In-depth Crash Causation Analysis of Motorcyclist Crashes

Tereza Tmejová

, Robert Zůvala

and Kateřina Bucsuházy

Transport Research Centre, Lisenska 33a, Brno, Czech Republic

Keywords: Crash, Crash Analysis, in-Depth, Motorcycle, Intersection, Human Failure.

Abstract: Motorcyclists as vulnerable road users are likely to be seriously injured during crashes. Realizing the need for

mitigating the serious consequences of motorcyclist crashes, this paper aims to investigate and identify the

factors contributing to the crash occurrence. The in-depth data used for the purpose of this study allows the

detailed analysis of contributory factors and the whole human functional failure chain leading to the crash as

well as the crash mechanism. Not only the failure of motorcyclists leading to the crash was analysed, but also

the failure of passenger vehicle drivers involved in a collision with a motorcyclist. To define the risk factors

of motorcycle-vehicle crashes, the obtained results focused on the motorcycle-vehicle crashes were compared

with the two passenger vehicle crashes. The most typical vehicle–motorcycle crash caused by vehicle driver

failure is right of way violation. While motorcyclists frequently fail at the diagnosis level (especially incorrect

evaluation of a road difficulty), vehicle drivers mostly fail at the detection level, especially in the intersections.

Obtained data highlighted the necessity of the educational and preventive activities focused differently on the

motorcyclist and vehicle drivers.

1 INTRODUCTION

Motorcyclists are with pedestrians and cyclists

among the most vulnerable road users. Motorcyclists

are around 16-20 times more likely involved in an

injury or fatal crash in comparison with passenger

vehicle drivers (Walton, 2012) and 25times more

likely to be fatally injured per million vehicle

kilometres than passenger vehicle drivers (ONISR,

2010). The higher injury risk is mainly caused by low

motorcyclist protection (compared to the vehicle

crew) and higher speeds in comparison with the other

vulnerable road users. (Obenski et al., 2011)

2 LITERATURE REVIEW

The most common causes of motorcycle crashes are

failure to give way, rider losing control (especially in

the curve) and overtaking (Clarke et al., 2004). Even

though the failure to give way belongs to the most

common causes of motorcycle crashes, only

approximately 20% of crashes are non-priority

https://orcid.org/0000- 0003-2090-6144

https://orcid.org/0000-0003-2038-7292

https://orcid.org/0000-0003-1247-6148

crashes caused by motorcycle failures (Clarke et al.,

2004; Clabaux et al., 2012; Pai, 2011). Motorcycle

non-priority crashes belong to the riskiest situations

for the motorcyclist, the crashes are often

characterised by serious consequences. (Clabaux et

al., 2012; Pai, 2011). These crash scenarios

predominantly involve a driver failing to detect the

presence of an oncoming motorcycle or failure in the

decision-making process (Clarke et al. 2004; MAIDS,

2004; Pai, 2011; Crundall, 2008).

The safe task performance depends on sensory

detection of all the relevant data (van Elslande). The

detection failures could occur if the driver overlooked

a motorcyclist approaching the intersection. For these

types of crashes is common that the other users

declare that he had looked in the motorcycle driving

direction prior to undertaking manoeuvre but did not

see the motorcyclist – the crashes are referred to as

“looked-but-failed-to-see” (Clabaux et al., 2012,

Brown, 2002, Clarke, 2007). The crashes could be

explained by a phenomenon called inattention

blindness (Pammer & Blink, 2013; Pammer et al.,

2017; Clark et al., 2004). Inattentional blindness

crashes are usually caused by factors such as low

Tmejová, T., Z˚uvala, R. and Bucsuházy, K.

In-depth Crash Causation Analysis of Motorcyclist Crashes.

