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      Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand

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      Journal of Advanced Transportation

      Hindawi Limited

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          Abstract

          With the improvement of people’s living standards, people’s demand of traveling by taxi is increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their operational experience or cruise randomly to find passengers. Without macroguidance, the role of the taxi system cannot be fully utilized. Many scholars have studied taxi behaviors to find better operational strategies for drivers, but their researches rely on local optimization methods to improve the profit of drivers, which will lead to imbalance between supply and demand in the city. To solve this problem, we propose a Multiagent Reinforcement Learning- (MARL-) based taxi predispatching model through analyzing the running data of 13,000 taxis. Different from other methods of scheduling taxis based on the real-time location of orders, our model first predicts the demand for taxis in different regions in the next period and then dispatches taxis in advance to meet the future requirement; thus, the number of taxis needed and available in different regions can be balanced. Besides, in order to reduce computational complexity, we propose several methods to reduce the state space and action space of reinforcement learning. Finally, we compare our method with another taxi dispatching method, and the results show that the proposed method has a significant improvement in vehicle utilization rate and passenger demand satisfaction rate.

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          Most cited references 18

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          Current trends in Smart City initiatives: Some stylised facts

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            An Information Framework for Creating a Smart City Through Internet of Things

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              T-Finder: A Recommender System for Finding Passengers and Vacant Taxis

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                Author and article information

                Journal
                Journal of Advanced Transportation
                Journal of Advanced Transportation
                Hindawi Limited
                0197-6729
                2042-3195
                February 19 2020
                February 19 2020
                : 2020
                : 1-12
                Affiliations
                [1 ]School of Computer Science and Technology, Jilin University, Changchun 130012, China
                Article
                10.1155/2020/8674512
                © 2020

                http://creativecommons.org/licenses/by/4.0/

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