2025-08-07 ペンシルベニア州立大学（PennState）
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A group of researchers at Penn State recently modeled data taken from Pennsylvania roads, drawing a connection between crash risk and the presence of passing zones. Credit: krblokhin/iStock. All Rights Reserved.
＜関連情報＞
- https://www.psu.edu/news/engineering/story/overtaking-odds-do-passing-zones-make-rural-roads-safer
- https://www.sciencedirect.com/science/article/abs/pii/S0001457525002519
ペンシルベニア州の2車線農村道路における通過区間セグメントの安全性能：因果推論モデルと観測不能な異質性モデルからの衝突修正係数の比較 Safety performance of passing zone segments on two-lane rural highways in Pennsylvania: Comparing crash modification factors from causal inference and unobserved heterogeneity models
Prakash Poudel, Eric T. Donnell, Vikash V. Gayah
Accident Analysis & Prevention Available online 7 July 2025
DOI:https://doi.org/10.1016/j.aap.2025.108165
Highlights
- Assess the safety performance of passing zone presence on two-lane undivided rural highways.
- Consistent crash modification factor (CMF) results from unobserved heterogeneity models and causal inference method.
- Roadway segments with passing zones expected to experience fewer crashes compared to segments without passing zones.
- Safety management and network screening with improved safety assessments using the developed CMFs.
Abstract
Passing zones on two-lane rural highways are marked based on minimum passing sight distance criteria associated with the 85th-percentile speed or posted speed limit along the roadway segment. Although passing-related crashes account for a relatively small proportion of total reported crashes on two-lane rural highways, past research suggests that they tend to result in more severe injuries than non-passing-related crashes. However, the safety performance of roadway segments with passing zones has not been quantified or compared to segments with no passing zones. The purpose of this paper is to use data from Pennsylvania to compare the safety performance of two-lane rural highways with and without the presence of passing zone markings. Total crashes, fatal plus injury crashes, and target crashes are used to estimate crash modification factors (CMFs) for the presence of passing zones. A second objective of the paper is to compare the CMFs developed using two different methodological approaches: the propensity scores-potential outcomes causal inference framework and unobserved heterogeneity (random parameters) models. The results indicate that the CMFs developed using these two approaches are similar, although the CMFs from the unobserved heterogeneity models tended to estimate slightly fewer expected crashes in passing zone segments than the causal inference method. When compared to road segments without passing zones, those with passing zones experienced fewer total crashes, fatal and injury crashes, and head-on plus sideswipe crashes by 11.2 %, 12.2 %, and 10.6 %, respectively, based on the causal inference method.
