include large engine (as well as lack of daylight running lights), pillion rid-
ers. Interestingly, they suggested that crashes in which the motorcyclist was
more blameworthy tended to be more serious than ones in which the other
participant was more blameworthy.
2.4 Young riders
That young riders are no longer the highest risk group for motorcycle mor-
tality has already been noted. Much earlier work, such as Begg et al. (1992)
identifed a number risk factors for young riders which have been addressed
by legislation, although it remains the case that a strong link with risk taking
12
behaviour is very apprarent in this group (Everett et al., 2001). One choice
related issue that has emerged in New Zealand (Langley et al., 2000) is that
the engine size of a motorcycle has particularly strong implications for young
riders.
Most of the case-controlled road safety research recently appears to have
originated from Australia or New Zealand. It would appear that riding rates
are lower in the UK. In New Zealand, Reeder et al. (1995) found that 51% of
adolescents could ride. When considering the non-riders, fear of injury was
the reason 55% of them hadn’t learnt to ride. According to this study, females
less likely to wear personal protective equipment. Reeder et al. (1996) found
that mothers were the main source of dissaproval towards motorcycling.
In a motorcycling context, (Mullin et al. (2000)) consider age and expe-
rience as protective factors against motorcycle accident involvement. They
suggest:
� the association between age and lower risk of motorcycle injury was
confirmed
� little evidence that experience, either on a motorbike or in a car are
protective once allowance has been made for age
� familiarity with the specific motorcycle is the only experience measure
having a protective effect
Mullin et al. (2000) therefore make some clear recommendations in rela-
tion to age; continuing the age stratification of licencing requirements, with
the additional condition of introducing conditions that encourage use of a
familiar machine. However, if there is evidence to support this locally, there
is a clear role for increasing experience without necessarily the requirement
to have particularly formal training sessions. It may therefore be possible to
deliver training packages that have wider appeal.
2.4.1 Risk taking
Reeder et al. (1996) emphasised that even“young motorcyclists” forms a very
13
heterogenous group, but commented (in New Zealand anyway) that early in-
formal off-road training may have established innappropriate behavious that
were subsequently transferred on road. In the US, Kraus et al. (1991) re-
ported that non-owners were more likely to ride non-licenced, and that this
was particularly apparent amongst young riders. Reeder et al. (1995) demon-
strated the importance of unlicenced riding and borrowed bikes amongst
young riders in New Zealand. Of 217 motorcyclists, 72% had borrowed a
friends bike, 86% had ridden on public roads before being licenced. This
puts later work on the importance of familiarity Mullin et al. (2000) with
your machine into context and would be a fascinating piece of information
to have for young Devon riders.
A number of risk factors have been noted which are associated with in-
creased crash severity. These include speed, but also in Taiwan rurality has
been noted (Lin et al., 2003). Lin et al. (2003) identifed a high risk subgroup
which included factors around age, experience and licence holding although
none of these were statistically significant. Spain et al. (1997) emphasised
alcohol alongside other risk taking behaviours and Middleman et al. (1995)
commented on anabolic steroids being associated with high risk behaviour
and motorcycle crash severity.
2.4.2 Socio-economic differentials
One feature which emerges strongly from the published research globally is
that there are constant socio-economic differences in terms of injury rates.
Zambon and Hasselberg (2006) reports on a national Swedish cohort and
found socio econonomic differences most pronounced in frst year of riding
such that the lower SES were 20% more likely to be injured on a moped.
This association with Socio-economic status seems to universal linkage, for
example in Turkey Oksuz and Malhan (2005) an association was also seen
(and it was proposed that it was mediated via high risk behaviours).
14
2.5 Changes in behaviour
The psychological sequelae of road traffic injury (Mayou et al., 1993) are well
documented, yet Lin et al. (2004), studying self reported behaviour in Taiwan
found that previous crashes had no apparent effect on risk taking behaviour
accoring. Mangus et al. (2004) also found that previous crashes had little
influence on the use of personal protective equipment.
2.5.1 Behavioual research
Swaddiwudhipong et al. (1998) report a Taiwanese educational intervention,
unfortunately the study is rather small and although the results appear
promising they are too small to claim significance. There appears to be
little other work formally published evaluating educational interventions on
rider safety.
Rutter et al. (1998) describe a natioanal postal survey of motorcyclists
which revealed that UK motorcyclists tended to have an over-optimistic view
of the risk they were exposed to. There was some realism, in that younger
riders and prolific risk takers were aware that they were more at risk that the
average motorcyclist, albeit their assessments of their risk and average risk
were wildly over-optimistic. Personal knowledge of another motorcyclist who
had been killed or seriously injured increased their assessments of overall risk,
without altering the over-optimistic assessment of their own riding ability.
The most significant finding of this work was that higher assesment of risk
at time 1 predicted higher levels of safety abandonment at time 2. There are
some clear cautions in this work as to how elevating riders sense of risk could
be counter-productive.
There is some Australian data available via the LTSN at Glasgow Univer-
sity. A data set intended for undergraduate projects is described. The origi-
nal project was conducted in Australia as an attempt to determine whether
riders premiums’ should increase or not following an accident. In effect this
was meant to indicate whether riders learnt from an accident. By question-
15
naire, riders were asked:
� Time subject has ridden a motorcycle for
� Had accident , Never had accident
� Time until first accident occurred
� Size of bike ridden in first accident
� Injured in first accident, not injured
� Had second accident , no second accident
� Time until second accident occurred
� Size of bike ridden in second accident
� Injured in second accident, not injured
However, there is much more careful and detailed research available. In
E.S.P.R.C. funded research, Horswill and Helman (2001) set up laboratories
to assess driving and riding behaviour, and compared matched groups of
motorcyclists and car drivers in a laboratory setting simulating car driving
and riding a bike and also subjected the particpants to a battery of stan-
dard tests in relation to attitudes, sensation seeking and so on (for which
the partipants were given £15). In total, they compared three demograph-
ically matched groups, motorcyclists riding simulated motorbikes, motorcy-
clists driving simulated cars and car drivers driving simulated cars. They
suggested that there was little difference between the groups in terms of gen-
eral measures of sensation seeking and social motives, or attitudes towards
driving and riding. The differences were found amongst riders when riding a
machine such that:
� Motorcyclists on laboratory motorcycles took more risks in terms of
speed / attitudes and gap acceptance / overtaking than either of the
other two groups.
16
� Motorcyclists in laboratory cars took less risks with gap acceptance /
overtaking than car-drivers.
It was noted that there were significant numbers of motorcycling enthu-
siasts in the study group, and that overall the study group had a younger
than average and higher milage than average membership, also containing
larger numbers of advanced trained drivers / riders than may have been an-
ticipated. This tends to suggest that motorcyclists may not be an aberrant
group, but that they may exhibit slightly riskier behaviour when riding.
