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

●●

●

●

●

●

●

●

●

●

●

●

●

●

●● ●● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●

●

●

●●

●

●

●

●

●

●

● ●●

●

●

●

●●●

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

Year

Age

of r

ider

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10111213141516

●● ●

●

●

●

●

● ● ● ● ● ● ● ● ●

●●●

●

●

●

●

●●

●

●

●

●

●●

●

●

●●

●

●

●●●

●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●●

●

●●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●●

●

●●

●

●

●●

●●

●●

●

●

●

●●

●

●

●

●

●

●

●

●

●

Moped

1 2 3 4 5 6 7 8 9 10111213141516

● ● ●

●

●

●

●

● ● ● ● ● ● ● ● ●

●●

●

●

●

●

●

●

●

●

●●

●

●

●●

●

●●

●●

●●

●●●

●

●

●

●

● ●

●

●

●

●

●●

●

●

●

●●

●

●

●●

●

●

●●

●●

●

●

●

●

●

●●

●

●

●

●

●

●●●

●

●

●

●

●

●

●●●●●●●

●

●

●

●

●

●●●●

●

●●

●

●

●

●

●

●●

●

●

●

●

●

●

●●●

●

●

●

●●●

●

●●

●

●

●

●

●

●●

●

●

●●

●

●

●

●●

●●

●●

●●

●●●

●

●

●

●

● ●

●

●

●●

●●●

●

●●

●

●●●

●

●

●●

●●●

●

●

●●●

●

●

●

●

●●●

●●●

●

●

●●

●

●●

●

●

●

●

●

●

●

●

●

●●

●

●

●●

●●

●

●

●

●

● ●

●●

●

●

●

●

●●●●

●

●

●●●

●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●

●●●

●

●

●●●

●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●●●●

Under125

20

40

60

80

100

● ● ● ● ● ● ● ●● ● ●

● ● ● ●●

●

●●

●

●

●

●

●

●

●●

●●●

●

●●

●

●●●●●

●●●

●●

●

●

●●●●●●●

●●

●

●

●●

●

●

●

●

●

●●●

●●●●

●

●●●

●

●

●

●●●

●●

●

●●

●

●

●

●●

●

●

●

●●●

●

●

●

●●

●●

●

●

●

●●●●

●

●

●

●

●●●

●

●●

●●●●

●

●●

●

●

●

●

●

●●

●

●

●●●●

●

●●●●

●

●

●

●●●●●●

●

●

●

●●●●

●

●

●

●●

●

●

●●

●●●

●●●

●●●●

●●

●●●●

●

●●●

●

●

●

●

●●

●

●

●●

●

●

Over 125

Figure 5.2: Age distribution of motorcyclists by bike type

49

Year

Age

of r

ider

20

40

60

80

100

12345678910111213141516

●●

●●●

●

●●

●●

●

●●

●●

●

●●

●●●

●

●

●

●

●

●

●

●

●●

MopedFEMALE

12345678910111213141516

●

●

●●

●●

●●

●●●●●●●

●

●

●●

●

●

●

●

Under125FEMALE

12345678910111213141516

●●●●

●●●●

●●●●●●●

●

●

●

●

●

●

●● ● ●

●

●

●

●

●

●●

Over 125FEMALE

●●●

●●●

●

●●●●●●●●●

●●

●● ●

●

●

●

●●

●

●

●

●

●

●

●

●

●●●●

●

●

●

●

●

●●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●●●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●●

