Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models

Accident; Analysis and Prevention
Dibakar SahaWanyang Wu

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 ...Continue Reading

Citations

Feb 1, 2019·International Journal of Environmental Research and Public Health·Kyuhyun Lee, Ipek N Sener
Nov 27, 2020·Accident; Analysis and Prevention·Marcus Skyum MyhrmannStefan Eriksen Mabit
Apr 30, 2021·Accident; Analysis and Prevention·Shamsunnahar YasminNaveen Eluru
Jun 29, 2021·Accident; Analysis and Prevention·Mohamed Bayoumi Kamel, Tarek Sayed
Feb 1, 2020·Accident; Analysis and Prevention·Mohamed Bayoumi KamelAlexander Bigazzi

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