A1275
Title: Application of Bayesian spatiotemporal models with Potts to analyze the incident rate of traffic accidents in Taiwan
Authors: Kuo-Jung Lee - National Cheng-Kung University (Taiwan)
Tzu Wei Yin - National Cheng Kung University (Taiwan) [presenting]
Abstract: A Bayesian spatiotemporal generalized linear regression framework incorporating a Potts model is developed to capture spatial dependence and latent regional clustering in traffic accident incidence and its associated risk factors. Bayesian inference is conducted via a Metropolis-within-Gibbs sampling algorithm. Results from simulation studies demonstrate the models effectiveness in identifying influential covariates and in recovering latent spatial heterogeneity structures along with their corresponding regression effects. The model is applied to traffic accident data from the Taiwan Open Government Data Platform, covering the period from January to December 2023. The analysis shows that variations in incident risk across 349 townships in Taiwan are attributable to spatial heterogeneity in key contributing factors. In particular, evidence of spatial clustering in environmental effects is observed in central and southern Taiwan.
