Analysis of Injury Severity of Drivers Involved Different Types of Two-Vehicle Crashes Using Random-Parameters Logit Models with Heterogeneity in Means and Variances

Title

Analysis of Injury Severity of Drivers Involved Different Types of Two-Vehicle Crashes Using Random-Parameters Logit Models with Heterogeneity in Means and Variances

 Author

Wu, Qiang (Nantong University)
Song, Dongdong (Beijing Jiaotong University)
Wang, Chenzhu (Southeast University)
Chen, Fei (Southeast University)
Cheng, Jianchuan (Southeast University)
Easa, Said M. (Toronto Metropolitan University)
Yang, Y. (TU Delft Transport and Planning)
Yang, Wenchen (Broadvision Engineering Consultants Co., Ltd.; Yunnan Key Laboratory of Digital Communications)

Date

2023

 Abstract

This study proposes random-parameters multinomial logit models, with heterogeneity in means and variances, to explore the differences in the factors influencing injury severities of drivers involved in different types of two-vehicle crashes. The models are verified using crash data from the United Kingdom (UK) over three years (2016–2018). Three types of crashes are separately identified (car-car, car-truck, and truck-truck crashes). In this study, a wide variety of potential variables, including the driver, vehicle, road, and environmental characteristics, are considered, with two possible injury-severity outcomes: severe and slight injury. The results show that unobserved heterogeneity existed for young drivers in both car-car and truck-truck crash models and the 30 mph speed limit in the three separate models. Remarkably variations are observed in crashes involving different types of vehicles. The driver’s age and gender, speeding, sideswipes, presence of junctions, weekdays, unlit, and weather conditions significantly impact driver-injury severities in various types of vehicle crashes. These findings are expected to help policymakers seek to improve highway safety and implement proper safety countermeasures.

 To reference this document use:

http://resolver.tudelft.nl/uuid:751dc447-830d-4c96-a68d-31aa34f1386b

 DOI

https://doi.org/10.1155/2023/3399631

 ISSN

0197-6729

 Source

Journal of Advanced Transportation, 2023

 Part of collection

Institutional Repository

 Document type

journal article

 Rights

© 2023 Qiang Wu, Dongdong Song, Chenzhu Wang, Fei Chen, Jianchuan Cheng, Said M. Easa, Y. Yang, Wenchen Yang
Files
PDF 3399631.pdf 524.39 KB
Close viewer