2.6 Ongoing U.K. research
There are a number of current research projects which have a bearing on anal-
ysis of motorcycle accidents. It may be useful to summarising current DfT
projects, and closely related work to see where there is overlap or potential
for some collaborative working.
2.6.1 “Looked but failed to see” patterns
DfT project S240M examines “Looked but Failed to See” accident causa-
tions. The study has been commissioned with a literature review, and to
examine whether the phenomena is genuine. If there is evidence to support
the existence of this phenomena the project obviously aims to see whether
it is amenable to research. If amenable to research, the project would be
conducted with a view to developing countermeasures. The project was let
to Ivan Brown associates and was due for completion in November 2001.
Martin Langham and Graham Hole of the University of Sussex are also in-
terested in “Looked but Failed to See” errors. Based on analysis of Sussex
Police data, they have suggested that motorcycle accidents involving another
vehicle tend to occur at uncontrolled junctions in uncongested urban envi-
ronments. Within that, they have suggested that they believe T junctions
are over-represented but roundabouts are under-represented. Examination of
17
the “driver” involved in the accident suggests that there is no peak amongst
younger drivers, which they suggest implies that this causation has a prone-
ness that does not reduce with experience. They further suggested that
studies from fatal accident records imply that conspicuity enhancers do sig-
nificantly reduce the chances of a serious accident but there evidence for this
is not given. Their research suggests that “looked but failed to see” is a
reflection of the rarity of two wheel motorvehicles on the road, and drivers
being unaccustomed to having to check for their presence before initiating
a manoeuvre. This is rather different to other theories that suggest “looked
but failed to see” is characterised by a subconcious assessment of the risk the
other object poses to the driver.
2.6.2 Older motorcyclists
Leeds University have been awarded project S501B“The Older Motorcyclist”.
This project was let on the premise that killed and seriously injured casuatl-
ties amongst riders in the 30 - 59 age group have increased gradually in the
last decade. Other than age, little information is available on rider charac-
teristics particularly in relation to skills, experience history or exposure and
this project seeks to remedy this knowledge gap.
2.6.3 “Multivariate” analysis
Project S501F “Multivariate analysis of Existing data on factors affecting the
accident risk of Motorcyclists” is mentioned but few details are given, likewise
there is a project listed, S501G, “Scoping study on Motorcycle Training” to
review the content and practice of existing training provision and identify
gaps in knowledge. These two projects have the greatest potential for overlap
with the work initiated here. Although few details are given, it may be
possible that Project S501F wishes to use the methodology of Mullin et al.
(2000).
18
2.6.4 In-depth study
New project, S501A, “In-depth study of motorcycle and work-related acci-
dents” has been let to Nottingham University, and intends to take a detailed
analysis of police road accident files sampled from Nottinghamshire, Der-
byshire and Leicestershire forced for 3 - 5 years and will focus on fatal and
serious accidents with under-sampling of slight accidents. The aims of the
research are to identify the incidence of particular factors such as errors, vio-
lations or riding style by age, gender, experience, type of vehicle, manoeuvre,
time and location type. It also aims to identify potential countermeasures
and estimate their effectiveness and to report all this work in an accessible
manner. As with the previous two studies, there is some potential for overlap
with the work initiated here.
2.6.5 “On-the-spot” study
Finally, one particularly interesting project, the“On the Spot”Study is being
run by from Loughborough and Crowthorne. Despite repeated attempts to
make contact, it has not been possible to speak with the project manager to
discuss the possibility of some collaborative work.
2.7 Aims
Despite the current focus on behavioural issues, this report will examine per-
ceptions of external risks (highways conditions) and align this with rider per-
ceptions. Anecdotally there has been much discussion of riding lines around
corners, yet for example Matthes et al. (2007) reported that most tree crashes
happen on straight roads.
In doing this, it will be useful to make use of a crash typology. Kim et al.
(2002) developed a typology of crashes for use in Hawaii. This report will
consider a related typology. In medical based research, we note for example
that Peekasa and Kraus (1996b) found that, with the exception of head on
19
collisions, approaching turn crashes were the most serious and it is therefore
useful to understand the crash types.
20
Chapter 3
Overview
Figure 3.1 shows a large decline in reported serious crashes, reported fatal
crashes look fairly constant - this in confirmed by zooming in on fatals only
in figure 3.2.
21
Year
Rep
orte
d cr
ashe
s
0
100
200
300
400
1995 2000 2005
● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
Fatal
●
●● ●
● ●● ● ● ●
● ●
● ● ●
●
Serious
● ● ●●
●●
● ●
●
●
●
● ● ●
●
●
Slight
1995 2000 2005
0
100
200
300
400
●
●
● ●
●● ● ●
●
●●
●
●●
●
●
Damage Only
Figure 3.1: Medium term Devon Trends
22
Year
Rep
orte
d C
rash
es
5
10
15
1995 2000 2005
● ●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
Figure 3.2: Medium term Devon Trends - fatalities only
23
All Fatal Collisions
Figure 3.3: All fatal collisions reported
24
Part II
Exploratory Data Analysis
25
Chapter 4
Motorcycle crashes
Initially, this stage of the report will consider the circumstances around re-
ported crashes involving motorcycles. Seasonal / temporal patterns are the
easiest to examine, and this is done first.
4.1 Weekly and daily patterns
Figure 4.1 and figure 4.2 contrast the weekday crash patterns from before
and after 2002. The patterns are similar; it would appear that slight crashes
are most common on weekdays whereas it is difficult to immediately see any
obvious pattern regarding fatalities.
Figure refdayhour is a filled contour plot which illustrates a further detail
concerning the time patterns involved in motorcycle crashes. It can be seen
for example that on weekdays the reported crashes follow the rush hour. A
different pattern is seen at weekends where crashes during the day become
more dominant. Conversely, figure 4.4, which only examines fatal crashes,
suggests that Sunday afternoons have been the most common time for fatal
collisions involving motorcycles.
26
Sun Mon Tue Wed Thu Fri Sat
010
020
030
040
050
060
0
Fatal Serious Slight Damage Only
Figure 4.1: Collisions by weekday by severity from 2002 onwards
27
Sun Mon Tue Wed Thu Fri Sat
020
040
060
080
010
0012
00
Fatal Serious Slight Damage Only
Figure 4.2: Collisions by weekday by severity before 2002
legend(”topleft”, col = c(”red”, ”orange”, ”green”, ”grey”), legend =
c(”Fatal”, ”Serious”, ”Slight”, ”Damage Only”), ncol = 4)
28
0
1
2
3
4
5
6
7
Sun Mon Tue Wed Thu Fri Sat
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Figure 4.3: Day and time patterns
29
0
1
2
3
4
5
6
7
Sun Mon Tue Wed Thu Fri Sat
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Figure 4.4: Day and time patterns for motorcycle crashes involving a fatality
30
Jan Mar May Jul Sep Nov
010
020
030
040
0
Fatal Serious Slight Damage Only
Figure 4.5: Crashes by month by severity since 2002
4.1.1 Seasonal patterns
A further complexity in considering seasonal patterns is that riding and crash
behaviour varies throughout the year.