●●●●

●

●●

●●

●●

●

●●

●

●

●

●

●

●

●

●

●

MopedMALE

●●●

●

●

●

●

●●●●●●●●●

●

●●

●

●

●

●

● ●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●●

●

●●●●

● ●

●

●

●

●

●

●

●

●

●●●

●

●●

●

●

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●

●●●●

●

●

●

●

●

●

●

●

●●

●

●

●

●●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●●●

●●

●

●

●

●

●

●●

●

●

●●

●

●

●

●

●

●●

●

●●

●●

●●●

●

●

●

●

●

●●

●●●

●●

●

●●●

●

●

●●

●

●

●

●●●

●

●

●●●

●●●

●●●

●●

●

●

●

●

●●

●

●

●●

●

●●

●●

●

●

●

●

● ●●

●

●

●

●

●

●●●

●

●

●●

●●

●

●

●

●

●

●

●

●●

●

●●

●

●

●

●●●

●

●

●●●

●●●●

●●

●

●●

●

●

●●

●

●

●

●

●

●

●●●

Under125MALE

20

40

60

80

100

●●●●●●●●

●●●●●●●●

●

●

●

●

●

●

●

●

●●

●●●

●

●●●●●●

●●●● ●●

●●

●●●●●●●●

●

●

●●

●●

●●●

●

●

●

●

●

●●●●

●●●

●

●●●●●

●

●

●

●

●

●●

●

●

●

●

●●●

●●

●

●

●●●

●

●

●

●●●●●●

●

●

●

●

●●

●

●●

●

●●●

●

●●●●

●●

●

●

●●

●

●●●

●●●●●

●●●

●

●

●

●●

●

●

●●

● ●●●●

●●●●●

●●●●

●

●●

●●●●

●

●●

●

●

Over 125MALE

Figure 5.3: Age distribution of motorcyclists by bike type

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

Bibliography

Ankarath, S., P. Giannoudis, I. Barlow, M. Bellamy, S. Matthews, and

R. Smith (2002). Injury patterns associated with mortality following

motorcycle crashes. INJURY-INTERNATIONAL JOURNAL OF THE

CARE OF THE INJURED 33 (6), 473 – 477.

Arzemanian, S., M. Salatka, C. Anderson, and J. Kraus (1993). Geographic-

distribution of fatal motorcycle crashes, by design type, in los-angeles,

orange, and san-diego counties from 1983-1985. JOURNAL OF SAFETY

RESEARCH 24 (2), 87 – 95.

Begg, D., J. Langley, and D. Chalmers (1992). Motor-vehicle road crashes

during the 14th and 15th years of life. NEW ZEALAND MEDICAL JOUR-

NAL 105 (932), 150 – 151.

Bolhofner, B., B. Carmen, S. Donohue, and K. Harlen (1994). Motorcycle

accident injury severity, blood-alcohol levels, insurance status, and hospi-

tal costs - a 4-year study in st-petersburg, florida. JOURNAL OF OR-

THOPAEDIC TRAUMA 8 (3), 228 – 232.

Braddock, M., R. Schwartz, G. Lapidus, L. Banco, and L. Jacobs (1992).

A population-based study of motorcycle injury and costs. ANNALS OF

EMERGENCY MEDICINE 21 (3), 273 – 278.

Bueno, M., N. Redeker, and E. Norman (1992). Analysis of motor-vehicle

crash data in an urban trauma center - implications for nursing practice

and research. HEART & LUNG 21 (6), 558 – 567.

70

Chang, L. and H. Wang (2006). Analysis of traffic injury severity: An applica-

tion of non-parametric classification tree techniques. ACCIDENT ANAL-

YSIS AND PREVENTION 38 (5), 1019 – 1027.

Chesham, D., D. Rutter, and L. Quine (1993). Motorcycling safety research

- a review of the social and behavioral literature. SOCIAL SCIENCE &

MEDICINE 37 (3), 419 – 429.

Colburn, N. and R. Meyer (2003). Sports injury or trauma? injuries of the

competition off-road motorcyclist. INJURY-INTERNATIONAL JOUR-

NAL OF THE CARE OF THE INJURED 34 (3), 207 – 214.

de lapparent, M. (2006). Empirical bayesian analysis of accident severity for

motorcyclists in large french urban areas. ACCIDENT ANALYSIS AND

PREVENTION 38 (2), 260 – 268.

Everett, S., R. Shults, L. Barrios, J. Sacks, R. Lowry, and J. Oeltmann (2001).

Trends and subgroup differences in transportation-related injury risk and

safety behaviors among high school students, 1991-1997. JOURNAL OF

ADOLESCENT HEALTH 28 (3), 228 – 234.

Gobbi, A., B. Tuy, and I. Panuncialman (2004). The incidence of motocross

injuries: a 12-year investigation. KNEE SURGERY SPORTS TRAUMA-

TOLOGY ARTHROSCOPY 12 (6), 574 – 580.

Gorski, T., Y. Gorski, G. Mcleod, D. Suh, R. Cordero, F. Essien, D. Berry,

and F. Dada (2003). Patterns of injury and outcomes associated with

motocross accidents. AMERICAN SURGEON 69 (10), 895 – 898.

Greenlander, S. (1994). Modelling risk ratios from matched cohort data - an

estimating equation approach. JRSSC 43 (1), 223 – 232.