DOI: 10.5220/0011033800003191

In Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2022), pages 249-256

ISBN: 978-989-758-573-9; ISSN: 2184-495X

249

conspicuity, divided attention and high expectation or

lower arousal (Green, 2004; Clark et al., 2004). The

conspicuity could be divided into two categories –

sensory which refers to physical properties of

information and cognitive which refers to the

perceived relevance of information (Grissinger,

2012). Conspicuity as one of the key factors in

motorcycle road crashes is often associated with

motorcycle size, low contrast with the road and its

surroundings, speed etc. (Khalid, 2021; Clabaux et

al., 2012; de Craen, Doumen, & van Norden, 2014;

Mitsopoulos- Rubens & Lenne, 2012). The “looked-

but-failed-to-see” mostly occurred in the good

visibility condition without other contributing risk

factors such as inexperience, intoxication or fatigue

(Pai, 2011). The majority of the right of way

motorcycle crashes occur at the T-intersections.

(Clark et al, 2004).

Following a sequential logic of driver

malfunctions, once the detection stage is correctly

performed is necessary to process acquired

information. The functional stage resulting from the

detection and processing of the event encountered

consists in the decision-making processes (van

Elslande, 2008). The incorrect decision could be

influenced by a wrong assessment of the motorcycle

speed and/or distance. The misperception of a

motorcycle’s motion is related to the overestimation

of the arrival time of small objects - the “size-arrival

effect” (DeLucia, 1991, Caird and Hancock, 1994;

Horswill, 2005).

As evidenced from the literature review, the

motorcyclist belongs to the most seriously injured

crash participants and is necessary to focus on the

causes of their crashes, especially motorcycle–

vehicle intersection crashes. The aim of this study is

to analyze the failures leading to these crashes -

whether from the point of view of motorcyclists'

failures or other participants. The study aims to use

in-depth crash data which allows the detailed analysis

of contributory factors. In comparison to the studies

which used official police data, in-depth data allows

analyzing not only the whole human functional

failure chain but also the crash mechanism (including

the possibility of reaction or driving speed before the

crash).

3 METHODS/

3.1 Czech In-depth Study

For the purpose of this study, data from the Czech In-

depth Accident Study has been used. The project

focuses on-road accidents with injuries that occurred

within a defined region of South Moravia. The

database currently includes more than 2000 crashes,

376 from this dataset involved motorcyclists. The in-

depth crash investigation is focused on the failure of

the whole system road user – infrastructure – vehicle.

The investigation includes an individual interview

with crash participants focused on all relevant

information related to the crash causes and

consequences.

3.2 Human Failure

The analysis will use the van Elslande human

functional failure model (van Elslande, 2008) which

assumes a sequential information processing chain of

human functions involved in information gathering,

processing, decision and action. During crash

analysis, the functional buckle is stopped in the stage

of rupture in the progress of the driver which leads to

losing control of the situation. At a general stage, the

classification model allows distinguishing Failures at

the information detection stage, Failures at the

diagnostic stage, Failures at the prognostic stage,

Failures at the decision stage on the execution of a

specific manoeuvre, Failures at the psychomotor

stage of taking action, and Overall failures dealing

with the psycho-physiological capacities of the

driver.

In each crash configuration, even the most often

contributing factors will be analyzed. Similar factors

affecting the likelihood of traffic accident causation

as by Petridou et al. (2000) was used. The human

factors contributing to crashes are modulate risk-

taking factors such as speeding or non-adjustment of

driving, conscious violation of traffic rules, risky

overtaking, the influence of alcohol or other

psychoactive substances and reduce capability to

meet traffic contingencies such as inexperience,

reduction of cognitive and psychomotoric function in

relation to higher age, panic reaction, glare, health

indisposition, drowsiness/fatigue or microsleep,

incorrect evaluation of the situation, limited view,

inattention, mental or somatic handicap.

4 RESULTS

The motorcycle crashes were subdivided according to

the crash type – individual motorcycle crashes on the

straight road and in a curve; vehicle-motorcycle

crashes on the straight road and in a curve;

intersection crashes. The human functional failure

was analyzed in relation to the road user.

VEHITS 2022 - 8th International Conference on Vehicle Technology and Intelligent Transport Systems

250

The most common types of motorcycle accidents

are motorcycle-vehicle crashes at the intersection

(36.4%), followed by motorcycle-vehicle crashes on

the straight road (21.3%) and individual motorcycle

crashes in a curve (12.3%).