Figure 4.5 reports the crashes by severity by month from 2002, figure 4.6
reports the same information for the before 2002 period. It would appear
that crashes of all severities are most common during the summer months.
Whilst this fits with established ideas regarding motorcycling as an activity
influenced by the weather, it is a feature of the data that requires emphasis.
The usually riskier winter conidtions (bad weather, poor visibility amongst
other road users, icy roads) tends to mean that crash risk is higher at that
time of year - yet despite this the numbers of motorcycle crashes during the
31
Jan Mar May Jul Sep Nov
010
020
030
040
050
060
070
0
Fatal Serious Slight Damage Only
Figure 4.6: Crashes by severity by month from before 2002
32
0
1
2
3
4
5
6
7
Sun Mon Tue Wed Thu Fri Sat
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Figure 4.7: Day and month patterns for reported motorcycle crashes
summer are considerably bigger.
As with earlier plots, figure 4.7 and figure 4.8 highlight the evidence for an
increased fatality risk at weekends. In particular, figure 4.7 - the all severities
plots indicates that Sunday crashes are most common during the Summer,
and figure 4.8 does appear to confirm the importance of Saturday and Sunday
during the summer months.
4.2 Road surface
As will be seen later, issues around road surfacing are raised as a concern
by users of motorcycles. It is therefore somewhat informative to examine
33
0
1
2
3
4
5
6
7
Sun Mon Tue Wed Thu Fri Sat
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Figure 4.8: Day and month patterns for motorcycle crashes involving a fa-
tality
34
Missing Dry Wet Snow Flood Mud
0
500
1000
1500
2000
2500
Fatal Serious Slight Damage Only
Figure 4.9: Reported road surface conditions
the police reports in relation to road surface conditions generally. Figure 4.9
confirms that most reported crashes appear to take place on dry roads, this
is also seen in the pre-2002 data (table 4.10).
Figure 4.9 depicts the reported road surface conditions from 2002 on-
wards, figure 4.10 depicts similar information for crashes from before 2002.
In terms of STATs19 completion, the figure of 0 crash reports indicating
diesel involvement is notable when contrasted with perceptions of such road
surface conditions.
35
Missing Dry Wet Snow Flood Mud
0
500
1000
1500
2000
2500
3000
3500Fatal Serious Slight Damage Only
49
2973
1250
0 22 1 0 0
Figure 4.10: Reported road surface conditions - crashes reported before 2002
36
4.3 Text mining
It may be that the STATs19 completion in terms of less used parts of the
form is more difficult (or that the choices to be made are a little ambiva-
lent, for example are you supposed to put “dry” or “diesel” if both are
true). This report has therefore used generalised regular expressions (see
http://www.gnu.org/software/grep/ for more information on this very pow-
erful utility) as a simple way of mining the free text accident descriptions for
any words than may suggest diesel spills as a contributory factor.
It is worth highlighting the use of “regular expressions” as a simple form
of text-mining. The words diesel and oil can be searched in the free text
description. The power of regular expressions is that one can specify for
example that we are searching for words starting with “D”, containing an “S”
and ending in “L”. This allows us to capture mis-spellings (such as DEISEL)
as well as abbreviations (such as “D’SEL”). It also means we can insist when
searching for“OIL”that the word starts with“O”. This prevents us extracting
words containing “oil” such as “(aero)foil”. In total, 57 crashes were identified
involving oil or diesel.
Table 4.1 denotes the severity versus road section for reported crashes
where (so far) we have noted “diesel” in the free text (using a grep to find
diesel - to account for some possibe mis-spellings). Figure 4.11 denotes the
location of these crashes.
Whilst higher than the relevant STATs19 coding, these numbers are still
rather modest, and contast strongly with the number implied by the “Bring-
ing Bikers out of the Blind Spot” survey. This survey reported that 96 out of
634 bikers stated that they had been involved in a crash that involved diesel.
In comparison, the numbers for motorcylists who reported in the “Bring-
ing Bikers out of the Blind Spot”surveythat they had been involved in crashes
as a result of manhole covers were somewhat smaller, namely 23 out of 631.
GREPping the STATs19 free text descriptions for any mention of manholes
apparently gave no matches.
37
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Crashes involving diesel
Figure 4.11: Crashes where diesel was reported
38
Fine Rain Fine/Windy Snow/Wind Other
0
1000
2000
3000
4000Fatal Serious Slight Damage Only
3622
419
4 56 49 0 10 70 65
Figure 4.12: Reported weather conditions in post 2002 crashes
4.4 Weather
It was noted earlier that riding crashes were most common in the Summer
months; this fits with a perception that riding is largely a fair weather activity
and that the increase in crashes in the Summer is a feature of increased
exposure.
Figure 4.12 confirms that the greatest number of crashes happen in fine
weather.
Furthermore, figure 4.13 confirms that motorcycle crashes are more likely
to occur in fine weather regardless of the month of the year.
39
Jan Mar May Jul Sep Nov
010
020
030
040
0
FineRainSnow
Fine/WindyRain/WindSnow/Wind
FogOtherUnknown
Figure 4.13: Weather conditions by time of year recorded in motorcycle
crashes
40
Figure 4.14: Junction details at site of reported crashes
4.5 Road layout
Junction types are reported in figure 4.14. This figure seems to confirm the
risk of crash involvement at T/Y junctions, but it is most apparent that
the greatest number of crashes are reported on straight road. This will be
xplored more later.
4.6 Road Types
Further information on the risk factors for motorcycle crashes can be gleaned
by considering the road code at which reported crashes occurred. Table 4.2
confirms that most reported crashes took place on 2 lane signle carriageways
of all classification.
The general location of these roads can also be assessed to some extent by
considering the speed limit (this provides some discrimination between rural
and urban roads). Since 2002,table 4.3 indicates that most crashes involving
a fatality were reported on 30mph roads, most serious and slight crashes were
reported on 40mph roads. Table 4.4 indicates a similar pattern prior to 2002.
4.7 Key points
Most motorcycle collisions involving a fatality were reported on 30mph roads,
whereas for serious and slight crashes 40mph roads were more common.
