Heterogeneity in Hybrid Involved Crash Severities: an exploratory Analysis Using The Hierarchical Mixed Logit Model With Hierarchical Hybrid Vehicle Attributes

Open Access
Huang, Shuaiqi
Graduate Program:
Civil Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
July 10, 2015
Committee Members:
  • Venkataraman Shankar, Thesis Advisor
  • Vikash Varun Gayah, Thesis Advisor
  • Sukran Ilgin Guler, Thesis Advisor
  • Hybrid Vehicle Crash
  • Hierarchical Mixed Logit Model
  • Hybrid Vehicle Attributes
In recent years, hybrid vehicles have been increasingly accepted by consumers because of environmental, economic and nonrenewable (e.g. fossil fuels) resource concerns. In addition to the differences in fuel source, hybrid vehicles are different from the traditional fuel-engine vehicles in terms of ride characteristics, ride noise, vehicles weight, etc. For example, the power of hybrid vehicles comes from the combination of a fuel engine and an electric engine; when hybrid vehicles are using an electric engine, overall traffic noise is much lower than it would be with a traditional fuel engine vehicle. In addition, because of the extra battery, hybrid vehicles are always heavier than fuel engine vehicles in the same class. Factors such as weight distribution, vehicle noise interactions with driver and other vehicles can be sources of potential shifts in severity distributions involving hybrid vehicle crashes. In particular, how the attributes of a hybrid vehicle affect our inferences on crash severity propensities is a largely unaddressed issue in the literature. With the emergence of the hybrid vehicle market, this issue is bound to become prominent in our address of severe crashes in the nation, especially the FHWA’s target of lowering fatalities. This thesis attempts contribute some insight into the impact of hybrid vehicle attributes on crash severity propensities by using a random parameter (mixed) logit model to predict the crash severity in crashes involving hybrid vehicles. Three levels of severity are considered: (a) property damage only; (b) possible injury; and (c) injury. The injury category combines the fatality, severe (incapacitating) injury and evident injury categories. Using 5 years (from 2006 to 2010) of statewide data from reported crashes in Washington State involving hybrid vehicles, this thesis develops a mixed logit model for the severity of crashes involving hybrid vehicles, by considering factors such as roadway conditions, environment factors, driver and passenger attributes and vehicle characteristics. The mixed logit model is the state of the art in modeling crash severity. However, the extant literature does not include a form of the mixed logit model where the random crash severity parameters are evaluated hierarchically. The hierarchical model allows for the identification of factors that can influence of the mean of the random parameters in the mixed logit. In this thesis, the hierarchical influences consist of hybrid vehicle attributes, due to the fact that at least one of the vehicles involved in the crash dataset is a hybrid. The research results shows that the hierarchical mixed logit is a plausible approach for gaining insight into the particular impact of hybrid vehicle attributes on crash severity parameters.