4.1 Single Motorcycle Crashes

From the whole motorcycle crashes dataset, 29,3 %

involve single motorcycle crashes. The motorcycle

crashes were subdivided into two categories based on

the crash location - crashes in the curve (12,3 % of the

total crashes), crashes on the straight road (11,7 % of

the total crashes). Crashes in the curve mainly involve

loss of control of the motorcycle due to excessive

speed. The most common type of failure which leads

to curve crashes is the failure on the diagnosis level,

specifically incorrect evaluation of road conditions.

In comparison to the curve crashes, diagnosis level

failure is less common on a straight road segment.

The other common failure of the motorcycle driver on

diagnostic level is incorrect evaluation of a gap.

Crashes in the curve and on a straight segment show

equal contributing factors - speeding or non-

adjustment of driving, incorrect evaluation of the

situation and inexperience.

Table 1: Motorcyclist failures in the single motorcycle

crashes in the curve.

Diagnosis

failure

(73,7 %)

Incorrect

evaluation

of a road

difficulty

(71,1 %)

speeding or non-

adjustment of driving

(47,9 %)

incorrect evaluation of

the situation

(14,1 %)

inexperience

(12,7 %)

Prognosis

failure

(13,2 %)

Expecting no

perturbation

ahead

(10,5 %)

Table 2: Motorcyclist failures in the single motorcycle

crashes on the straight road.

Diagnosis

failure

(41,2 %)

Incorrect

evaluation

of a road

difficulty

(23,5 %)

Incorrect

evaluation of a

gap

(17,6 %)

speeding or non-

adjustment of

driving

(39,6 %)

inattention

(14,6 %)

Inexperience

(10,4 %)

incorrect evaluation

of the situation

(10,4 %)

Overall failure

(20,6 %)

Overstretching

cognitive

capacities

(11,8 %)

4.2 Motorcycle – Vehicle Crashes

Motorcycle-vehicle crashes are the most common

crash type (71 %). For detailed analysis were

separately analyzed motorcycle and passenger

vehicle failure.

4.2.1 Non-Intersection Crashes

Non-intersection crashes are more often caused by

motorcycle failure. The comparison of the

motorcycle-vehicle crashes on the straight road

sections and in the curve shows a similar

representation of the most common failure types and

contributing factors of individual crash participants.

While motorcyclists most often fail at the diagnosis

level (in terms of a collision with another vehicle

similarly as in terms of single-vehicle collisions), the

failure of vehicle drivers leading to a collision with a

motorcyclist is most often at the perception/detection

level.

Table 3: Driver failures in the motorcycle – vehicle crashes

on the straight road.

Motorcyclist failure

Diagnosis

failure

(35,5 %)

Incorrect

evaluation of a

gap

(22,6 %)

Inattention

(39,6 %)

speeding or non-

adjustment of driving

(18,9 %)

Prognosis

failure

(22,9 %)

Expecting no

perturbation

ahead

(12,9 %)

Expecting

another user not

to perform a

manoeuvre

(9,7 %)

Vehicle driver failure

Detection

failure

(70,8 %)

Information

acquisition

focused on a

partial

component of

the situation

(34,8 %)

Cursory or

hurried

information

acquisition

(26,1 %)

Inattention

(44,4 %)

incorrect evaluation of

the situation (18,5 %)

Diagnosis

failure

(16,7 %)

Incorrect

evaluation of a

gap

(8,7 %)

The frequency of detection level failure decreases in

the curves in comparison to the straight segments.

The most common failures at the detection level are

information acquisition focused on a partial

In-depth Crash Causation Analysis of Motorcyclist Crashes

251

component of the situation and cursory or hurried

information acquisition. The most common

contributory factors of vehicle drivers in the curve

and on a straight segment are inattention and incorrect

evaluation of the situation.

Table 4: Driver failures in the motorcycle – vehicle crashes

in the curve.

