Predicting microscopic vehicle collision risks at toll plaza diverging area using bayesian dynamic logistic regressions

The absence of lane markings at toll plaza diverging areas results in frequent vehicle weaving motions, making these areas typical high-risk bottlenecks on highways. Existing conflict prediction methods often rely on historical data and static models, which lack adaptability to dynamic changing traffic conditions. This study proposes a Bayesian dynamic logistic regression approach capable of self-adaptive prediction of vehicle collision risks at toll plaza diverging areas. First, the aggregated traffic characteristics were extracted from the high-precision vehicle trajectory data and the indicator Extended Time-to-Collision (ETTC) was employed to measure multi-directional vehicle collision risks. Then, Bayesian dynamic logistic regression models were developed based on aggregated traffic characteristics from different sampling strategies. Results show that as the data volume increases, the Area Under the Curve (AUC) values of these models all gradually exceeds 0.9, demonstrating strong self-adaptive correction capabilities. Compared with standard logistic regression models, the Bayesian dynamic logistic regression models identified more influencing factors and required only 20% of the data for initialization, while continuously updating estimates with incoming data, significantly reducing computational resource demands for collision risk prediction. Furthermore, sensitivity analysis of the forgetting parameter indicates that incorporating richer prior information enhances predictive accuracy. These findings provide valuable insights for developing tailored management strategies to reduce potential traffic conflicts at toll plaza diverging areas.