Edinburgh Napier University

  Motorcyclists that have no protective structures while motorcycling as other
occupants of automobiles do can be particularly vulnerable to accident injuries (i.e.,
motorcycles are not as crashworthy as automobiles). Motorcyclists' susceptibility to
accident injuries in nature may act synergistically with the complexity of conflicting
manoeuvres between motorcycles and other motor vehicles to increase their injury
severities in accidents that take place at junctions (e.g., T-junction or crossroad).
Previous studies have applied crash prediction models to investigate influential factors
on the occurrences of different crash configurations among automobiles but statistical
models of motorcyclist injury severity resulting from different motorcycle-car crash
configurations have rarely been developed.
This current research attempts to develop the appropriate statistical models of
motorcyclist injury severity by various crash configurations conditioned on crash
occurrence at T-junctions in the UK. T-junctions are selected in this study because
such junctions represent the single greatest danger to motorcyclists - for junction-type
accidents, the statistics from the UK Stats19 accident injury database over the years
1991 and 2004 suggested that T-junctions were ranked the highest in terms of injury
severity (Le., accidents at T-junctions resulted in approximately 65% of all casualties
that sustained fatal or serious injuries) and accident occurrence (i.e., accidents at Tjunctions
accounted for 62% of all motorcyclist casualties). This may be in part
because there is a comparatively large number ofT-junctions in the UK. Although the
author was unable to take into account the exposure factor due to the lack of such data
(Le., the total number of T-junctions, and the number of motorcycles travelling on
these locations), it remains true that more severe accidents happen at T-junctions than
any other type of junction. In this present study, motorcycle-car accidents at Tjunctions
were classified into several crash configurations based on two methods that
have been widely used in literature. The first method is based on the conflicts that
arise from the pre-crash manoeuvres of the motorcycle and car. The second method is
on the basis of first points of impact of the motorcycle and car. The crash
configurations that are classified in this current study based on the mixture of these
two methods include (a) accidents involving gap acceptance (i.e., approach-turn crash and angle crash), (b) head-on crash, and (c) same-direction crash (i.e., sideswipe crash
and rear-end crash).
Since injury severity levels in traffic accidents are typically progressive (ranging from
no injury to fatal/death), the ordered response models have come into fairly wide use
as a framework for analysing such responses. Using the accident data extracted from
the Stats19 accident injury database over 14-year period (1991~2004), the ordered
probit (OP) model of motorcyclist injury severity were estimated because the
dependent variable (i.e., no injury, slight injury, KSI: killed or seriously injured) is
intrinsically discrete and ordinal. A set of the independent variables were included as
the predictor variables, including rider/motorist attributes, vehicle factors,
weather/temporal factors, roadway/geometric characteristics, and crash factors. The
current research firstly estimated the aggregate OP model of motorcyclist injury
severity by motorcycle-car accidents in whole. Additional disaggregate models of
motorcyclist injury severity by various crash configurations were subsequently
conducted ..
It appears in this current research that while the aggregate model by motorcycle-car
accidents in whole is useful to uncover a general overview of the factors that were
associated with the increased motorcyclist injury severity, the dis aggregate models by
various crash configurations provide valuable insights (that may not be uncovered by
an aggregate crash model) that motorcyclist injury severity in different crash
configurations are associated with different pre-crash conditions. For example, the
preliminary analysis by conducting descriptive analysis reveals that the deadliest
crash manner in approach-turn crashes and angle crashes was a collision in which a
right-turn car collided with an approaching motorcycle. Such crash patterns that
occurred at stop-/give-way controlled junctions appear to exacerbate motorcyclist
injury severity. The disaggregate models by the deadliest crash manners in approachturn
crashes and angle crashes suggest that injuries tended to be more severe in
crashes where a right-turn motorist was identified to fail to yield to an approaching
motorcyclist. Other disaggregate crash models also identified important determinants
of motorcyclist injury severity. For instance, the estimation results of the head-on
crash model reveal that motorcyclists were more injurious in collisions where curves
were present for cars than where the bend was absent. Another noteworthy result is that a traversing motorcycle colliding with a travelling-straight car predisposed
motorcyclists to a greater risk of KSIs. These findings were clearly obscured by the
estimation of the aggregate model by accidents in whole.
In the course of the investigation of the factors that affect motorcyclist injury severity,
it became clear that another problem, that of a right-turn motorist's failure to yield to
motorcyclists (for the deadliest crash patterns in both approach-turn crash and angle
crash), needs to be further examined. The logistic models are estimated to evaluate the
likelihood of motorist's right-of-way violation over non right-of-way violation as a
function of human attributes, weather/temporal factors, roadway/geometric factors,
vehicle characteristics, and crash factors. The logistic models uncover the factors
determining the likelihood of motorists' failure to yield. Noteworthy findings include,
for instance, teenaged motorists, elderly motorists, male motorists, and professional
motorists (Le., those driving heavy goods vehicles and buses/coaches) were more
likely to infringe upon motorcycle's right-of-way. In addition, violation cases
appeared to be more likely to occur on non built-up roadways, and during
evening/midnight/early morning hours This present research has attempted to fill the research gaps that crash prediction
models focused on analysing motorcyclist injury severity in different crash
configurations have rarely been developed. The results obtained in this current
research, by exploring a broad range of variables including attributes of riders and
motorists, roadway/geometric characteristics, weather/temporal factors, and vehicle
characteristics, provide valuable insights into the underlying relationship between risk
factors and motorcycle injury severity both at an aggregate level and at a disaggregate
level. This research finally discusses the implications of the findings and offers a
guideline for future research.