Citation

Lee J, Abdel-Aty MA. Int. J. Sustain. Transp. 2018; 12(8): 553-560.

Copyright

(Copyright © 2018, Informa - Taylor and Francis Group)

DOI

10.1080/15568318.2017.1407973

PMID

unavailable

Abstract

Over the last decade, bicycle ridership has been encouraged as a sustainable mode of transportation as it is economic and has less impact on the environment. Still, higher crash risk for bicyclists remains a deterrent for people to choose bicycling as their major mode of travel. As a first step in investigating bicycle safety, it is essential to identify not only the characteristics of the areas with the excessive number of bicycle crashes; but also those of the areas where crash-prone bicyclists reside. Therefore, this study aims to identify contributing factors for two subjects: (1) the number of bicycle crashes in the crash location's ZIP code and (2) the number of crash-involved bicyclists in their residence's ZIP. In order to achieve these objectives, a multivariate Bayesian Poisson lognormal CAR (conditional autoregressive) model was developed to identify the contributing factors for each subject. Regarding the model performance, the multivariate model outperformed its univariate counterpart in terms of DIC (deviance information criterion). Subsequently, hot zones for the two target zones were identified based on the modeling results. It is expected that practitioners are able to understand the contributing factors for bicycle crashes and identify hotspots from the results suggested in this study. In addition, they could implement safety countermeasures for the identified problematic locations to effectively reduce bicycle crashes.

Language: en

Keywords

Bayesian modeling; Bicycle safety; hotspot identification; macroscopic safety modeling; simultaneous equations modeling; socio-demographic factors