Haasper, C., D. Otte, K. Knobloch, J. Zeichen, C. Krettek, and M. Richter

(2006). Knee injuries of vulnerable road users in road traffic. UN-

FALLCHIRURG 109 (12), 1025 – 1031.

71

Hinds, J., G. Allen, and C. Morris (2007). Trauma and motorcyclists; born

to be wild, bound to be injured? INJURY-INTERNATIONAL JOURNAL

OF THE CARE OF THE INJURED 38 (10), 1131 – 1138.

Holubowycz, O., C. Kloeden, and A. Mclean (1994). Age, sex, and blood-

alcohol concentration of killed and injured drivers, riders, and passengers.

ACCIDENT ANALYSIS AND PREVENTION 26 (4), 483 – 492.

Horswill, M. and S. Helman (2001). Comparative approaches to differential

accident liability. Technical report, University of Reading.

Horswill, M. and S. Helman (2003). A behavioral comparison between mo-

torcyclists and a matched group of non-motorcycling car drivers: Factors

influencing accident risk. Accident Analysis and Prevention 35, 589–597.

Horswill, M. S., S. Helman, P. Ardiles, and J. P. Wann (2005). Motorcycle

accident risk could be inflated by a time to arrival illusion. Optometry and

Vision Science 82, 740–746.

Horvath, M., F. Rakotoalizaorabenjamina, E. Massade, and M. Treboux

(1993). Traumatic bilateral testicular dislocation. ANNALES DE

CHIRURGIE 47 (1), 71 – 72.

Hurt Jr., H., J. Oullet, and D. Thom (1981). Motorcycle accident cause fac-

tors and identification of countermeasures. Technical Report DOT HS 805

862, National Highway Traffic Safety Administration, Washington D.C.

Jeffers, R., H. Tan, C. Nicolopoulos, R. Kamath, and P. Giannoudis (2004).

Prevalence and patterns of foot injuries following motorcycle trauma.

JOURNAL OF ORTHOPAEDIC TRAUMA 18 (2), 87 – 91.

Kim, K., J. Boski, and E. Yamashita (2002). Typology of motorcycle crashes

- rider characteristics, environmental factors, and spatial patterns. HIGH-

WAY SAFETY: WORK ZONES, LAW ENFORCEMENT, MOTORCY-

CLES, TRUCKS, OLDER DRIVERS, AND PEDESTRIANS (1818), 47

– 53.

72

Ko, S., S. Ng, Y. Wan, C. Huang, T. Lee, C. Kung, and P. Liu (2004). Testicu-

lar dislocation: An uncommon and easily overlooked complication of blunt

abdominal trauma. ANNALS OF EMERGENCY MEDICINE 43 (3), 371

– 375.

Kraus, J., C. Anderson, P. Zador, A. Williams, S. Arzemanian, W. Li, and

M. Salatka (1991). Motorcycle licensure, ownership, and injury crash in-

volvement. AMERICAN JOURNAL OF PUBLIC HEALTH 81 (2), 172 –

176.

Langley, J., D. Begg, and A. Reeder (1994). Motorcycle crashes resulting

in death and hospitalization .2. traffic crashes. ACCIDENT ANALYSIS

AND PREVENTION 26 (2), 165 – 171.

Langley, J., B. Mullin, R. Jackson, and R. Norton (2000). Motorcycle en-

gine size and risk of moderate to fatal injury from a motorcycle crash.

ACCIDENT ANALYSIS AND PREVENTION 32 (5), 659 – 663.

Lapidus, G., M. Braddock, R. Schwartz, L. Banco, and L. Jacobs (1994).

Accuracy of fatal motorcycle-injury reporting on death certificates. AC-

CIDENT ANALYSIS AND PREVENTION 26 (4), 535 – 542.

Lin, M., S. Chang, W. Huang, H. Hwang, and L. Pai (2003). Factors as-

sociated with severity of motorcycle injuries among young adult riders.

ANNALS OF EMERGENCY MEDICINE 41 (6), 783 – 791.

Lin, M., S. Chang, L. Pai, and P. Keyl (2003). A longitudinal study of risk

factors for motorcycle crashes among junior college students in taiwan.

ACCIDENT ANALYSIS AND PREVENTION 35 (2), 243 – 252.

