Utilizing Partial Least-Squares Path Modeling to Analyze Crash Risk Contributing Factors for Shanghai Urban Expressway System
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 5, Issue 4
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.
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Acknowledgments
This study was jointly sponsored by the Chinese National Natural Science Foundation (NSFC 71771174 and 71401127) and the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RSC2017K003), Beijing Jiaotong University.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 5 • Issue 4 • December 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Sep 20, 2018
Accepted: Mar 25, 2019
Published online: Sep 18, 2019
Published in print: Dec 1, 2019
Discussion open until: Feb 18, 2020
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