There is evidence that fatal crashes are more often reported on a Sunday,
and that usually the report indicates that the weather was fine at the time
of a crash, regardless of the time of year. Whilst motorcylist perceptions of
diesel hazard is high, even having conducted a text-mining exercise there is
very little information in the reported data that this is a common risk factor
41
for reported injury crashes.
42
Fatal Serious Slight Damage Only
A3022 0 0 1 0
A30M 0 0 2 0
A3122 0 0 1 0
A361 0 1 0 0
A374 0 0 6 1
A376 0 0 0 1
A379 0 0 4 1
A38 0 0 1 0
A380 0 0 0 1
A382 0 0 0 1
A385 0 0 1 0
A386 0 0 3 0
A38M 0 1 1 0
A396 0 1 0 0
B3172 0 0 1 0
B3212 0 0 3 0
B3213 0 0 1 0
B3260 0 0 1 0
B3396 0 0 1 0
C11 0 1 0 0
C14 0 0 1 0
C487 0 0 1 0
C5 0 0 1 0
C660 0 1 0 1
C70 0 0 1 0
C737 0 0 1 0
C771 0 0 0 1
C80 0 0 2 0
C817 0 0 1 1
C87 0 0 1 0
U 0 2 4 2
Table 4.1: Crashes on roads where diesel reported in free text43
A B C M U
Roundabout 361 109 62 3 92
One way street 40 20 38 1 145
Dual carriageway - 2 lanes 608 96 21 29 82
Dual carriageway - 3+ lanes 0 0 0 0 0
Single carriageway - single track 0 0 0 0 0
Single carriageway - 2 lanes 2383 1489 1613 0 2889
Unclassified 38 10 8 9 35
Single carriageway - 3 lanes 0 0 0 0 0
Unknown 450 211 262 2 504
Table 4.2: Road type where crashes reported
0 5 10 15 20 30 40 50 60 70
Fatal 0 0 0 0 1 49 6 2 6 9
Serious 0 0 1 0 6 429 46 4 43 21
Slight 1 0 7 2 25 2012 169 16 167 39
Damage Only 133 4 8 1 14 954 34 1 77 8
Table 4.3: Speed limit in force (post 2002)
0 1 2 4 5 10 15 20 30 40 50 60 70
Fatal 0 4 0 1 0 0 0 0 83 7 4 1 12
Serious 1 35 3 5 0 1 0 0 1178 93 9 13 55
Slight 2 99 6 5 2 1 1 3 3319 268 38 62 93
Damage Only 1114 65 2 2 2 8 0 2 665 33 2 5 11
Table 4.4: Speed limit in force (before 2002)
44
Chapter 5
Riders
The next chapter considers riders involved in motorcycle crashes. Here, ref-
erence shall be made to the“Bringing Bikers out of the Blind Spot” survey. It
should first be noted that there is a mismatch between the age/gender profile
of survey respondents, and the age/gender profile of crash involved riders.
Female Male No reply
Under 21 2 18 0
21 to 29 13 48 3
30 to 39 17 125 2
40 to 49 33 194 5
50 to 59 11 122 2
60 and over 1 46 0
No reply 0 0 6
Table 5.1: Age and gender of survey respondents
Table 5.1 indicates the age and gender of respondents to the “Bringing
Bikers out of the Blind Spot” surveysurvey. In contrast, the riders involved
in reported motorcycle crashes are as reported in table 5.2. It is striking
that rather more emales are crash involved than responded to the survey.
Also, the dominant crash involved age group are the under 21s, yet these are
45
almost entirely absent from the survey.
FEMALE MALE
Under 21 341 3387
21 to 29 302 2593
30 to 39 166 1989
40 to 49 123 1124
50 to 59 92 521
60 and over 52 284
Table 5.2: Age and gender of motorcyclists involved in police reported colli-
sions
No reply No Yes Percent commuting
Under 21 0 1 19 95
21 to 29 0 23 41 64
30 to 39 5 47 92 64
40 to 49 2 114 116 50
50 to 59 1 76 58 43
60 and over 1 35 11 23
No reply 6 0 0 0
Given the preponderance of young people in the crash statistics, it is
intresting to examine the survey reponses with relation to reasons for trav-
elling by motorcycle. Table ?? gives the breakdown for commuters and non-
commuters by age, it is quite striking in table ?? that the proportion of survey
respondents who use a motorcycle for commuting decreases with increasing
age.
Table ?? suggests that the females completing the survey were less likely
to commute than the males. No doubt this will in part be confounded by the
age of respondents.
46
No reply No Yes
Female 1 43 33
Male 8 249 296
No reply 6 4 8
The “Bringing Bikers out of the Blind Spot” surveycontained a question
on self-reported crash involvement and points on licences. The results are
summarised in table 5.3.
No reply No Point Points
No reply 7 0 1
No Crash 1 393 104
Crash 0 109 33
Table 5.3: Self reported association between points on licence and crash in
previous 3 years
Out of interest, a very simple χ2 hypothesis test for looking for an associ-
ation between crash history and points on licence gives the followin p-value:
0.633653267850278. It should be noted that this is an opportunistic survey
(self selecting and largely web-based), so we have no real idea which popula-
tion are represented in these figures.
It would be most useful to be able to compare these figures with licence/points
prevalence for the wider public
No reply No Crash Crash
1 8 502 86
2 0 0 52
Table 5.4: Self reported association between points on licence and crash in
previous 3 years
47
No reply No Crash Crash
1 8 502 54
2 0 0 84
Table 5.5: Self reported association between points on licence and crash in
previous 3 years
Figure 5.1: Crash involved motorcyclists by gender and year
Tables 5.4 and 5.5 provide a breakdown for points incurred by “Bringing
Bikers out of the Blind Spot” surveyrespondents while riding a bike and while
driving a car respectively.
5.1 Gender
Figure 5.1 looks at trends over time in males and females involved in motor-
cycle crashes.
It would be extremely interesting to follow these results up “through the
system”, i.e. to examine the gender mix involved in all severity crashes, the
most serious crashes, in hospital presentations and in fatalities.
5.2 Bike types
It is a pity that there is such a large degree of granularity in the recording
of the size of collision involved powered two wheelersin the STATs 19 data.
Nevertheless, it is useful to consider the data that are presented.
Figure 5.2 shows a clear trend for big bike riders to increase in age, fig-
ure 5.3 confirms that this clearly affects male riders. In other words, over
time, even with the crude granularity of recorded bike sizes, we see an increase
in bike size over time.
48
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Figure 5.2: Age distribution of motorcyclists by bike type
49
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50
Number of reported bikers from originating wards
0
20
40
60
80
100
Figure 5.4: Ward by ward origin of riders involved in reported road accidents
within Devon
5.3 Origin of the riders
It may be easier to examine these data as a table:
Table 5.3 denotes the number of casualties originating from each of the
authorities in the South West Peninsula.
