Latent Segmentation Based Count Models: Analysis Of Bicycle Safety In Montreal And Toronto
Keywords
Bicycle crashes; Latent segmentation negative binomial model; Latent segmentation poisson model; Population heterogeneity
Abstract
The study contributes to literature on bicycle safety by building on the traditional count regression models to investigate factors affecting bicycle crashes at the Traffic Analysis Zone (TAZ) level. TAZ is a traffic related geographic entity which is most frequently used as spatial unit for macroscopic crash risk analysis. In conventional count models, the impact of exogenous factors is restricted to be the same across the entire region. However, it is possible that the influence of exogenous factors might vary across different TAZs. To accommodate for the potential variation in the impact of exogenous factors we formulate latent segmentation based count models. Specifically, we formulate and estimate latent segmentation based Poisson (LP) and latent segmentation based Negative Binomial (LNB) models to study bicycle crash counts. In our latent segmentation approach, we allow for more than two segments and also consider a large set of variables in segmentation and segment specific models. The formulated models are estimated using bicycle-motor vehicle crash data from the Island of Montreal and City of Toronto for the years 2006 through 2010. The TAZ level variables considered in our analysis include accessibility measures, exposure measures, sociodemographic characteristics, socioeconomic characteristics, road network characteristics and built environment. A policy analysis is also conducted to illustrate the applicability of the proposed model for planning purposes. This macro-level research would assist decision makers, transportation officials and community planners to make informed decisions to proactively improve bicycle safety – a prerequisite to promoting a culture of active transportation.
Publication Date
10-1-2016
Publication Title
Accident Analysis and Prevention
Volume
95
Number of Pages
157-171
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1016/j.aap.2016.07.015
Copyright Status
Unknown
Socpus ID
84978538367 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84978538367
STARS Citation
Yasmin, Shamsunnahar and Eluru, Naveen, "Latent Segmentation Based Count Models: Analysis Of Bicycle Safety In Montreal And Toronto" (2016). Scopus Export 2015-2019. 2738.
https://stars.library.ucf.edu/scopus2015/2738