Validating the underlying assumption of quasi-induced exposure technique disaggregated by crash injury severity |
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| Institution: | 1. College of Engineering, Zhejiang Normal University, Zhejiang 321005, China;2. Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, Zhejiang Normal University, Zhejiang 321005, China;3. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 611756, China;4. Sichuan Vocational and Technical College of Communications, Chengdu, Sichuan 611130, China;1. Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA;2. Applied Statistics, School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA;3. Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208 USA;1. School of Transportation, Southeast University, China;2. Jiangsu Key Laboratory of Urban ITS, China;3. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, China;4. Transport Strategy Centre, Imperial College London, UK;1. University of Iowa Injury Prevention Research Center, 2195 Westlawn, Iowa City, IA 52242, USA;2. Department of Epidemiology, College of Public Health, University of Iowa, 145 North Riverside Drive, Iowa City, IA 52242, USA;3. Department of Biostatistics, College of Public Health, University of Iowa, 145 North Riverside Drive, Iowa City, IA 52242, USA;4. Department of Occupational and Environmental Health, College of Public Health, University of Iowa, 145 North Riverside Drive, Iowa City, IA 52242, USA;1. Exercise and Sports Medicine, Department of Orthopedics, Rheumatology and Traumatology, University of Campinas, UNICAMP, Campinas, SP, Brazil;2. University of Illinois at Chicago, Chicago, IL, USA;3. Department of Orthopedics and Traumatology, University of São Paulo, São Paulo, SP, Brazil;4. Centre for Sports and Exercise Medicine, Barts and The London School of Medicine and Dentistry, Mile End Hospital, 275 Bancroft Road, London E1 4DG, UK |
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| Abstract: | 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. |
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| Keywords: | Quasi-induced exposure Injury severity Latent class analysis Chi-square test Driver-vehicle characteristics |
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