Vol.28, No.3, 2021, pp.753-767, doi:10.32604/iasc.2021.014661
OPEN ACCESS
ARTICLE
Analysis of Roadside Accident Severity on Rural and Urban Roadways
  • Fulu Wei1,2, Zhenggan Cai1, Yongqing Guo1,*, Pan Liu2, Zhenyu Wang3, Zhibin Li2
1 Department of Transportation Engineering, Shandong University of Technology, Zibo, 255000, China
2 Department of Transportation Planning and Management, Southeast University, Nanjing, 211189, China
3 Center for Urban Transportation Research, University of South Florida, Tampa, 33620, USA
* Corresponding Author: Yongqing Guo. Email:
(This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)
Received 06 October 2020; Accepted 09 February 2021; Issue published 20 April 2021
Abstract
The differences in traffic accident severity between urban and rural areas have been widely studied, but conclusions are still limited. To explore the factors influencing the occurrence of roadside accidents in urban and rural areas, 3735 roadside traffic accidents from 2017 to 2019 were analyzed. Fourteen variables from the aspects of driver, vehicle, driving environment, and other influencing factors were selected to establish a Bayesian binary logit model of roadside crashes. The deviance information criterion and receiver operating characteristic curve were used to test the goodness of fit for the traffic crash model. The results show that: (1) the Bayesian binary logit regression model well fits the traffic crash data, and the goodness of fit for sub-models based on separate urban and rural data is better than when based on all data (urban and rural); (2) 10 variables have a significant influence on the extent of roadside crash severity in the two areas, with different impacts; (3) drunk driving increases the probabilities of fatal traffic accidents by 10.8% and 16.4% in urban and rural areas, respectively; (4) the probabilities of fatality caused by traffic accidents involving trucks are 4.6% and 9.8% higher than those without trucks in urban and rural areas, respectively. The findings of this study can provide a theoretical foundation for traffic safety administration to formulate relevant policies or strategies in order to reduce the severity of roadside accidents.
Keywords
Traffic safety; roadside crashes; binary logit model; Bayesian estimation; injury severity
Cite This Article
F. Wei, Z. Cai, Y. Guo, P. Liu, Z. Wang et al., "Analysis of roadside accident severity on rural and urban roadways," Intelligent Automation & Soft Computing, vol. 28, no.3, pp. 753–767, 2021.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.