To evaluate the safety conditions of expressway exit areas with different exit forms, a safety analysis method based on the conflict extreme model is proposed. Based on video data from four typical urban expressway diversion areas, longitudinal conflicts and lateral conflicts were identified using time to collision and cross time to collision methods, respectively, and traffic parameters such as traffic flow were extracted. A Bayesian hierarchical conflict extreme model for joint conflict types was constructed by integrating the peak over threshold and Bayesian hierarchical approach. The threshold for conflicts was determined using the mean residual life plot and the threshold stability plot. Subsequently, a logarithmic link function for the scale parameter was established by combining different covariates. Then, the model parameters were estimated using the Markov chain Monte Carlo simulation method. Finally, based on the fitted distribution, a comprehensive safety evaluation and influential factor analysis were conducted for each diversion area. The results show that an increase in traffic flow and the ratio of large vehicles leads to a higher probability of traffic accidents. However, an increase in the average speed reduces the probability of traffic accidents. The construction form of the diversion area and the number of off-ramp lanes do not demonstrate an explicit relationship with the probability of traffic accidents. The research conclusion indicates that the Bayesian hierarchical extreme model can effectively utilize short-term observed conflicts for long-term crash estimation, and it has the potential to be applied in the field of safety evaluation.
Safety Evaluation and Influential Factor Analysis of Urban Expressway Diversion Areas Based on Non-Stationary Conflict Extremes
Transportation Research Record: Journal of the Transportation Research Board
Transportation Research Record: Journal of the Transportation Research Board ; 2678 , 12 ; 1813-1825
2024-06-06
Article (Journal)
Electronic Resource
English
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