Citation

Wang C, Chen F, Zhang Y, Cheng J. Transp. Lett. 2023; 15(7): 742-753.

Copyright

(Copyright © 2023, Maney Publishing, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19427867.2022.2086760

PMID

unavailable

Abstract

To examine the difference in contributing factors of rear-end crashes of different injury severity involving different types of vehicles, this paper proposed random-parameters multinomial logit models with heterogeneity in means and variances. A three-year (2017-2019) rear-end crash data collected from Beijing-Shanghai Highways in China was used to calibrate the models. The rear-end crashes were classified as five types (Car-Car, Car-Truck, Truck-Truck, Truck-Car, Others). With two possible injury severity outcomes of medium/severe injury and light injury, a wide range of possible variables including crash, traffic, speed, geometric, and sight characteristics were considered in this study. Likelihood ratio tests revealed the rationality of adopting merged models using the data across three-year periods. Remarkably significant differences were shown in crashes involving different types of vehicles. The results accounting for the possible heterogeneity could be of value to roadway designers and traffic management departments seeking to promote highway safety and raise accurate safety countermeasures.

Language: en

Keywords

injury severity; marginal effect; random-parameters multinomial logit approach; Rear-end crash type; unobserved heterogeneity in means and variances