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A study of driver's route choice behavior based on evolutionary game theory
Xiaowei Jiang1, Yanjie Ji1, Muqing Du2
1School of Transportation, Southeast University, 35 Jinxianghe Road, Nanjing, Jiangsu 210096, China.
Computational Intelligence and Neuroscience
|January 23, 2015
Summary
This study integrates cumulative prospect theory and evolutionary game theory to understand how traffic information influences driver route choice. Findings show traffic information significantly impacts route decisions, optimizing travel efficiency.
Area of Science:
- Transportation Science
- Behavioral Economics
- Game Theory
Background:
- Driver route choice is complex, influenced by bounded rationality and incomplete traffic information.
- Existing models may not fully capture the dynamic decision-making processes under real-time information.
- Understanding these dynamics is crucial for effective traffic management systems.
Purpose of the Study:
- To develop and illustrate an analytic method for driver route choice behavior using cumulative prospect theory and evolutionary game theory.
- To investigate the impact of traffic information on route selection dynamics.
- To provide insights for optimizing traffic guidance systems.
Main Methods:
- Embedding cumulative prospect theory (CPT) within evolutionary game theory (EGT).
- Construction of an evolutionary game model for boundedly rational drivers.
- Simulation of a network with two routes and a variable message sign to analyze route choice adjustments.
Main Results:
- Traffic information plays a critical role in shaping driver route choice behavior.
- The driver's route decision-making evolves towards different stable states based on traffic conditions.
- The integrated CPT-EGT approach effectively models driver behavior and its response to information.
Conclusions:
- The proposed analytic method, combining CPT and EGT, is effective for studying driver route choice.
- Traffic information significantly influences route selection, leading to dynamic evolutionary stable states.
- This research offers academic support for traffic guidance systems aiming to improve travel efficiency.
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