Occupant injury severities in hybrid-vehicle involved crashes: A random parameters approach with heterogeneity in means and variances

Puttipan Seraneeprakarn, Shuaiqi Huang, Venkataraman Shankar, Fred Mannering, Narayan Venkataraman, John Milton

Research output: Contribution to journalArticle

50 Scopus citations

Abstract

Differences in hybrid and non-hybrid vehicle design, and potential differences in driver-related behavior among owners of these vehicle types, can potentially have interesting implications for safety-related policies. To study possible differences in hybrid and non-hybrid occupant injury severities in motor vehicle crashes, this paper uses a sample of hybrid-vehicle-involved crashes and estimates a mixed logit model of the resulting injury level of the most severely injured occupant in the crash, while accounting for possible heterogeneity in the means and variances of model parameters. A total of 2015 crashes in Washington State, involving at least one hybrid vehicle in the 5-year period from January 1, 2006 to December 31, 2010 were analyzed. The data included crash information regarding occupants, vehicles, environmental conditions at the time of the crash, hybrid and non-hybrid vehicle attributes, crash-contributing circumstances for both hybrid and non-hybrid involved vehicles, collision type and crash location information relating to intersections, functional class of the highway, and highway curvature. Model estimation results show that a wide range of variables influence the most severely injured occupant, and that the number-of-occupants parameter and the intersection-location indicator parameter are random with significant heterogeneity in both means and variances. Sources of heterogeneity include the ratio of hybrid to non-hybrid vehicle counts in the crash, vehicle weight to horsepower ratio range (maximum difference in ratio) for the crash, number of adult occupants aged 41–64 years, functional class, and vehicle type interactions. The results further demonstrate the potential of models that address unobserved heterogeneity to unravel important relationships in the analysis of highway injury severities.

Original languageEnglish (US)
Pages (from-to)41-55
Number of pages15
JournalAnalytic Methods in Accident Research
Volume15
DOIs
StatePublished - Sep 2017

All Science Journal Classification (ASJC) codes

  • Transportation
  • Safety Research

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