Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand
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 c...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8674512 |
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| _version_ | 1832547146476290048 |
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| author | Yongjian Yang Xintao Wang Yuanbo Xu Qiuyang Huang |
| author_facet | Yongjian Yang Xintao Wang Yuanbo Xu Qiuyang Huang |
| author_sort | Yongjian Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-52dad6395785467ba85b1dc305fef04d |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-52dad6395785467ba85b1dc305fef04d2025-02-03T06:45:54ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/86745128674512Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and DemandYongjian Yang0Xintao Wang1Yuanbo Xu2Qiuyang Huang3School of Computer Science and Technology, Jilin University, Changchun 130012, ChinaSchool of Computer Science and Technology, Jilin University, Changchun 130012, ChinaSchool of Computer Science and Technology, Jilin University, Changchun 130012, ChinaSchool of Computer Science and Technology, Jilin University, Changchun 130012, ChinaWith 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.http://dx.doi.org/10.1155/2020/8674512 |
| spellingShingle | Yongjian Yang Xintao Wang Yuanbo Xu Qiuyang Huang Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand Journal of Advanced Transportation |
| title | Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand |
| title_full | Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand |
| title_fullStr | Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand |
| title_full_unstemmed | Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand |
| title_short | Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand |
| title_sort | multiagent reinforcement learning based taxi predispatching model to balance taxi supply and demand |
| url | http://dx.doi.org/10.1155/2020/8674512 |
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