Journal
ANALYTIC METHODS IN ACCIDENT RESEARCH
Volume 28, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.amar.2020.100137
Keywords
Pedestrian; Injury severity; Spatiotemporal analysis; Hierarchical Bayesian model; Random effects only
Funding
- United States Department of Transportation, University Transportation Center through the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) at the University of North Carolina at Charlotte [69A3551747133]
Ask authors/readers for more resources
To systematically account for the spatiotemporal features and unobserved heterogeneity within pedestrian-vehicle crashes, this paper employs the spatiotemporal analysis and hierarchical Bayesian random-effects models to explore the factors contributing to pedestrian-injury severities of pedestrian-vehicle crashes involving single vehicle in North Carolina from 2007 to 2018. Ten spatiotemporal patterns of the crashes are identified by applying an improved spatiotemporal analysis. Significant temporal instability and the spatiotemporal instability of the factors to the pedestrian-injury crashes are identified by the likelihood ratio tests. A hierarchical Bayesian random intercept logit model with random-effects across the spatiotemporal groups is firstly employed for the whole dataset. The comparison between different hierarchical models indicates that addressing random-effects across observations and increasing the number of random parameters could both improve the model performance. Then a hierarchical Bayesian random-effects-only logit model, which allows all parameters to be randomly distributed across observations, is developed to further investigate the unobserved heterogeneity in spatiotemporal segmented datasets. The significant improvements in terms of model fit and the hit accuracy underscore the superiority of the random-effects-only model. The marginal effects of the human, vehicle, crash, locality, roadway, environment, time, and traffic control factors for each spatiotemporal dataset also provide insights into possible inherent reasons for the spatiotemporal instability/tendency of the crash and correlated factors. Meanwhile, specific countermeasures are given to locations especially in which the spatially aggregated patterns of the crashes have new, consecutive, and intensifying temporal tendencies. This study provides a framework for engineers and researchers to identify spatiotemporal patterns of the crashes and explore the factors affecting pedestrian-injury severities especially in those existing crash-prone areas. (C) 2020 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available