关键词: 交通运输工程；事故严重程度分析；摩托车事故；时空综合效应；贝叶斯推断

Abstract: Aiming at the injury severity of motorcycle crashes involving non-helmeted riders, various kinds of Bayesian spatiotemporal logistic models were established to systematically evaluate the comprehensive impact of driver, vehicle, road, and environmental characteristics. Based on relevant data from 5,447 crashes from 2 015 to 2 019, five kinds of models incorporating spatial, temporal, and spatiotemporal interaction effects were established and the Markov chain Monte Carlo method was used to carry out parameter estimation. Results show that the two-component mixed model comprehensively considering Leroux CAR spatial prior, temporal random walk, and spatio-temporal interaction effect achieves the best performance, achieving a classification accuracy of 86.74% and a 3% reduction in DIC value, which significantly outperforms other models and identifies rainfall as a significant risk factor at the first time. Further analysis reveals that older age, distracted driving, drug driving, high-speed driving, nighttime travel, and complex road conditions all significantly increase the severity of accidents, while low speed, urban roads, and certain distracted environments can mitigate risks to some extent. Research indicates that breaking through the assumption of spatial and temporal independence and introducing spatiotemporal interaction effects are of great significance for revealing complex risk patterns. The proposed model can offer a refined risk assessment tool for traffic management and provide a theoretical basis for formulating targeted safety strategies, such as strengthening education for elderly drivers and enforcing helmet wearing.

Key words: traffic and transportation engineering; accident severity analysis; motorcycle accidents; spatiotemporal comprehensive effect; Bayesian inference