An evolutionary game theory-based machine learning framework for predicting mandatory lane change decision

Abstract

Mandatory lane change (MLC) is likely to cause traffic oscillations, which have a negative impact on traffic efficiency and safety. There is a rapid increase in research on mandatory lane change decision (MLCD) prediction, which can be categorized into physics-based models and machine-learning models. Both types of models have their advantages and disadvantages. To obtain a more advanced MLCD prediction method, this study proposes a hybrid architecture, which combines the Evolutionary Game Theory (EGT) based model (considering data efficient and interpretable) and the Machine Learning (ML) based model (considering high prediction accuracy) to model the mandatory lane change decision of multi-style drivers (i.e. EGTML framework). Therefore, EGT is utilized to introduce physical information, which can describe the progressive cooperative interactions between drivers and predict the decision-making of multi-style drivers. The generalization of the EGTML method is further validated using four machine learning models: ANN, RF, LightGBM, and XGBoost. The superiority of EGTML is demonstrated using real-world data (i.e., Next Generation SIMulation, NGSIM). The results of sensitivity analysis show that the EGTML model outperforms the general ML model, especially when the data is sparse.

Graphical abstract

Keywords

Mandatory lane change / Evolutionary game theory / Physics-informed machine learning