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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%.

Keywords

Intelligent transportation systems driver’s propensity vehicle aggregation situation many-person multi-objective decision problems mixed fuzzy games

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The drivers’ lane selection model based on mixed fuzzy many-person multi-objective non-cooperative game

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