Introduction: Hit-and-run crashes occur when the driver at fault leaves the scene without reporting, which could delay emergency response for the victims who are left. For this reason, it is assumed that hit-and-run crashes lead to more serious injuries; however, the research in this area is limited. The objectives of the study are to examine the differences in hit-and-run and non-hitand-run victim injury severities and to identify the factors that may influence any differences. Methods: Quasi-induced exposure technique, an indirect method, is employed to measure the relative crash exposures between hit-and-run and non-hit-and-run crashes using Michigan twovehicle injury crashes 2012-2014. Random parameter ordered logit model is used to reveal the discrepancy of the factors contributing to victim injury severity. Results: We found that the injuries sustained by the drivers left at the scene (victims) of hit-andrun crashes were generally less severe compared to non-hit-and-run driver victims, which may be attributed to the differential crash factors in terms of driver age and vehicle type. The injuryseverity contributing factors of hit-and-run crashes differed considerably from the non-hit-andrun crashes. Characteristics such as occurring in rural areas, at nighttime, at intersections, crash type, and alcohol involvement significantly increased the injury severities of the driver victims. Conclusion: We inform the hit-and-run literature to suggest a contradiction to the assumption that leaving the crash scene may lead to more serious injuries. This example emphasizes the importance of distinguishing different types of crashes and their contributing factors. We offer an indirect approach that can help to identify underlying factors and reduce bias, which can inform traffic safety methods and serve to propose effective safety countermeasures.

INTRODUCTION:Quasi-induced exposure (QIE) technique has been popularly applied in the field of traffic safety research for decades. One of the basic assumptions of QIE theory is that the not-at-fault driving parties (D2s) involved in the crashes are the random selection of overall driving population at the event of crash occurrence. Very few literatures, however, can be identified to validate the assumption for crashes with specific injury severities that may not be satisfied in reality.METHOD:The study aims to check the validity of the assumption categorized by crash injury severity with the use of Michigan crash data. Latent class analysis is employed to generate several latent classes for the crashes with specific injury outcomes. Chi-square test is adopted to identify the significance of the similarity of D2 distributions among the latent classes.RESULTS:The results indicate that: (a) for fatal crashes the statistical tests do not identify the significant discrepancies for D2 distributions of driver gender, age, and vehicle type between latent classes; (b) for injury crashes, both D2 driver gender and age have the similar distributions between/among various classes, while the D2 vehicle types show the inconsistent distributions; and (c) with respect to property damage only crashes, the distributions of three vehicle-driver characteristics are significantly different among the latent classes. It implies that the underlying assumption may not entirely hold true for all the injury severities and driver-vehicle characteristics. Practical Applications: The findings pinpoint the applicability of the QIE technique under specific scenarios and highlight the importance of validating the underlying assumption of QIE prior to its application.