Citation

Ding H, Sze NN. Multimodal Transp. 2022; 1(2): e100020.

Copyright

(Copyright © 2022, The Author(s), Publisher Elsevier Publishing)

DOI

10.1016/j.multra.2022.100020

PMID

unavailable

Abstract

Although cycling has benefits to environment and physical health, bicyclists are vulnerable road users. Prior studies have identified the environment, traffic and road user factors that affect the risk of bicycle-related crashes. However, it is rare that difference in their effects on the risk amongst different bicycle crash types is investigated. For instance, there is possible correlation between counts of different crash types. In this study, multivariate Poisson-lognormal regression method is applied to examine the relationship between bicycle crash frequencies and possible explanatory factors, with which the correlation between bicycle-vehicle and bicycle-bicycle crashes is considered, using the crash data from London in 2018 and 2019. In addition, effects of road network characteristics on bicycle crash frequencies are also considered.

RESULTS indicate that proposed multivariate Poisson-lognormal model outperforms conventional univariate Poisson-lognormal models, in term of the metrics like deviance information criterion (DIC). In addition, factors including population socio-economics, land use, and road network characteristics that affect bicycle crash risk are identified. For instance, effects of traffic flow, residential area, network connectivity, and intersection density on the crash counts are different between bicycle-vehicle and bicycle-bicycle crashes.

FINDINGS are useful for the implementation of remedial measures that can improve overall bicycle safety in the long run.

Language: en

Keywords

Bicycle crash; Bicycle safety; Multivariate Poisson-lognormal model; Network accessibility; Network connectivity