Citation

Cai Q, Abdel-Aty M, Lee J, Huang H. Transportmetrica A: Transp. Sci. 2019; 15(2): 285-306.

Copyright

(Copyright © 2019, Informa - Taylor and Francis Group)

DOI

10.1080/23249935.2018.1471752

PMID

unavailable

Abstract

Traditionally, crash frequency analyses have been undertaken at the macro- and micro-levels, independently. This study proposes a Bayesian integrated spatial crash frequency model, which links the crash counts of macro- and micro-levels based on the spatial interaction. In addition, the proposed model considers the spatial autocorrelation of the different types of road entities (i.e. segments and intersections) at the micro-level with a joint structure. The modelling results indicated that the integrated model can provide better model performance for estimating macro- and micro-level crash counts, which validates the concept of integrating the models for the two levels. Also, the integrated model could simultaneously identify both macro- and micro-level factors contributing to the crash occurrence. Subsequently, a novel hotspot identification method was suggested, which enables us to detect hotspots for both macro- and micro-levels with comprehensive information from the two levels.

Language: en

Keywords

Bayesian modeling; hotspot identification; Integrated model; macro- and micro-level crash frequency; spatial interaction