Motorcyclis

failure

Diagnosis

failure

(58,8 %)

Incorrect

evaluation

of a road

difficulty

(58,8 %)

speeding or non-

adjustment of driving

(39,5 %)

Inattention

(13,2 %)

Prognosis

failure

(23,5 %)

Expecting no

perturbation

ahead

(17,6 %)

Vehicle driver failure

Detection

failure

(57,6 %)

Cursory or

hurried

information

acquisition

(25 %)

Information

acquisition

focused on a

partial

component of the

situation

(25 %)

Inattention

(42,9 %)

incorrect evaluation

of the situation

(18,6 %)

Diagnosis

failure

(18,2 %)

Incorrect

evaluation

of a road

difficulty

(9,4 %)

4.2.2 Motorcycle – Vehicle Intersection

Crashes

While non-intersection crashes are more often caused

by motorcycle failure, with intersection crashes the

situation is reversed. At intersections, motorcycle-

vehicle crashes are most often caused by a vehicle

driver failure. The vehicle driver failure is most

commonly on the detection level. The crashes are

commonly not only due to the information acquisition

focused on a partial component of the situation and

cursory or hurried information acquisition as in non-

intersection crashes but also due to the non-detection

in visibility constraints conditions.

Most of the intersection crashes are caused by

right of way (ROW) violations. For purpose of

detailed analyses of the right of way crashes were

described individual types of right of way violations.

The most common failure of vehicle drivers when

making a left turn is not giving a right of way to the

oncoming motorcycle.

Only about 20 % of all intersection crashes are

caused by motorcycle failure and motorcyclists

mostly fail at the diagnosis level (similarly to non-

intersection crashes and individual crashes). Most of

contributing factors, besides risky overtaking, are

also similar to the non-intersection motorcycle–

vehicle crashes and individual motorcycle crashes.

Vehicle drivers mostly fail to see oncoming

motorcycle or motorcycle coming from his/her left

side.

Table 5: Driver failures in the motorcycle – vehicle

intersection crashes.

Motorcycle failure

Diagnosis

failure

(44,4 %)

Incorrect

evaluation

of a road

difficulty

(22,2 %)

Incorrect evaluation

of a gap

(11,1 %)

Inattention

(34,1 %)

incorrect

evaluation of the

situation

(22 %)

Risky overtaking

(14,6 %)

Detection

failure

(22,2 %)

Non-detection in

visibility constraints

condition

(11,1%)

Vehicle driver failure

Detection

failure

(80,2 %)

Information

acquisition

focused

on a partial

component of the

situation

(38 %)

Non-detection in

visibility constraints

conditions

(26,6 %)

Cursory or hurried

information

acquisition

(13,9 %)

Inattention

(50,4 %)

incorrect

evaluation of the

situation

(14,5 %)

Limited view

(13 %)

Diagnosis

failure

(6,2 %)

Incorrect

evaluation

of a road

difficulty

(2,5 %)

Incorrect evaluation

of a gap

(2,5 %)

4.3 Two Passenger Vehicle Crashes

4.3.1 Two Passenger Vehicle Intersection

Crashes

For the comparison and definition of the factors

which affect the failure in the motorcycle perception

by vehicle drivers, also the two-vehicle intersection

crashes were analyzed with the focus on the human

functional failure and contributory factors. The

obtained results show a reduction in the detection

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252

failure in comparison with failure in the perception of

the approaching motorcycle (an increase of about 11

%). The more frequently drivers in the two-vehicle

intersection crashes failed in the prognosis stage.

Table 6: Driver failures in the two passenger vehicle

intersection crashes.

Vehicle driver failure

Detection

failure

(69,7 %)

Information

acquisition

focused on a

partial

component of the

situation

(43,2 %)

Cursory or

hurried

information

acquisition

(10,2 %)

Non-detection in

visibility

constraints

conditions

(5,7 %)

Momentary

interruption in

information

acquisition

activity

(5,7 %)

Inattention

(42,5 %)

incorrect evaluation

of the situation

(18,1 %)

Limited view

(7,9 %)

Involutional changes

(7,1 %)

Diagnosis

failure

(13,5 %)

Mistaken

understanding of

how a site

functions

(4,5 %)

Erroneous

evaluation of a

passing road

difficulty

(3,5 %)

Prognosis

failure

(11,2 %)

Expecting

another user not

to perform a

manoeuvre

(6,8 %)

Actively

expecting

another user to

take regulating

action

(2,3 %)

Inattention

(42,9 %)

incorrect evaluation

of the situation

(18,6 %)

4.4 Comparison of Two-Vehicle

Crashes at the Intersection

The vehicle - motorcycle intersection crashes are

caused mainly by vehicle driver failure. For the

definition of risk factors associated with these types

of crashes, also some of the factors influencing the

mechanism of the motorcycle – vehicle and two

passenger vehicle crashes were compared.

