A cross-comparison of different techniques for modeling macro-level cyclist crashes.

Despite the recognized benefits of cycling as a sustainable mode of transportation, cyclists are considered vulnerable road users and there are concerns about their safety. Therefore, it is essential to investigate the factors affecting cyclist safety. The goal of this study is to evaluate and compare different approaches of modeling macro-level cyclist safety as well as investigating factors that contribute to cyclist crashes using a comprehensive list of covariates. Data from 134 traffic analysis zones (TAZs) in the City of Vancouver were used to develop macro-level crash models (CM) incorporating variables related to actual traffic exposure, socio-economics, land use, built environment, and bike network. Four types of CMs were developed under a full Bayesian framework: Poisson lognormal model (PLN), random intercepts PLN model (RIPLN), random parameters PLN model (RPPLN), and spatial PLN model (SPLN). The SPLN model had the best goodness of fit, and the results highlighted the significant effects of spatial correlation. The models showed that the cyclist crashes were positively associated with bike and vehicle exposure measures, households, commercial area density, and signal density. On the other hand, negative associations were found between cyclist crashes and some bike network indicators such as average edge length, average zonal slope, and off-street bike links.

[1]  M. Harris,et al.  The impact of transportation infrastructure on bicycling injuries and crashes: a review of the literature , 2009, Environmental health : a global access science source.

[2]  Luis F. Miranda-Moreno,et al.  Effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter of Poisson-gamma models for modeling motor vehicle crashes: a Bayesian perspective , 2008 .

[3]  Satish V. Ukkusuri,et al.  Random Parameter Model Used to Explain Effects of Built-Environment Characteristics on Pedestrian Crash Frequency , 2011 .

[4]  Tarek Sayed,et al.  Models for estimating zone-level bike kilometers traveled using bike network, land use, and road facility variables , 2017 .

[5]  Hoong Chor Chin,et al.  Severity of driver injury and vehicle damage in traffic crashes at intersections: a Bayesian hierarchical analysis. , 2008, Accident; analysis and prevention.

[6]  Tarek Sayed,et al.  Evaluating Safety of Urban Arterial Roadways , 2001 .

[7]  Mohamed El Esawey,et al.  Development of a cycling data model: City of Vancouver case study , 2015 .

[8]  Wei Wang,et al.  Effects of parallelogram-shaped pavement markings on vehicle speed and safety of pedestrian crosswalks on urban roads in China. , 2016, Accident; analysis and prevention.

[9]  Fred L Mannering,et al.  Highway accident severities and the mixed logit model: an exploratory empirical analysis. , 2008, Accident; analysis and prevention.

[10]  Mohamed Abdel-Aty,et al.  Macroscopic spatial analysis of pedestrian and bicycle crashes. , 2012, Accident; analysis and prevention.

[11]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .

[12]  Carlo Giacomo Prato,et al.  Infrastructure and spatial effects on the frequency of cyclist-motorist collisions in the Copenhagen Region , 2016 .

[13]  Tarek Sayed,et al.  Urban Arterial Accident Prediction Models with Spatial Effects , 2009 .

[14]  Raghavan Srinivasan,et al.  Evaluating the safety effects of bicycle lanes in New York City. , 2012, American journal of public health.

[15]  Tarek Sayed,et al.  Evaluating the impact of bike network indicators on cyclist safety using macro-level collision prediction models. , 2016, Accident; analysis and prevention.

[16]  Tarek Sayed,et al.  Accident prediction models with random corridor parameters. , 2009, Accident; analysis and prevention.

[17]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[18]  Luis F. Miranda-Moreno,et al.  Disaggregate Exposure Measures and Injury Frequency Models of Cyclist Safety at Signalized Intersections , 2011 .

[19]  Tarek Sayed,et al.  A framework to proactively consider road safety within the road planning process , 2003 .