Lin, M., W. Huang, H. Hwang, H. Wu, and L. Yen (2004). The effect of

crash experience on changes in risk taking among urban and rural young

people. ACCIDENT ANALYSIS AND PREVENTION 36 (2), 213 – 222.

73

Lynam, D., J. Broughton, R. Minton, and R. Tunbridge (2001). An analysis

of police reports of fatal accidents involving motorcycles. Technical Report

TRL 492, Tranport Research Laboratory.

Mangus, R., C. Simons, L. Jacobson, E. Streib, and G. Gomez (2004). Cur-

rent helmet and protective equipment usage among previously injured atv

and motorcycle riders. INJURY PREVENTION 10 (1), 56 – 58.

Matthes, G., U. Schmucker, M. Schindel, S. Siebenhuhner, A. Ekkernkamp,

and J. Seifert (2007). Tree collisions in road traffic accidents - mechanism

and pattern of injury. ZENTRALBLATT FUR CHIRURGIE 132 (2), 142

– 145.

Matzavakis, L., C. Frangakis, A. Charalampopoulou, and E. Petridou (2005).

Burn injuries related to motorcycle exhaust pipes: a study in greece.

BURNS 31 (3), 372 – 374.

Mayou, R., B. Bryant, and R. Duthie (1993). Psychiatric consequences of

road traffic accidents. BRITISH MEDICAL JOURNAL 307 (6905), 647 –

651.

Middleman, A., A. Faulkner, E. Woods, S. Emans, and R. Durant (1995).

High-risk behaviors among high-school-students in massachusetts who use

anabolic-steroids. PEDIATRICS 96 (2), 268 – 272.

Mulhall, J., S. Gabram, and L. Jacobs (1995). Emergency management of

blunt testicular trauma. ACADEMIC EMERGENCY MEDICINE 2 (7),

639 – 643.

Mullin, B., R. Jackson, J. Langley, and R. Norton (2000). Increasing age and

experience: are both protective against motorcycle injury? a case-control

study. Injury Prevention 6, 32 – 35.

Nelson, R., P. Gustafson, and R. Szlabick (2006). Motorcycle collisions in-

volving white-tailed deer in central and northern wisconsin: A rural trauma

74

center experience. JOURNAL OF TRAUMA-INJURY INFECTION AND

CRITICAL CARE 60 (6), 1297 – 1300.

Oksuz, E. and S. Malhan (2005). Socioeconomic factors and health risk be-

haviors among university students in turkey: Questionnaire study. CROA-

TIAN MEDICAL JOURNAL 46 (1), 66 – 73.

Paulozzi, L. (2005). The role of sales of new motorcycles in a recent increase in

motorcycle mortality rates. JOURNAL OF SAFETY RESEARCH 36 (4),

361 – 364.

Peek, C., E. Braver, H. Shen, and J. Kraus (1994). Lower-extremity

injuries from motorcycle crashes - a common-cause of preventable in-

jury. JOURNAL OF TRAUMA-INJURY INFECTION AND CRITICAL

CARE 37 (3), 358 – 364.

Peekasa, C. and J. Kraus (1996a). Alcohol use, driver, and crash characteris-

tics among injured motorcycle drivers. JOURNAL OF TRAUMA-INJURY

INFECTION AND CRITICAL CARE 41 (6), 989 – 993.

Peekasa, C. and J. Kraus (1996b). Injuries sustained by motorcycle riders in

the approaching turn crash configuration. ACCIDENT ANALYSIS AND

PREVENTION 28 (5), 561 – 569.

Plasencia, A., C. Borrell, and J. Anto (1995). Emergency department and

hospital admissions and deaths from traffic injuries in barcelona, spain -

a one-year population-based study. ACCIDENT ANALYSIS AND PRE-

VENTION 27 (4), 591 – 600.

Pomerantz, W., M. Gittelman, and G. Smith (2005). No license required:

Severe pediatric motorbike-related injuries in ohio. PEDIATRICS 115 (3),

704 – 709.

Quddus, M., R. Noland, and H. Chin (2002). An analysis of motorcycle injury

and vehicle damage severity using ordered probit models. JOURNAL OF

SAFETY RESEARCH 33 (4), 445 – 462.

75

Reeder, A., D. Chalmers, and J. Langley (1996). Rider training, reasons for

riding, and the social context of riding among young on-road motorcyclists

in new zealand. AUSTRALIAN AND NEW ZEALAND JOURNAL OF

PUBLIC HEALTH 20 (4), 369 – 374.

