首页|Examining the injury severity of public bus–taxi crashes: a random parameters logistic model with heterogeneity in means approach
Examining the injury severity of public bus–taxi crashes: a random parameters logistic model with heterogeneity in means approach
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NETL
NSTL
Taylor & Francis
Abstract Public buses and taxis play crucial roles in urban transportation. Ensuring their safety is of paramount importance to develop sustainable communities. This study investigated the significant factors contributing to the injury severity of bus–taxi crashes, using the crash data recorded by the police in Hong Kong from 2009 to 2019. To account for the unobserved heterogeneity, the random parameters logistic model with heterogeneity in means was elaborately developed. The results revealed that taxi driver age, bus age, traffic congestion, and taxi driver behavior had significantly heterogeneous effects on the injury severity of bus–taxi crashes and that the mean value of the random parameter for severe traffic congestion was likely to increase if the taxi’s age was <5 years. Taxi driver gender, rainfall, time of day, crash location, bus driver behavior, and collision type were found to significantly affect the bus–taxi crash severity. Specifically, female taxi drivers, old taxis, rainfall, midnight, improper manipulation of bus and taxi drivers, head-on and sideswipe collision types, and non-intersections were associated with a higher likelihood of fatal and severe crashes. Based on our findings, targeted countermeasures were proposed to mitigate the injury severity of bus–taxi crashes.
Bus–taxi crashesinjury severityunobserved heterogeneityrandom parametersheterogeneity in means
School of Civil Engineering and Transportation, South China University of Technology||Key Laboratory of Highway Engineering of Ministry of Education, Changsha University of Science & Technology
School of Civil Engineering and Transportation, South China University of Technology
Department of Civil Engineering, The University of Hong Kong
School of Civil Engineering and Transportation, South China University of Technology||Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology
NETL
NSTL
Taylor & Francis
