Quantifying Taxi Drivers' Behaviors with Behavioral Game Theory.

Mengyu Ji, Yuhong Xu,Shih-Fen Cheng

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)(2023)

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摘要
With their flexibility and convenience, taxis play a vital role in urban transportation systems. Understanding how human drivers make decisions in a context of uncertainty and competition is crucial for taxi fleets that depend on drivers to provide their services. As part of this paper, we propose modeling taxi drivers' behaviors based on behavioral game theory. Based on real-world data, we demonstrate that the behavioral game theory model we select is superior to state-of-the-art baselines. These results provide a solid foundation for improving taxi fleet efficiency in the future.
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关键词
Game Theory,Behavioral Theory,Taxi Drivers,Behavioral Game Theory,Behavioral Model,Real-world Data,Urban Transportation Systems,Decision-making Process,Population Level,Iterative Process,Poisson Distribution,Transition Probabilities,Distribution Of Levels,Best Response,Reward Function,Rational Agents,Markov Decision Process,Nash Equilibrium,Iterative Learning,Strategic Thinking,Mixed Estimates,Supply Distribution,Probability Of Strategy,Synthetic Experiments,Transportation Research,Backward Induction
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