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

作　　者：Sixuan Xu Mengyun Li Wei Zhou Jiyang Zhang Chen Wang 

机构地区：[1]School of Transportation,Southeast University,2 Southeast University Road,Nanjing 211189,Jiangsu,PR China

出　　处：《Digital Transportation and Safety》2024年第3期115-125,共11页数字交通与安全（英文）

基　　金：supported by the National Key R&D Program of China(2023YFE0106800);the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX24_0100).

摘　　要：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.

关 键 词：Mandatory lane change Evolutionary game theory Physics-informed machine learning 

分 类 号：U495[交通运输工程—交通运输规划与管理] TP181[交通运输工程—道路与铁道工程]