Citation

Li J, Wang X. Accid. Anal. Prev. 2022; 169: e106632.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.aap.2022.106632

PMID

35279617

Abstract

Urban arterials form the main structure of city street networks, and typically have high traffic volume and high crash frequency. To reduce the number of crashes, hotspot identification (HSID) is the first step in the traffic safety management process, and often utilizes crash prediction models. Classical crash prediction models investigate the relationship between roadway characteristics and traffic safety at the micro level, that is, they treat road segments and intersections as isolated units. Micro-level analysis has limitations, however, when examining urban arterial crashes: 1) signal spacing is typically short for urban arterials in dense street networks, and there are interactions between intersections and road segments that classical models do not accommodate; and 2) for practical engineering, a hotspot to which countermeasures are applied generally consists of several adjacent intersections and road segments instead of a single intersection or road segment. To address these concerns, signalized intersections and their adjacent road segments were combined into meso-level units, which were adopted to investigate traffic safety data from 21 urban arterials in Shanghai, China. To determine if the meso-level unit is the most suitable research unit for identifying hotspots on urban arterials, and if so, which HSID method can most consistently identify them, this study identified micro-level (separate intersection and road segment) and meso-level (combined intersection and road segment) hotspots using crash frequency, empirical Bayesian (EB), potential for safety improvement (PSI), and full Bayesian (FB) methods. To evaluate the performance of the HSID methods, hotspot consistency over two years was tested. The results showed 1) that EB and PSI performed better than the other methods no matter which research unit was used; 2) substantial inconsistency between the identified micro- and meso-level hotspots. To identify both hazardous corridors and individual intersections and road segments on urban arterials, a combination of micro- and meso-level hotspots should be recommended to local transportation authorities.

Language: en

Keywords

Empirical Bayesian; Hotspot identification; Meso level; Potential for safety improvement; Urban arterial