Expressway Rear-End Conflict Pattern Classification and Modeling

Abstract

The study of expressway rear-end conflicts is of great significance to analyze driving behaviors and improve traffic safety. However, research on the classification and modeling of conflict patterns is still lacking. This study aimed to explore conflict patterns and their relationship with influencing factors. The conflict data used in this study was extracted from a trajectory data set that collected 3 h of data during a morning peak hour on an expressway in Shanghai, China. An improved k-means algorithm, which can automatically obtain the optimal number of clusters, was used to classify the conflict events into six conflict patterns. The conflict patterns were interpreted from five aspects: risk level, speed of risk-changing, risk-avoidance response, risk-avoidance attitude, and risk-avoidance action. Furthermore, a multivariate Poisson-lognormal (MVPLN) model considering spatial–temporal correlation was applied. The relationship between the independent variables and the number of each conflict pattern within the spatial–temporal unit was obtained. The root mean square error of the MVPLN model was 0.81. Compared with univariate Poisson model, univariate negative binomial model, and univariate Poisson-lognormal model, the MVPLN model improved by 73.8%, 81.3%, and 29.6% in accuracy respectively. The results of this study can classify expressway rear-end conflict patterns and obtain the number of each conflict pattern within spatial–temporal units using available traffic data.