The majority of vehicle-motorcycle crashes occur

at the T-intersection (two-vehicle crashes at T-

intersection are less common in comparison with

motorcycle-vehicle crashes). Vehicle drivers react

almost about 25% less frequently to the approaching

motorcycle in comparison with the reaction to the

vehicle approaching the intersection.

Figure 1: Comparison of vehicle driver’s reaction in two

vehicle crashes and vehicle-motorcycle crashes.

For more detailed analyses of pre-collision

scenarios, were compared the pre-collision speed and

collision speed for both two-vehicle crashes and

vehicle-motorcycle crashes. Before the collision,

more than 50 % of vehicle drivers, who failed to see

motorcycles, drove slower than 21 kph. On the other

side, drivers who failed to see another vehicle drove

faster than 30 kph about 10 % more frequently than

vehicle drivers, who failed to see motorcycles. For

collision speed is this difference even greater. The

collision speed of vehicle drivers, who fail to see an

approaching vehicle, was above 30 kph two times

more common than the collision speed of vehicle

drivers, who fail to see the approaching motorcycles.

Figure 2: Comparison of vehicle driver’s (who failed to

give way) pre-collision speed in two vehicle crashes and

vehicle-motorcycle crashes.

In-depth Crash Causation Analysis of Motorcyclist Crashes

253

Figure 3: Comparison of vehicle driver’s (who failed to

give way) pre-collision speed in two vehicle crashes and

vehicle-motorcycle crashes.

5 CONCLUSIONS

Motorcyclist belongs to the most seriously injured

crash participants. The study was carried out to

determine the common causes of motorcycle crashes

and analyse the failures leading to these crashes -

whether from the point of view of motorcyclists'

failures or other participants. Around half of the total

cases (single motorcycle and vehicle-motorcycle

collisions) are caused by motorcyclist failure, so the

provided data confirmed that initiatives in motorcycle

safety and countermeasures should be targeted on

both – motorcyclists and vehicle drivers, but human

failure causation differs, similarly, crash mechanisms

differ. The study uses in-depth crash data which

allows the detailed analysis of contributory and also

allows to analyse the whole human functional failure

chain and the crash mechanism (including the

possibility of reaction or driving speed before the

crash). This is a significant benefit compared to the

use of national crash data. Analyses of the in-depth

data allows to describe the vehicle

driver's/motorcyclist’s behaviour before the crash and

the most common factors that contribute to failure of

vehicle driver/motorcyclist. This study specifically

focused on vehicle-motorcycle crashes at the

intersections, which belongs to the most common

motorcycle crashes. These crashes are more likely to

be the fault of vehicle drivers, who fail to see

motorcycles. Several different theories were brought

to explain, why vehicle drivers fail to see motorcycles

despite, being in full view (Pammer et al., 2017;

Green, 2004; Crundall, 2012; Clabaux et al., 2012).

The specificity of the study is the comparison of the

factors influencing the mechanism of the motorcycle–

vehicle and two passenger vehicle crashes.

Similarly as described in RoSPA (2016), also

results from this study shows that crashes on curves

are often caused by motorcyclist failure especially

non-adjustment of speed or misjudgement of the

curve properties. Contributory factors of the

motorcycle failures are not only non-adjustment of

speed and incorrect situation evaluation but also

inexperience. Di Stasi et al. (2011) found in their

study that failure to adapt to external conditions is

often due to the inexperience of motorcyclists and the

associated lack of awareness of the impending

danger. This type of crash are nearly three times as

likely (compared with all the cases) to be rated as an

‘inexperienced’ motorcyclist (RoSPA, 2016). Clark

(2007) suggest that inexperienced motorcyclists are

more likely to participate in curve crashes. On the

other side, experienced vehicle drivers are more

susceptible to ROW crashes.

