Discretionary Lane-Changing Decision Making Framework Combining Cumulative Prospect Theory and Discrete Choice Model

Analyzing discretionary lane-changing (DLC) behavior can provide support for intelligent connected vehicle driving behavior modeling and microscopic traffic simulation. The discrete choice model is the basic model in DLC modeling. However, the discrete choice model cannot fully consider risk preferences during the DLC decision making process. This study harnesses cumulative prospect theory to consider risk preference, constructing a feature vector that includes lane-changing benefits, lane-changing risks, and driving style of drivers. Based on this, a DLC decision making framework based on the discrete choice model is proposed. The framework considers risk preference in the DLC decision making process. It not only retains the interpretability of the discrete choice model but also enhances the predictive performance of DLC. The DLC decision-making framework proposed in this study is validated through the Next Generation Simulation (NGSIM) dataset. The results show that the DLC decision making framework proposed in this study can achieve better performance than the discrete choice model, with an accuracy reaching 77.25% in the test set.