Modeling Vehicle Merging Position Selection Behaviors Based on a Finite Mixture of Linear Regression Models

Abstract

Vehicle merging is a complex and tactical decision process. Merging position selection behavior has been largely ignored in microscopic traffic simulators. Driver heterogeneity has received substantial attention in recent years; however, few studies have focused on the heterogeneity in merging behaviors. To account for the heterogeneity among merging drivers during the merging process and to improve the accuracy of the merging model, a finite mixture of linear regression models was developed for describing the merging position selection model. BIC was used to determine the optimal number of classes, and Latent Gold 5.0 was used to estimate parameters. Based on the US101 data in the NGSIM project, which were provided by FHWA, a 3-class linear regression model was developed. The results demonstrate that the variables differ across the classes, and the sign of each variable may also differ among the classes; hence, the strategies that are used by drivers for merging position selection differ across the classes. Cooperative lane changing of the putative leading vehicle was found to have significant influence on the merging position selection behavior; thus, merging behavior is a two-dimensional behavior that may be influenced by both lateral and longitudinal factors. Compared with previous studies, the proposed model can naturally identify the heterogeneity among drivers and is much more accurate; therefore, the proposed model is a promising tool for microscopic traffic simulation and automatic driving systems or driver assistance systems.