Crashes on the straight road have some similar

characteristics to crashes on curves – the crashes are

mostly caused by motorcycle’s failure on diagnosis

level and the most common contributing factors are

speeding or non-adjustment of driving and

inattention. The crashes caused by motorcyclists’

failure are largely related to insufficient safety

distance and loss of control over the motorcycle.

Insufficient safety distance is more likely

motorcyclist failure than the other road user. (RoSPA,

2016)

The obtained results can thus help to focus on the

risk aspects of these crashes and their mitigation.

While motorcyclists frequently fail at the diagnosis

level (especially incorrect evaluation of a road

difficulty), vehicle drivers mostly fail at the detection

level, especially in the intersections. Similarly as

described e.g. by Clabaux et al. (2012) or Clark

(2004), also in this study was confirmed that the

majority of motorcycle-vehicle crashes at the

intersection are caused by vehicle drivers’ failure.

About 80 % of vehicle drivers failed on detection

level. The obtained results confirmed Hurt et al.

(1981) conclusions, that in post-crash interviews

vehicle drivers involved in such crashes normally

stated that they did not see motorcycles when making

manoeuvres until the last moment before collisions.

Pammer (2017) suggested, that one of the key factors

of crashes are divided attention expectation or

attention set. This hypothesis suggests that vehicle

drivers don’t expect to see motorcycles in the driving

environment because they are rare on the road

compared to other road users. Therefore, we

compared crashes of vehicle drivers, who failed to see

approaching motorcycles and crashes of vehicle

drivers, who failed to see an approaching vehicle. In

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254

vehicle-motorcycle crashes, vehicle drivers are more

likely to fail at detection level in comparison to two-

vehicle crashes. These vehicle drivers commonly

(26,6 %) fail in detecting motorcycles in visibility

constraints conditions. This is not common for

vehicle drivers in two-vehicle crashes. Vehicle

drivers in two-vehicle intersection crashes more

frequently failed in the prognosis level – vehicle

driver incorrectly evaluate potential scenarios that

may occur in a given situation.

Similarly as described by Clark et al. (2004), the

majority of right of way crashes with motorcycles

were investigated at the T-intersection. Another

difference between vehicle-motorcycle crashes and

two-vehicle crashes in intersections is that vehicle

drivers (who should give way) drive faster before a

collision. They drive above 30 kph about 10 % more

frequently than vehicle drivers, who fail to see the

approaching motorcycle. Speed could be the factor

influencing the ability to correctly perceive the

situation in traffic. The collision speed of vehicle

drivers, who fail to see approaching vehicles, was

above 30 kph even two times more common than the

collision speed of vehicle drivers, who fail to see the

approaching motorcycle.

In this study, was not distinguished controlled and

uncontrolled (intersection with no traffic light, only

with road markings or signs) intersection, because

this was not necessary for purpose of the analyse.

Also, (similarly to the finding of Hole et. al, 1996)

there were only a few cases of ROW crashes, that

occur at a controlled intersection. This study did not

consider the level of experience of both motorcyclists

and vehicle drivers. Also, the factors which could

influence conspicuity such as the clothing colour,

helmet colour or use of any reflective elements were

not analyzed.

Drivers need to be aware of the number of factors

influencing motorcycle detection. The motorcyclist

conspicuity and detectability could be positively

affected by different conspicuity aids such as lights,

reflective vests, and coloured helmets. (e.g. Al-Awar

Smithe, 2010; Mitsopoulos-Rubens, 2012; Helman,

2012; de Craen, 2014; Wells 2004). The educational

activities should improve also motorcycle drivers’

skills and driving techniques especially in potentially

risky situations (especially inexperienced drivers),

the sensation-seeking and tendency to risky driving

should be also targeted. Road design strategies such

as traffic calming or enforcement strategies could

indirectly improve motorcyclists’ perceptibility, at

least in urban environments.

ACKNOWLEDGEMENTS

This paper was produced with the financial support of

the Ministry of Transport within the programme of

long-term conceptual development of research

institutes.

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