Citation

ASCE ASME J. Risk Uncertain. Eng. Syst. A Civ. Eng. 2019; 5(4): e05019001.

Copyright

(Copyright © 2019, American Society of Civil Engineers)

DOI

10.1061/AJRUA6.0001022

PMID

unavailable

Abstract

Currently, frequent crash occurrences significantly influence traffic operation conditions and travel reliability for urban expressway systems. Therefore, it is vital to understand the crash occurrence mechanisms and then introduce safety improvement countermeasures. Emerging studies have been conducted to unveil the relationships between traffic operation conditions and crash occurrence with advanced traffic-sensing data. However, the majority of previous studies have only identified correlation relationships, which are insufficient for traffic-safety improvement. On the other hand, existing crash causal investigations have limitations of utilizing aggregated traffic-flow data and considering the crash occurrence mechanisms only in a reflective way (in contrast to the formative way). In this study, the confounding impacts among crash risk contributing factors and the crash causal relationships were revealed through the partial least-squares path modeling (PLS-PM) analysis approach. Data from the Shanghai urban expressway system in China were utilized for the empirical analyses. First, random forest models were adopted to rank the variable importance, and a total of six contributing factors were selected as inputs that feed into the PLS path models. Then, two different causal relationship structures (formative and reflective) were established, and the best-fitted model structures were identified. The results showed that average operation speed has negative impacts on crash occurrence, and the variables indicated that disturbed traffic flows have positive causal relationships. Finally, the analysis results shed some light on proactive safety management strategies.

Language: en