Reeder, A., D. J. Chalmers, and J. D. Langley (1995). Young on-road motor-

cyclists in New Zealand: age of licensure, unlicensed riding, and motorcycle

borrowing. Injury Prevention 1 (2), 103 – 108.

Roberts, R., E. Kelson, D. Goodall-Wilson, and R. Kimble (2002). Motor-

cycle exhaust burns in children. BURNS 28 (4), 367 – 369.

Rutter, D., L. Q. L, and I. Albery (1998). Perceptions of risk in motorcyclists:

unrealistic optimism, relative realism and predictions of behaviour. British

Journal of Psychology 89, 681–696.

Savolainen, P. and F. Mannering (2007). Probabilistic models of motorcy-

clists’ injury severities in single- and multi-vehicle crashes. ACCIDENT

ANALYSIS AND PREVENTION 39 (5), 955 – 963.

Sinha, A., D. Boot, D. Gorman, and D. Teanby (1995). Severe motorcycle

injury in mersey region and north-wales. INJURY-INTERNATIONAL

JOURNAL OF THE CARE OF THE INJURED 26 (8), 543 – 545.

Spain, D., P. Boaz, D. Davidson, F. Miller, E. Carrillo, and J. Richard-

son (1997). Risk-taking behaviors among adolescent trauma pa-

tients. JOURNAL OF TRAUMA-INJURY INFECTION AND CRITI-

CAL CARE 43 (3), 423 – 426.

Sun, S., D. Kahn, and K. Swan (1998). Lowering the legal blood alcohol level

for motorcyclists. ACCIDENT ANALYSIS AND PREVENTION 30 (1),

133 – 136.

Suri, M., N. Naik, S. Raibagkar, and D. Mehta (2007). Heel flap injuries in

spoke wheel accidents. INJURY-INTERNATIONAL JOURNAL OF THE

CARE OF THE INJURED 38 (5), 619 – 624.

76

Swaddiwudhipong, W., C. Boonmak, P. Nguntra, and P. Mahasakpan (1998).

Effect of motorcycle rider education on changes in risk behaviours and

motorcycle-related injuries in rural thailand. TROPICAL MEDICINE &

INTERNATIONAL HEALTH 3 (10), 767 – 770.

Weiss, A. (1994). A bivariate ordered probit model with truncation - helmet

use and motorcycle injuries. JASA 87 (417), 48 – 56.

Wells, S., B. Mullin, R. Norton, J. Langley, J. Connor, R. Lay-Yee, and

R. Jackson (2004). Motorcycle rider conspicuity and crash related injury:

case-control study. BRITISH MEDICAL JOURNAL 328 (7444), 857 –

860A.

Wick, M., E. Muller, A. Ekkerkamp, and G. Muhr (1998). The motorcyclist,

easy rider or easy victim? an analysis of motorcycle accidents in germany.

Am. J. Emerg. Med. 16 (3), 320 – 323.

Woodward, A. (1983a). Motorcycle accidents in Nottinghamshire. Public

Health 97 (3), 139 – 148.

Woodward, A. (1983b). Time trends in motorcycle accidents in britain. J.

Epidemiol. Community Health 37 (1), 66 – 69.

Wyatt, J., J. O’donnell, D. Beard, and A. Busuttil (1999). Injury analyses of

fatal motorcycle collisions in south-east scotland. FORENSIC SCIENCE

INTERNATIONAL 104 (2-3), 127 – 132.

Yuan, W. (2000). The effectiveness of the ’ride-bright’ legislation for motor-

cycles in singapore. ACCIDENT ANALYSIS AND PREVENTION 32 (4),

559 – 563.

Zambon, F. and M. Hasselberg (2006). Socioeconomic differences and mo-

torcycle injuries: Age at risk and injury severity among young drivers -

a swedish nationwide cohort study. ACCIDENT ANALYSIS AND PRE-

VENTION 38 (6), 1183 – 1189.

77

Acknowledgements

”Census output is Crown copyright and is reproduced with the permission of

the Controller of HMSO and the Queen’s Printer for Scotland.”

Office for National Statistics, 2001 Census: Standard Area Statistics

(England and Wales) [computer file]. ESRC/JISC Census Programme, Cen-

sus Dissemination Unit, Mimas (University of Manchester)

78