Modeling and Analysis of Mandatory Merging Decision Behavior Considering Temporal and Spatial Risks

This paper analyzes the effects and potential causes of different variables on drivers’ mandatory merging decision behavior by constructing temporal and spatial dimensional merging risk indicators. This study employs the grouped latent class logit model (GLCM) and the grouped random parameter logit model with heterogeneity in means and variances (GRPMV) to analyze group heterogeneity and individual heterogeneity, respectively, and compared their goodness-of-fit and classification performance with other heterogeneity models. GLCM allows for the identification of latent classes among drivers, capturing unobserved heterogeneity by grouping drivers into distinct behavioral categories. Meanwhile, GRPMV provides a nuanced analysis of individual heterogeneity by accounting for variations in both means and variances of the parameters. The results suggest that merging risk has an important impact on the modeling of merging decision behavior, which can significantly improve the fitting performance of the model and effectively reveal unobserved group heterogeneity. In GRPMV, merging risk in the time dimension has the greatest impact on drivers’ merging decision behavior. In GLCM, individuals in different latent classes exhibit varying sensitivity to the presence of merging risk in merging situations. The parameter estimation results show the driving behavior characteristics of “conservative” and “aggressive” drivers. The results provide a theoretical basis for establishing a more comprehensive collision risk response identification function for autonomous vehicles in weaving areas and contribute to the enhancement of real-time decision-making processes in complex traffic scenarios.