LA Ward Count
185 DEVON COUNTY Seaton Ward 17
186 DEVON COUNTY Honiton St. Paul’s Ward 16
187 DEVON COUNTY Honiton St. Michael’s Ward 24
188 DEVON COUNTY Ottery St. Mary Town Ward 25
189 DEVON COUNTY Exmouth Brixington Ward 35
51
190 DEVON COUNTY Exmouth Withycombe Raleigh Ward 60
191 DEVON COUNTY Exmouth Halsdon Ward 29
192 DEVON COUNTY Bishopsteignton Ward 12
193 DEVON COUNTY Bushell Ward 28
194 DEVON COUNTY College Ward 30
195 DEVON COUNTY Ipplepen Ward 12
196 DEVON COUNTY Marldon Ward 14
197 TORBAY (B) Blatchcombe Ward 92
198 TORBAY (B) Goodrington-with-Roselands Ward 39
199 TORBAY (B) Shiphay-with-the-Willows Ward 59
200 TORBAY (B) Preston Ward 56
201 DEVON COUNTY Dartmouth and Kingswear Ward 17
202 DEVON COUNTY Dartmouth and Kingswear Ward 17
203 TORBAY (B) St Mary’s-with-Summercombe Ward 66
204 DEVON COUNTY Kingsbridge North Ward 11
205 DEVON COUNTY Dartmouth Townstal Ward 17
206 DEVON COUNTY Totnes Bridgetown Ward 22
207 DEVON COUNTY Totnes Town Ward 17
208 DEVON COUNTY Ivybridge Filham Ward 29
209 DEVON COUNTY Avon and Harbourne Ward 11
210 DEVON COUNTY Kingsteignton West Ward 38
211 DEVON COUNTY Chudleigh Ward 37
212 DEVON COUNTY Whipton & Barton Ward 48
213 DEVON COUNTY Mincinglake Ward 33
214 DEVON COUNTY Pennsylvania Ward 23
215 DEVON COUNTY Polsloe Ward 26
216 DEVON COUNTY Pinhoe Ward 35
217 DEVON COUNTY Heavitree Ward 20
218 DEVON COUNTY St. Thomas Ward 47
219 DEVON COUNTY Cowick Ward 23
220 DEVON COUNTY Exwick Ward 57
52
221 DEVON COUNTY St. David’s Ward 18
222 DEVON COUNTY Alphington Ward 52
223 DEVON COUNTY St. Leonard’s Ward 17
224 DEVON COUNTY St. Loyes Ward 39
225 DEVON COUNTY Castle Ward 11
226 DEVON COUNTY Exe Valley Ward 13
227 DEVON COUNTY Bradley Ward 34
228 DEVON COUNTY Boniface Ward 14
229 DEVON COUNTY Bovey Ward 35
230 DEVON COUNTY Haytor Ward 11
231 DEVON COUNTY Eastmoor Ward 12
232 DEVON COUNTY Cornwood and Sparkwell Ward 20
233 DEVON COUNTY Lydford Ward 11
234 DEVON COUNTY Tamarside Ward 14
235 CITY OF PLYMOUTH (B) Plymstock Dunstone Ward 105
236 CITY OF PLYMOUTH (B) Ham Ward 98
237 CITY OF PLYMOUTH (B) Plympton St. Mary Ward 74
238 CITY OF PLYMOUTH (B) Plympton Erle Ward 45
239 DEVON COUNTY Yealmpton Ward 26
240 DEVON COUNTY Buckland Monachorum Ward 15
241 DEVON COUNTY Bickleigh and Shaugh Ward 26
242 CITY OF PLYMOUTH (B) Drake Ward 18
243 CITY OF PLYMOUTH (B) Stoke Ward 68
244 DEVON COUNTY Wembury and Brixton Ward 30
245 DEVON COUNTY Bere Ferrers Ward 18
246 DEVON COUNTY Walkham Ward 20
247 DEVON COUNTY Tavistock North Ward 20
248 DEVON COUNTY Tavistock South Ward 15
249 DEVON COUNTY Okehampton East Ward 15
250 DEVON COUNTY South Molton Ward 23
251 DEVON COUNTY Torrington Ward 22
53
252 DEVON COUNTY Forches and Whiddon Valley Ward 36
253 DEVON COUNTY Newport Ward 16
254 DEVON COUNTY Central Town Ward 25
255 DEVON COUNTY Fremington Ward 14
256 DEVON COUNTY Bideford North Ward 17
257 DEVON COUNTY Bratton Fleming Ward 11
258 DEVON COUNTY Ilfracombe West Ward 30
259 DEVON COUNTY Braunton East Ward 22
260 DEVON COUNTY Heanton Punchardon Ward 20
261 DEVON COUNTY Braunton West Ward 21
262 DEVON COUNTY Bideford South Ward 32
263 DEVON COUNTY Lowman Ward 15
264 CITY OF PLYMOUTH (B) St. Peter and the Waterfront Ward 66
265 CITY OF PLYMOUTH (B) St. Peter and the Waterfront Ward 66
266 DEVON COUNTY Wembury and Brixton Ward 30
270 CORNWALL COUNTY Torpoint West Ward 15
271 CORNWALL COUNTY Saltash Burraton Ward 13
272 CORNWALL COUNTY Saltash St. Stephens Ward 13
319 DEVON COUNTY Ivybridge Woodlands Ward 29
320 DEVON COUNTY Westville and Alvington Ward 12
321 DEVON COUNTY St. James Ward 19
322 DEVON COUNTY Newtown Ward 27
324 DEVON COUNTY Landkey, Swimbridge and Taw Ward 38
325 DEVON COUNTY Marwood Ward 13
326 DEVON COUNTY Combe Martin Ward 24
327 DEVON COUNTY Georgeham and Mortehoe Ward 16
328 DEVON COUNTY Bickington and Roundswell Ward 18
329 DEVON COUNTY Yeo Valley Ward 20
330 DEVON COUNTY Longbridge Ward 26
332 DEVON COUNTY Ashburton and Buckfastleigh Ward 46
333 DEVON COUNTY Ambrook Ward 22
54
334 DEVON COUNTY Kingsteignton East Ward 44
335 DEVON COUNTY Kenton with Starcross Ward 20
336 DEVON COUNTY Kenn Valley Ward 29
337 DEVON COUNTY Shaldon and Stokeinteignhead Ward 13
338 DEVON COUNTY Dawlish South West Ward 17
339 DEVON COUNTY Dawlish Central and North East Ward 39
340 DEVON COUNTY Teignmouth Central Ward 26
341 DEVON COUNTY Teignmouth West Ward 27
342 DEVON COUNTY Teignmouth East Ward 19
343 DEVON COUNTY Ottery St. Mary Rural Ward 16
344 DEVON COUNTY Woodbury and Lympstone Ward 19
345 DEVON COUNTY Raleigh Ward 12
346 DEVON COUNTY Broadclyst Ward 33
347 DEVON COUNTY Coly Valley Ward 11
348 DEVON COUNTY Newbridges Ward 15
349 DEVON COUNTY Exmouth Littleham Ward 26
350 DEVON COUNTY Budleigh Ward 18
351 DEVON COUNTY Sidmouth Sidford Ward 32
352 DEVON COUNTY Exmouth Town Ward 30
353 DEVON COUNTY Northam Ward 35
370 TORBAY (B) Wellswood Ward 35
371 TORBAY (B) Churston-with-Galmpton Ward 21
372 TORBAY (B) Cockington-with-Chelston Ward 73
373 TORBAY (B) St. Marychurch Ward 76
374 TORBAY (B) Clifton-with-Maidenway Ward 55