[20]  Le Minh Kieu,et al.  Spatiotemporal and random parameter panel data models of traffic crash fatalities in Vietnam. , 2016, Accident; analysis and prevention.

[21]  Paul P Jovanis,et al.  Bayesian Multivariate Poisson Lognormal Models for Crash Severity Modeling and Site Ranking , 2009 .

[22]  Fred L Mannering,et al.  A note on modeling vehicle accident frequencies with random-parameters count models. , 2009, Accident; analysis and prevention.

[23]  Mohamed Abdel-Aty,et al.  Crash Estimation at Signalized Intersections Along Corridors: Analyzing Spatial Effect and Identifying Significant Factors , 2006 .

[24]  J. Dekoster,et al.  Cycling : the way ahead for towns and cities , 1999 .

[25]  Corinne Peek-Asa,et al.  On-road bicycle facilities and bicycle crashes in Iowa, 2007-2010. , 2013, Accident; analysis and prevention.

[26]  Mohamed Abdel-Aty,et al.  A Bayesian spatial random parameters Tobit model for analyzing crash rates on roadway segments. , 2017, Accident; analysis and prevention.

[27]  Paul P Jovanis,et al.  Spatial Correlation in Multilevel Crash Frequency Models , 2010 .

[28]  Tarek Sayed,et al.  Spatial Effects on Zone-Level Collision Prediction Models , 2013 .

[29]  Feng Wei,et al.  An empirical tool to evaluate the safety of cyclists: Community based, macro-level collision prediction models using negative binomial regression. , 2013, Accident; analysis and prevention.

[30]  Karim El-Basyouny,et al.  New techniques for developing safety performance functions , 2011 .

[31]  Shaw-Pin Miaou,et al.  Modeling Traffic Crash-Flow Relationships for Intersections: Dispersion Parameter, Functional Form, and Bayes Versus Empirical Bayes Methods , 2003 .

[32]  Erdong Chen,et al.  Modeling safety of highway work zones with random parameters and random effects models , 2014 .

[33]  Peng Chen,et al.  Built environment factors in explaining the automobile-involved bicycle crash frequencies: a spatial statistic approach , 2015 .

[34]  Bo Song,et al.  Macro and micro models for zonal crash prediction with application in hot zones identification , 2016 .

[35]  Luc Int Panis,et al.  Predicting cycling accident risk in Brussels: a spatial case-control approach. , 2014, Accident; analysis and prevention.

[36]  Tarek Sayed,et al.  Application of generalized link functions in developing accident prediction models , 2008 .

[37]  Qiang Zeng,et al.  Bayesian spatial joint modeling of traffic crashes on an urban road network. , 2014, Accident; analysis and prevention.

[38]  Tarek Sayed,et al.  Evaluating the Impact of Socioeconomics, Land Use, Built Environment, and Road Facility on Cyclist Safety , 2017 .

[39]  Chandra R. Bhat,et al.  On Accommodating Spatial Dependence in Bicycle and Pedestrian Injury Counts by Severity Level , 2013 .

[40]  Fred L Mannering,et al.  A study of factors affecting highway accident rates using the random-parameters tobit model. , 2012, Accident; analysis and prevention.

[41]  Pengpeng Xu,et al.  Modeling crash spatial heterogeneity: random parameter versus geographically weighting. , 2015, Accident; analysis and prevention.

[42]  Majid Sarvi,et al.  Macroscopic modeling of pedestrian and bicycle crashes: A cross-comparison of estimation methods. , 2016, Accident; analysis and prevention.

[43]  S. Wong,et al.  Incorporating temporal correlation into a multivariate random parameters Tobit model for modeling crash rate by injury severity , 2018 .

[44]  L. Miranda-Moreno,et al.  Cyclist activity and injury risk analysis at signalized intersections: a Bayesian modelling approach. , 2013, Accident; analysis and prevention.

[45]  Paul P Jovanis,et al.  Analysis of Road Crash Frequency with Spatial Models , 2008 .