[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121311-en":3,"doc-seo-121311-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121311,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","An Evolutionary Game Theory-Based Machine Learning Framework for Predicting Mandatory Lane Change Decision - Study","Mandatory lane change (MLC) can trigger traffic oscillations that reduce efficiency and compromise safety. Research on mandatory lane change decision (MLCD) prediction is expanding, with methods split into physics-based and machine-learning approaches. This study builds a hybrid EGTML framework by integrating an Evolutionary Game Theory model for interpretable, data-efficient driver interaction modeling with machine learning models for high predictive accuracy. Generalization is validated using ANN, RF, LightGBM, and XGBoost on real-world NGSIM data. Sensitivity analysis shows stronger performance, particularly under sparse data.","ARTICLE  \n\n| Open Access [https://doi.org/10.48130/dts-0024-001](https://doi.org/10.48130/dts-0024-001)1\u003Cbr>Digital Transportation and Safety 2024, 3(3): 115−125\u003Cbr>An evolutionary game theory-based machine learning framework for predicting mandatory lane change decision\u003Cbr>Sixuan Xu, Mengyun Li, Wei Zhou, Jiyang Zhang and Chen Wang*\u003Cbr>School of Transportation, Southeast University, 2 Southeast University Road, Nanjing 211189, Jiangsu, PR China\u003Cbr>* Corresponding author, [E-mail: wkobec@hotmail.com](E-mail: wkobec@hotmail.com) |\n| --- |\n| Abstract\u003Cbr>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 machinelearning 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 realworld 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.\u003Cbr>Keywords: Mandatory lane change; Evolutionary game theory; Physics-informed machine learning |\n| Citation: XU S, LI M, ZHOU W, ZHANG J, WANG C. 2024. An evolutionary game theory-based machine learning framework for predicting mandatory\u003Cbr>lane change decision. Digital Transportation and Safety 3(3): 115−125 [https://doi.org/10.48130/dts-0024-001](https://doi.org/10.48130/dts-0024-001)1 |\n\nIntroduction  \nMandatory lane change (MLC) refers to the behavior that the driver must change the current lane to the expected lane in some places due to traffic regulations or his/her driving needs. MLC usually occurs in expressway weaving areas, on and offramps, and the entrance to intersections. Compared with discretionary lane changing (DLC, e.g., the lane changing behavior taken by the drivers to improve the current driving environment), MLC is more likely to cause traffic oscillations, which have a negative impact on traffic efficiency and safety[1,2] . Therefore, analyzing, modeling, and predicting mandatory lane-changing behavior is important for improving road traffic safety and efficiency.  \nIn the past decade, there has been a rapid increase in research on lane change modeling, especially on mandatory lane change decision (MLCD) prediction[3−5] . MLCD models can be categorized into two types, physics-based models and machine-learning models. Early physics-based MLCD models started from the classic rule-based models (e.g., Gipps[6], MITSIM[7], MOBIL[8]), and utility-based models[9], which imitated human drivers' activities towards lane-changing. However, challenging function expressions and complicated parameters make these models more difficult to calibrate and validate. The lane-changing process involves dynamic interaction between drivers, that is, one driver pays the cost (e.g., speed, space) and the other driver benefits from it (e.g., acceleration, lane change) . Game theory (GT), one of the most frequent applications of simulating the process of human competitive and cooperative behaviors, can better describe the interaction  \nbetween drivers. Thus, there have been many MLCD models integrated with GT[10","cbCaij1msMN3uzBV","https://ap.wps.com/l/cbCaij1msMN3uzBV","pdf",1826563,1,11,"English","en",105,"# Introduction\n## Background and motivation\n## Review of physics-based and machine-learning MLCD models\n## Need for hybrid physics-informed learning\n# Proposed EGTML framework\n## Evolutionary game theory for multi-style drivers\n## Hybrid architecture and prediction pipeline\n# Experimental validation\n## Generalization on real-world NGSIM data\n## Comparison with baseline machine learning models\n# Sensitivity analysis\n## Performance under sparse data","[{\"question\":\"What problem does the framework target?\",\"answer\":\"The framework targets mandatory lane change decision (MLCD) prediction to improve traffic efficiency and safety by addressing oscillations caused by mandatory lane changes.\"},{\"question\":\"How does the proposed EGTML framework differ from purely physics-based or purely machine-learning methods?\",\"answer\":\"It combines an Evolutionary Game Theory model to provide interpretable, data-efficient driver interaction information with machine learning models that deliver high prediction accuracy.\"},{\"question\":\"Which machine learning models are used to validate generalization, and how does the framework perform on sparse data?\",\"answer\":\"ANN, RF, LightGBM, and XGBoost are used for generalization validation. Sensitivity analysis shows EGTML outperforms general ML models, especially when data is sparse.\"}]","An Evolutionary Game Theory-Based Machine Learning Framework for Predicting Mandatory Lane Change Decision - Study | PDF",1785735021,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"an-evolutionary-game-theory-based-machine-learning-framework-for-predicting-mandatory-lane-change-decision-study","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-evolutionary-game-theory-based-machine-learning-framework-for-predicting-mandatory-lane-change-decision-study/121311/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the framework target?","Question",{"text":76,"@type":77},"The framework targets mandatory lane change decision (MLCD) prediction to improve traffic efficiency and safety by addressing oscillations caused by mandatory lane changes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed EGTML framework differ from purely physics-based or purely machine-learning methods?",{"text":81,"@type":77},"It combines an Evolutionary Game Theory model to provide interpretable, data-efficient driver interaction information with machine learning models that deliver high prediction accuracy.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning models are used to validate generalization, and how does the framework perform on sparse data?",{"text":85,"@type":77},"ANN, RF, LightGBM, and XGBoost are used for generalization validation. 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