ABSTRACT: Macro-level traffic crash analyses and modeling are prevalent in many countries in order to incorporate traffic safety into long-term transportation planning. Due to the burgeoning urban development and hysteretic nature of data collection, however, many existing studies might be outdated and poorly adaptable. To address the problem, this study updated a macro-level safety model for 263 traffic analysis zones(TAZs) within the urban area of Shanghai. Independent variables for 2009 and 2016 from four categories were investigated to identify specific contributing factors for traffic crashes: socio-economic factors, traffic patterns, road characteristics, and land use features. A Bayesian conditional autoregressive negative binomial (CAR-NB) model was estimated to account for the spatial correlations among TAZs. The 2016 model was developed by using the two-stage Bayesian updating method to provide informative priors for 2009 model. Results show that higher crash frequency is associated with greater population, total length of major and minor arterials, trip frequencies, and shorter intersection spacing. The fact that most variables have similar significance for the two years is indicative of the good flexibility and interpretability of Bayesian CAR-NB model. Additionally, the informative priors are capable of providing theoretically based expectations without losing flexibility. This study helps to fill the gap in formulating informative priors for independent variables in macro-level traffic safety studies. Moreover, urban policy decision makers and traffic police can benefit from this study and implement area-wide engineering, education, and enforcement countermeasures to enhance regional traffic safety.
Minming Yang, Xuesong Wang, Meigen Xue. Macro-Level Traffic Safety Analysis and Model Updating in Shanghai, China. Transportation Research Board 99th Annual Meeting, Washington D.C., USA, 2020. 1.12-16.