Citation

Ding C, Ma X, Wang Y, Wang Y. Accid. Anal. Prev. 2015; 85: 58-65.

Affiliation

School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China. Electronic address: ypwang@buaa.edu.cn.

Copyright

(Copyright © 2015, Elsevier Publishing)

DOI

10.1016/j.aap.2015.08.024

PMID

26373988

Abstract

Understanding the relationships between influential factors and incident clearance time is crucial to make effective countermeasures for incident management agencies. Although there have been a certain number of achievements on incident clearance time modeling, limited effort is made to investigate the relative role of incident response time and its self-selection in influencing the clearance time. To fill this gap, this study uses the endogenous switching model to explore the influential factors in incident clearance time, and aims to disentangle causation from self-selection bias caused by response process. Under the joint two-stage model framework, the binary probit model and switching regression model are formulated for both incident response time and clearance time, respectively. Based on the freeway incident data collected in Washington State, full information maximum likelihood (FIML) method is utilized to estimate the endogenous switching model parameters. Significant factors affecting incident response time and clearance time can be identified, including incident, temporal, geographical, environmental, traffic and operational attributes. The estimate results reveal the influential effects of incident, temporal, geographical, environmental, traffic and operational factors on incident response time and clearance time. In addition, the causality of incident response time itself and its self-selection correction on incident clearance time are found to be indispensable. These findings suggest that the causal effect of response time on incident clearance time will be overestimated if the self-selection bias is not considered.

Language: en