Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models. (September 2018)

• Record Type: Journal Article
• Authors: Saha, Dibakar; Alluri, Priyanka; Gan, Albert; Wu, Wanyang
• Is Part Of: Accident analysis and prevention. Volume 118(2018)
• Page Start: 166
• Page End: 177
• Publication Date: 2018-09
• Subjects: Bicycle safety -- Conditional autoregressive model -- Besag's model -- Leroux's model -- STRAVA bicycle ridership data; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2018.02.014
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Bicycle crash frequency models were developed for census block groups in Florida. Two specifications of conditional autoregressive models were examined within the Bayesian framework. Census, roadway, traffic, and bicycle trip data were investigated to find their impacts on bicycle crash frequency. Besag's models outperformed Leroux's models. Bicycle trip miles and bicycle trip intensity obtained from Strava were found to have credible impact. Abstract: The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag's model and the Leroux's model, in crash prediction. The Besag's models, which differ from the Leroux's models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies.