Citation

Zhang Z, Gong Y, Yang X. Transp. Res. Rec. 2024; 2678(3): 613-624.

Copyright

(Copyright © 2024, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981231182394

PMID

unavailable

Abstract

The occurrence of secondary crashes on highways can bring many adverse effects, such as traffic congestion and air pollution, leading to more crashes. Accurate identification of secondary crashes is the basis for identifying contributing factors that are the cornerstones for incident management systems to find effective strategies to reduce the risk of secondary crashes. However, secondary crash records are often not recorded correctly in the crash database. To tackle this issue, this research proposes a hybrid method to accurately identify primary and secondary crashes. Based on the identified primary and secondary crashes, this study developed a binary logit model to find contributing factors of secondary crashes and construct a HOPIT model to analyze the crash injury patterns in primary and secondary crashes using the identified data on primary and secondary crashes, respectively. This study provides a better understanding of contributing factors as well as the crash injury patterns in secondary crashes.

Language: en