Citation

Zhang Z, Akinci B, Qian S. Anal. Meth. Accid. Res. 2022; 33: e100203.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.amar.2021.100203

PMID

unavailable

Abstract

The increasing number of crashes occurring in work zones has received considerable attention in recent years. Previous studies have mainly focused on associations between work zone configurations and crash occurrence. Although identification of associational relations helps us understand how work zones co-exist with crashes, it does not provide interventional guidelines necessary to improve safety of work zone operations. In this paper, a causal inference model based on the potential outcome framework is proposed to rigorously infer the causal effects of work zone presence on crash risks under various work zone configurations, along with robustness tests. In developing such a causal model, three research gaps are identified and addressed: (1) potential confounding bias due to unobservable roadway characteristics; (2) potential bias caused by unobserved variables in multisource data; and (3) lack of actually observed traffic data and weather information at the exact time when a crash occurred and lack of large-scale high-granular data. The proposed methodology is applied to 5,006 work zones in Pennsylvania from 2015 to 2017, and the results are validated via a series of robustness tests. The results show that the causal effect of a work zone on crash occurrence is significantly positive, especially on roadways with high traffic volumes, on long-distance work zones, and work zones conducted during daytime. It appears that conducting work zones during nighttime with the current deployment strategies on Pennsylvania state roads does not necessarily increase crash risks, but a work zone significantly increases crash risks during day time.

Language: en

Keywords

causal inference; crash analysis; multisource data; rare event bias; work zone safety