375 TORBAY (B) Roundham-with-Hyde Ward 44
376 TORBAY (B) Tormohun Ward 94
377 TORBAY (B) Watcombe Ward 49
378 TORBAY (B) Berry Head-with-Furzeham Ward 65
379 TORBAY (B) Ellacombe Ward 50
380 DEVON COUNTY Canonsleigh Ward 12
55
381 DEVON COUNTY Cullompton Outer Ward 18
382 DEVON COUNTY Lower Culm Ward 21
383 DEVON COUNTY Westexe Ward 16
384 DEVON COUNTY Upper Culm Ward 11
385 DEVON COUNTY Cranmore Ward 16
386 DEVON COUNTY Cullompton North Ward 14
387 DEVON COUNTY Cullompton South Ward 22
401 CITY OF PLYMOUTH (B) St. Peter and the Waterfront Ward 66
402 CITY OF PLYMOUTH (B) Devonport Ward 73
403 CITY OF PLYMOUTH (B) Efford and Lipson Ward 108
404 CITY OF PLYMOUTH (B) St. Budeaux Ward 97
405 CITY OF PLYMOUTH (B) Peverell Ward 52
406 CITY OF PLYMOUTH (B) Compton Ward 100
407 CITY OF PLYMOUTH (B) Plympton Chaddlewood Ward 72
464 DEVON COUNTY Priory Ward 70
465 DEVON COUNTY Buckland and Milber Ward 57
469 DEVON COUNTY Kerswell-with-Combe Ward 42
471 DEVON COUNTY Yeo Ward 13
472 DEVON COUNTY Sandford and Creedy Ward 12
473 DEVON COUNTY Teignbridge North Ward 17
474 DEVON COUNTY Newbrooke Ward 13
475 DEVON COUNTY Bideford East Ward 24
476 DEVON COUNTY Erme Valley Ward 23
477 CITY OF PLYMOUTH (B) Plymstock Radford Ward 106
478 CITY OF PLYMOUTH (B) Eggbuckland Ward 89
479 CITY OF PLYMOUTH (B) Moor View Ward 88
480 CITY OF PLYMOUTH (B) Honicknowle Ward 106
481 CITY OF PLYMOUTH (B) Budshead Ward 109
482 CITY OF PLYMOUTH (B) Southway Ward 105
483 CITY OF PLYMOUTH (B) Sutton and Mount Gould Ward 85
56
Table 5.6: Wards producing more than 10 of Devon’s
motorcycle casualties
Table 5.6 gives information on the origins of riders involved in crashes
on Devon’s roads corresponding to the data mapped in figure wherefromall.
Using data derived from Standard Table 119 of the 2001 census (Source:
2001 Census: Standard Area Statistics (England and Wales)”. ) it is pos-
sible to standardise these counts relative to the population and number of
motorcycling commuters in each ward.
It is however possible to consider these data as a rate. Figure 5.5 gives re-
sults on a per capita basis for each ward, which still highlights the importance
of Plymouth in terms of supplying motorcycle casualties, but does highlight
a number of rather interesting wards around South Devon. Figure 5.6 uses
a proxy variable derived from the 2001 census, namely the number of people
reporting that they used a motorbike to commute to work. This is doubtless
a particularly simplistic proxy measure, but it attempts to deal with the pos-
sibility that some areas may see higher levels of motorbike usage than others
(for example urban commuting areas).
5.4 Analysis of accident types
Table ?? indicates that a considerable number of motorcycle crashes are
single vehicle collisions, namely 36% of all. This is similar to the number of
crashes involving one other vehicle.
5.5 Manoeuvres
Folk wisdom in the motorcyling world suggesting that it the kind of line
taken when riding corners that causes crashes. Figure 5.7 suggests that it is
going straight ahead that is the most common manouvre during a crash.
57
Per capita involvement in reported bike crashes by ward
0.000
0.005
0.010
0.015
0.020
0.025
0.030
Figure 5.5: Ward by ward origin of bikers involved in road accidents in Devon
expressed as a per capita rate relative to the population of each ward
58
Involvement in reported bike crashes by ward using commute to work by motorcycle as a proxy exposure measure
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Figure 5.6: Ward by ward origin of riders involved in reported road acci-
dents within Devon expressed as a rate relative to the number of reported
motorcycle commuters within each ward
59
Freq
Unknown
Reversing
Parked
Waiting to go − held up or on main road
Slowing or stopping
Moving off
U−turn
Turning left
Waiting to turn left
Turning right
Waiting to turn right
Changing lane to left
Changing lane to right
Overtaking moving veh. − offside
Overtaking static veh. − offside
Overtaking − nearside
Going ahead left−hand bend
Going ahead right−hand bend
Going ahead other
0 500 1000 1500 2000
Figure 5.7: Reported manoeuvres
60
x
CITY OF PLYMOUTH (B) 1796
CORNWALL COUNTY 180
DEVON COUNTY 3403
DORSET COUNTY 0
SOMERSET COUNTY 40
TORBAY (B) 874
0 1 2 3 4 5 6 7
Count 4296 4278 3160 110 11 2 1 1
Percent of total 36 36 27 1 0 0 0 0
Table 5.7: Number and percentage of ”other” vehicles involved in motorcycle
crashes
Based on the police text descriptions, an attempt has been made by North
Devon Road Safety Office Mike Newcombe to classify the manouvre respon-
sible for the accident. Basically, all the fatal and serious crashes involving a
motorcycle in the three years between 2004 and 2006 were subject to detailed
analysis, and a random sample comprising one tenth of the slight crashes in-
volving a motorcycle were analysed. The following section reports the results
of an analysis of data collated on this basis. Firstly, figure 5.8 gives some
idea of the accident types identified, and their relative frequency.
