Macro and micro models for zonal crash prediction with application in hot zones identification

Helai Huang, Bo Song, Pengpeng Xu, Qiang Zeng, Jaeyoung Lee and Mohamed Abdel-Aty

Journal of Transport Geography, 2016, vol. 54, issue C, 248-256

Abstract: Zonal crash prediction has been one of the most prevalent topics in recent traffic safety research. Typically, zonal safety level is evaluated by relating aggregated crash statistics at a certain spatial scale to various macroscopic factors. Another potential solution is from the micro level perspective, in which zonal crash frequency is estimated by summing up the expected crashes of all the road entities located within the zones of interest. This study intended to compare these two types of zonal crash prediction models. The macro-level Bayesian spatial model with conditional autoregressive prior and the micro-level Bayesian spatial joint model were developed and empirically evaluated, respectively. An integrated hot zone identification approach was then proposed to exploit the merits of separate macro and micro screening results. The research was based on a three-year dataset of an urban road network in Hillsborough County, Florida, U.S.

Keywords: Crash prediction model; Zonal safety analysis; Bayesian inference; Spatial correlation; Conditional autoregressive model; Integrated screening (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:54:y:2016:i:c:p:248-256

DOI: 10.1016/j.jtrangeo.2016.06.012

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