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Journal of Advanced Transportation
Volume 2018, Article ID 2702360, 9 pages
https://doi.org/10.1155/2018/2702360
Research Article

Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model

1College of Traffic Engineering and Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
2Department of Civil & Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA

Correspondence should be addressed to Feng Chen; nc.ude.ijgnot@nehcgnef

Received 15 June 2017; Accepted 27 November 2017; Published 17 January 2018

Academic Editor: Helai Huang

Copyright © 2018 Bowen Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Road traffic accidents are believed to be associated with not only road geometric feature and traffic characteristic, but also weather condition. To address these safety issues, it is of paramount importance to understand how these factors affect the occurrences of the crashes. Existing studies have suggested that the mechanisms of single-vehicle (SV) accidents and multivehicle (MV) accidents can be very different. Few studies were conducted to examine the difference of SV and MV accident probability by addressing unobserved heterogeneity at the same time. To investigate the different contributing factors on SV and MV, a mixed logit model is employed using disaggregated data with the response variable categorized as no accidents, SV accidents, and MV accidents. The results indicate that, in addition to speed gap, length of segment, and wet road surfaces which are significant for both SV and MV accidents, most of other variables are significant only for MV accidents. Traffic, road, and surface characteristics are main influence factors of SV and MV accident possibility. Hourly traffic volume, inside shoulder width, and wet road surface are found to produce statistically significant random parameters. Their effects on the possibility of SV and MV accident vary across different road segments.