Citation

Chen P, Sun F, Wang Z, Gao X, Jiao J, Tao Z. J. Saf. Res. 2018; 64: 135-143.

Affiliation

Beijing Traffic Management Bureau, Beijing, China.

Copyright

(Copyright © 2018, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2017.12.008

PMID

29636162

Abstract

INTRODUCTION: Building a safe biking environment is crucial to encouraging bicycle use. In developed areas with higher density and more mixed land use, the built environment factors that pose a crash risk may vary. This study investigates the connection between biking risk factors and the compact built environment, using data for Beijing.

METHOD: In the context of China, this paper seeks to answer two research questions. First, what types of built environment factors are correlated with bike-automobile crash frequency and risk? Second, how do risk factors vary across different types of bikes? Poisson lognormal random effects models are employed to examine how land use and roadway design factors are associated with the bike-automobile crashes.

RESULTS: The main findings are: (1) bike-automobile crashes are more likely to occur in densely developed areas, which is characterized by higher population density, more mixed land use, denser roads and junctions, and more parking lots; (2) areas with greater ground transit are correlated with more bike-automobile crashes and higher risks of involving in collisions; (3) the percentages of wider streets show negative associations with bike crash frequency; (4) built environment factors cannot help explain factors contributing to motorcycle-automobile crashes. PRACTICAL APPLICATIONS: In China's dense urban context, important policy implications for bicycle safety improvement drawn from this study include: prioritizing safety programs in urban centers, applying safety improvements to areas with more ground transit, placing bike-automobile crash countermeasures at road junctions, and improving bicycle safety on narrower streets.

Copyright © 2018 National Safety Council and Elsevier Ltd. All rights reserved.

Language: en

Keywords

Bike-automobile crash; Built environment; Frequency; Poisson lognormal random effects model; Risk