The aim of a “Pareto” diagram is to try and depict the “80:20” rule. The
bar chart is arranged in order of how commonly each accident pattern was
recorded, and the actual number of occurences is recorded on the right hand
axis. A single curve, depicting the cumulative percentage of all accidents
explained by reading from left to right is superimposed. It is possible to see
how many of the causation patterns explain 80% of accidents. In systems with
less human factors involved, most effort would be directed towards dealing
with these 80% of accidents. However, given that human systems are more
61
Loss
of c
ontr
ol a
nd le
ft ro
adV
ehic
le e
mer
ging
from
sid
e ro
adM
otor
bike
faile
d to
sto
p fo
r ve
hicl
e in
fron
tLo
ss o
f con
trol
hit
othe
r ve
hicl
eM
otor
bike
cut
up
by v
ehic
le tu
rnin
g rig
htC
arel
ess
over
taki
ng b
y m
otor
bike
Hea
d on
cut
ting
corn
erP
edes
tria
n in
hig
hway
Veh
icle
faile
d to
sto
p fo
r m
otor
cycl
e in
fron
tS
lippe
ry r
oad
surf
ace
Kno
ck fo
r a
knoc
k he
ad o
nO
vert
aken
veh
icle
turn
ed in
to p
ath
Car
eles
s ov
erta
king
by
othe
r ve
hicl
eV
ehic
le fa
iled
to g
ive
way
on
roun
dabo
utLa
ne c
hang
e / P
oor
lane
dis
cipl
ine
of o
ther
veh
icle
Ani
mal
in h
ighw
ayU
nkno
wn
Mot
orbi
ke e
mer
ging
from
a s
ide
road
Mot
orbi
ke h
it by
veh
icle
is w
as o
vert
akin
gU
turn
Rev
ersi
ng v
ehic
leO
ther
veh
icle
loss
con
trol
hit
mot
orbi
keH
it st
atio
nary
obj
ect
Hea
d on
whi
lst o
vert
akin
gC
lippe
d by
veh
icle
trav
ellin
g op
posi
teR
ider
dis
trac
ted
Pun
ctur
ed ty
reP
arke
d ve
hicl
e pu
lls o
ut in
to p
ath
Mot
orbi
ke fa
iled
to g
ive
way
on
roun
dabo
utM
otor
cycl
e ra
n re
d lig
htH
ead
on b
y ot
her
vehi
cle
cutti
ng c
orne
rP
oor
lane
dis
cipl
ine
of m
otor
bike
Oth
er v
ehic
le r
an r
ed li
ght
Oth
er
Hea
d on
whi
lst o
ther
veh
icle
ove
rtak
ing
Num
ber
of A
ccid
ents
020
4060
8010
012
0
020
4060
8010
0
Cum
ulat
ive
Per
cent
age
Figure 5.8: Reported Motorcycle Accidents 2003 - 2006; Classified accident
patterns
62
complex, countermeasures may have to be applied where they can be devised,
but this chart may still be useful in suggesting the likely impact of any
countermeasures.
Types
Loss of control and left road 103
Vehicle emerging from side road 94
Motorbike failed to stop for vehicle in front 58
Loss of control hit other vehicle 33
Motorbike cut up by vehicle turning right 28
Careless overtaking by motorbike 26
Head on cutting corner 21
Pedestrian in highway 20
Vehicle failed to stop for motorcycle in front 19
Slippery road surface 17
Knock for a knock head on 16
Overtaken vehicle turned into path 15
Careless overtaking by other vehicle 15
Table 5.8: Crash ”types” causing four fifths of examined motorcycle crashes
Table 5.8 highlights the crash types which account for 80% of all motor-
cycle crashes. Nevertheless, it is worth highlighting that the following five
crash types:
� Loss of control and left road
� Vehicle emerging from side road
� Motorbike failed to stop for vehicle in front
� Loss of control hit other vehicle
� Motorbike cut up by vehicle turning right
63
account for 54% of all motorcycle crashes.
Before considering these in detail, it may be worth looking at the rela-
tionship (if any) between the crash types and the road/weather conditions
at the time of the reported crash.
Missing Daylight Dark: lit Dark: unlit
Animal in highway 0 9 0 4
Careless overtaking by motorbike 0 23 1 2
Careless overtaking by other vehicle 0 10 1 4
Clipped by vehicle travelling opposite 0 3 0 1
Head on by other vehicle cutting corner 0 2 0 0
Head on cutting corner 0 18 1 2
Head on whilst other vehicle overtaking 0 1 0 0
Head on whilst overtaking 0 4 0 0
Hit stationary object 0 4 0 0
Knock for a knock head on 0 15 0 1
Lane change / Poor lane discipline of other
vehicle
0 11 3 0
Loss of control and left road 0 78 12 13
Loss of control hit other vehicle 0 27 2 4
Motorbike cut up by vehicle turning right 0 23 5 0
Motorbike emerging from a side road 0 9 2 0
Motorbike failed to give way on round-
about
0 2 1 0
Motorbike failed to stop for vehicle in front 0 45 7 6
Motorbike hit by vehicle is was overtaking 0 7 1 1
Motorcycle ran red light 0 2 0 0
Other 0 0 1 0
Other vehicle loss control hit motorbike 0 4 0 0
Other vehicle ran red light 0 0 1 0
Overtaken vehicle turned into path 0 12 2 1
Parked vehicle pulls out into path 0 2 0 1
64
Pedestrian in highway 0 14 6 0
Poor lane discipline of motorbike 0 0 0 1
Punctured tyre 0 2 0 1
Reversing vehicle 0 6 0 0
Rider distracted 0 2 1 0
Slippery road surface 2 10 2 3
Unknown 0 6 3 3
U turn 0 5 3 0
Vehicle emerging from side road 0 80 8 6
Vehicle failed to give way on roundabout 0 11 3 0
Vehicle failed to stop for motorcycle in
front
0 15 0 4
Fatal Serious Slight
Rider 44 241 289
Passenger 3 11 21
Pedestrian 1 5 15
Table 5.10: Casualty and class breakdown for typed crashes
This provides some information. Note that the way STATs 19 are con-
figured means we get information on squashed pedestrians attached to the
vehicle which squashed them, these have therefore been removed for futher
analysis.
