Affiliations: [a] School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China
| [b] State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China
| [c]
Shandong Zibo Experimental High School, Zibo, China
Correspondence:
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Corresponding author. Xiaoyuan Wang, School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China. Tel.: +86 533 2788586/+86 13 864455865; Fax: +86 533 2786837; E-mail: [email protected].
Abstract: The adoption of automobile safety assistance and driving alert systems is an effective way to ensure traffic safety because such applications can accurately predict vehicle aggregation situations. Improving the drivers’ lane selection process is not only the most fundamental reason for transforming vehicle aggregating but also the basic component of traffic flow research. However, the effects of factors such as the characteristics of individual vehicles and drivers, the types of manipulators used in complex vehicle aggregation situations, and the influence of vehicle conflict on lane selection have not been addressed in previous studies. This paper assesses the characteristics of various traffic manipulators, vehicles, and drivers to develop a lane selection model of basic urban expressway segments based on mixed fuzzy many-person, multi-objective, non-cooperative games. By analyzing drivers’ profits under different combinations of lane selection behavior, Nash equilibrium was confirmed in a single game process and optimal lane selection behavior was obtained in a dynamic game. The results show that the model’s prediction accuracy of lane changing is 85.2%.
