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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.