65
Loss
of c
ontr
ol a
nd le
ft ro
adV
ehic
le e
mer
ging
from
sid
e ro
adC
arel
ess
over
taki
ng b
y m
otor
bike
Mot
orbi
ke c
ut u
p by
veh
icle
turn
ing
right
Hea
d on
cut
ting
corn
erM
otor
bike
faile
d to
sto
p fo
r ve
hicl
e in
fron
tLo
ss o
f con
trol
hit
othe
r ve
hicl
eC
arel
ess
over
taki
ng b
y ot
her
vehi
cle
Unk
now
nK
nock
for
a kn
ock
head
on
Veh
icle
faile
d to
giv
e w
ay o
n ro
unda
bout
Slip
pery
roa
d su
rfac
eM
otor
bike
em
ergi
ng fr
om a
sid
e ro
adA
nim
al in
hig
hway
Rev
ersi
ng v
ehic
leM
otor
bike
hit
by v
ehic
le is
was
ove
rtak
ing
Veh
icle
faile
d to
sto
p fo
r m
otor
cycl
e in
fron
tO
vert
aken
veh
icle
turn
ed in
to p
ath
Hea
d on
whi
lst o
vert
akin
gU
turn
Ped
estr
ian
in h
ighw
ayH
it st
atio
nary
obj
ect
Rid
er d
istr
acte
dO
ther
veh
icle
loss
con
trol
hit
mot
orbi
keM
otor
bike
faile
d to
giv
e w
ay o
n ro
unda
bout
Hea
d on
by
othe
r ve
hicl
e cu
tting
cor
ner
Clip
ped
by v
ehic
le tr
avel
ling
oppo
site
Pun
ctur
ed ty
reP
oor
lane
dis
cipl
ine
of m
otor
bike
Par
ked
vehi
cle
pulls
out
into
pat
hO
ther
veh
icle
ran
red
ligh
tO
ther
M
otor
cycl
e ra
n re
d lig
htLa
ne c
hang
e / P
oor
lane
dis
cipl
ine
of o
ther
veh
icle
Hea
d on
whi
lst o
ther
veh
icle
ove
rtak
ing
Num
ber
of A
ccid
ents
020
4060
80
020
4060
8010
0
Cum
ulat
ive
Per
cent
age
Types
Loss of control and left road 81
Vehicle emerging from side road 43
Careless overtaking by motorbike 21
Motorbike cut up by vehicle turning right 18
Head on cutting corner 18
Motorbike failed to stop for vehicle in front 16
Loss of control hit other vehicle 12
Careless overtaking by other vehicle 11
Unknown 8
Knock for a knock head on 8
Table 5.11: Crash ”types” causing four fifths of examined motorcycle crashes
66
Table 5.11 highlights the crash types which account for 80% of all motor-
cycle crashes. Nevertheless, it is worth highlighting that the following five
crash types:
� Loss of control and left road
� Vehicle emerging from side road
� Careless overtaking by motorbike
� Motorbike cut up by vehicle turning right
� Head on cutting corner
account for 61% of all fatal or serious motorcycle crashes.
Veh
icle
em
ergi
ng fr
om s
ide
road
Mot
orbi
ke fa
iled
to s
top
for
vehi
cle
in fr
ont
Loss
of c
ontr
ol a
nd le
ft ro
adLo
ss o
f con
trol
hit
othe
r ve
hicl
eV
ehic
le fa
iled
to s
top
for
mot
orcy
cle
in fr
ont
Ove
rtak
en v
ehic
le tu
rned
into
pat
hLa
ne c
hang
e / P
oor
lane
dis
cipl
ine
of o
ther
veh
icle
Slip
pery
roa
d su
rfac
eM
otor
bike
cut
up
by v
ehic
le tu
rnin
g rig
htK
nock
for
a kn
ock
head
on
Car
eles
s ov
erta
king
by
mot
orbi
keA
nim
al in
hig
hway
Ped
estr
ian
in h
ighw
ayV
ehic
le fa
iled
to g
ive
way
on
roun
dabo
utC
arel
ess
over
taki
ng b
y ot
her
vehi
cle
U tu
rnU
nkno
wn
Hea
d on
cut
ting
corn
erP
unct
ured
tyre
Mot
orbi
ke h
it by
veh
icle
is w
as o
vert
akin
gM
otor
bike
em
ergi
ng fr
om a
sid
e ro
adP
arke
d ve
hicl
e pu
lls o
ut in
to p
ath
Oth
er v
ehic
le lo
ss c
ontr
ol h
it m
otor
bike
Clip
ped
by v
ehic
le tr
avel
ling
oppo
site
Rid
er d
istr
acte
dM
otor
bike
faile
d to
giv
e w
ay o
n ro
unda
bout
Rev
ersi
ng v
ehic
leP
oor
lane
dis
cipl
ine
of m
otor
bike
Oth
er v
ehic
le r
an r
ed li
ght
Oth
er
Mot
orcy
cle
ran
red
light
Hit
stat
iona
ry o
bjec
tH
ead
on w
hils
t oth
er v
ehic
le o
vert
akin
gH
ead
on b
y ot
her
vehi
cle
cutti
ng c
orne
rH
ead
on w
hils
t ove
rtak
ing
Num
ber
of A
ccid
ents
010
2030
4050
60
020
4060
8010
0
Cum
ulat
ive
Per
cent
age
Table ?? highlights the crash types which account for 80% of all motor-
cycle crashes. Nevertheless, it is worth highlighting that the following five
crash types:
67
Types
Vehicle emerging from side road 55
Motorbike failed to stop for vehicle in front 41
Loss of control and left road 32
Loss of control hit other vehicle 22
Vehicle failed to stop for motorcycle in front 16
Overtaken vehicle turned into path 14
Lane change / Poor lane discipline of other vehicle 13
Slippery road surface 11
Motorbike cut up by vehicle turning right 11
Knock for a knock head on 10
Careless overtaking by motorbike 10
Animal in highway 8
Table 5.12: Crash ”types” causing four fifths of examined motorcycle crashes
� Vehicle emerging from side road
� Motorbike failed to stop for vehicle in front
� Loss of control and left road
� Loss of control hit other vehicle
� Vehicle failed to stop for motorcycle in front
account for 54% of all slight motorcycle crashes.
5.5.1 Comments
It is very clear that the crash types resulting in fatal injury are very different
from those resulting in a slight injury, most notably the dominant crash type
for fatal and serious casualties was Loss of control and left road whereas the
dominant crash type for slight crashes was Vehicle emerging from side road.
68
The implication is that the fatal and serious crashes are dominated by a
crash type which is more fully within the control of the rider, wherease the
relatively more of the slight crashes are associated with actions of the other
road user. The balance of defensive riding (i.e. gaining the experience to
avoid being hit by other road users) and encouraging other road users not to
hit motorcyclists is clearly a question that has to be answered when designing
interventions.
5.6 Appendix
For reference, the accidents identified using regular expressions which have
the phrases oil or diesel (or variants thereof such as deisel) are included below.
NULL
69
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Acknowledgements
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the Controller of HMSO and the Queen’s Printer for Scotland.”
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