Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning
Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/6654254 |
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| _version_ | 1832550906929872896 |
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| author | Sung-Jung Wang S. K. Jason Chang |
| author_facet | Sung-Jung Wang S. K. Jason Chang |
| author_sort | Sung-Jung Wang |
| collection | DOAJ |
| description | Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient algorithm for this large-scale dynamic optimization problem. An agent-based simulation platform was developed to model the dynamic system of a fixed stop/station loop route, autonomous bus fleet, and passengers. This platform was also applied to assess the performance of the proposed algorithm. The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. The simulation results also reveal the effectiveness of our proposed algorithm in outperforming the existing scheduled bus system in terms of the bus fleet size and passenger wait times for bus routes with comparatively lesser number of passengers. |
| format | Article |
| id | doaj-art-5dbeed88e5a14c48b9533c965e1fe027 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-5dbeed88e5a14c48b9533c965e1fe0272025-02-03T06:05:33ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66542546654254Autonomous Bus Fleet Control Using Multiagent Reinforcement LearningSung-Jung Wang0S. K. Jason Chang1Department of Civil Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, TaiwanDepartment of Civil Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, TaiwanAutonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient algorithm for this large-scale dynamic optimization problem. An agent-based simulation platform was developed to model the dynamic system of a fixed stop/station loop route, autonomous bus fleet, and passengers. This platform was also applied to assess the performance of the proposed algorithm. The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. The simulation results also reveal the effectiveness of our proposed algorithm in outperforming the existing scheduled bus system in terms of the bus fleet size and passenger wait times for bus routes with comparatively lesser number of passengers.http://dx.doi.org/10.1155/2021/6654254 |
| spellingShingle | Sung-Jung Wang S. K. Jason Chang Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning Journal of Advanced Transportation |
| title | Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning |
| title_full | Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning |
| title_fullStr | Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning |
| title_full_unstemmed | Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning |
| title_short | Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning |
| title_sort | autonomous bus fleet control using multiagent reinforcement learning |
| url | http://dx.doi.org/10.1155/2021/6654254 |
| work_keys_str_mv | AT sungjungwang autonomousbusfleetcontrolusingmultiagentreinforcementlearning AT skjasonchang autonomousbusfleetcontrolusingmultiagentreinforcementlearning |