Citation

Wu J, Rasouli S, Zhao J, Qian Y, Cheng L. Travel Behav. Soc. 2023; 30: 135-147.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.tbs.2022.09.003

PMID

unavailable

Abstract

Studies on fatal crashes involving large trucks are typically focused on the examination of injury severity at the personal level rather than the crash level. In this study, instead of adopting the predefined, commonly used rule of transformation of individual injury severity to crash level severity, we develop a data specific transformation approach. Crash-level severity for fatal large-truck involved crashes is first identified applying a two-step clustering method. A Bayesian network is then constructed to investigate the impacts of influential factors on the crash severity. Data obtained from the Fatality Analysis Reporting System in 2019, are used to calibrate the model. The results indicate that the number of vehicles involved in the crash, collision manner, truck-airbag deployment, overturn of vehicles, driver's speeding offense, and gross vehicle weight are the key determinants of crash injury severity. Uni- and two-dimensional inference analyses were conducted to determine the relations among these factors and their association with crash severity.

Language: en

Keywords

Bayesian network; Crash injury severity; Inference analysis; Truck-involved fatal crash