Reinforcement learning in transportation research: Frontiers and future directions
The development of reinforcement learning (RL) provides innovative solutions for various decision-making problems in transportation, often pertaining to integrating advanced vehicular technologies such as connected and autonomous vehicles and electric vehicles. This paper reviews transportation rese...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Multimodal Transportation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772586324000455 |
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| author | Xiongfei Lai Zhenyu Yang Jiaohong Xie Yang Liu |
| author_facet | Xiongfei Lai Zhenyu Yang Jiaohong Xie Yang Liu |
| author_sort | Xiongfei Lai |
| collection | DOAJ |
| description | The development of reinforcement learning (RL) provides innovative solutions for various decision-making problems in transportation, often pertaining to integrating advanced vehicular technologies such as connected and autonomous vehicles and electric vehicles. This paper reviews transportation research with RL-based methods over the recent decades. We start with a bibliometric analysis through extensive literature retrieval of 1030 papers from 1996 to 2023. We identify different research areas of RL in transportation, summarizing the most visited research problems. We find that, at the vehicle level, motion and route planning and energy-efficient driving problems have attracted the most attention. Meanwhile, adaptive traffic signal control and management have been the most visited problems at the network level. We discuss several potential future directions, including the migration of RL models from simulations to real-world cases, designing tailored control architectures for complex transportation systems, exploring explainable RL in transportation research to ensure transparency and accountability in decision-making processes, and integrating people and vehicles into transportation systems in a sustainable and equitable manner. |
| format | Article |
| id | doaj-art-c5a6cfa2996e417ab676766296abc3ad |
| institution | Kabale University |
| issn | 2772-5863 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Multimodal Transportation |
| spelling | doaj-art-c5a6cfa2996e417ab676766296abc3ad2024-12-08T06:13:31ZengElsevierMultimodal Transportation2772-58632024-12-0134100164Reinforcement learning in transportation research: Frontiers and future directionsXiongfei Lai0Zhenyu Yang1Jiaohong Xie2Yang Liu3Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore, 117576, SingaporeDepartment of Industrial Systems Engineering and Management, National University of Singapore, Singapore, 117576, Singapore; Urban Transport Systems Laboratory (LUTS), Ecole Polytechnique F´ed´erale de, Lausanne (EPFL), Lausanne, CH-1015, SwitzerlandDepartment of Industrial Systems Engineering and Management, National University of Singapore, Singapore, 117576, SingaporeDepartment of Industrial Systems Engineering and Management, National University of Singapore, Singapore, 117576, Singapore; Department of Civil and Environmental Engineering, National University of Singapore, Singapore, 117576, Singapore; Corresponding author.The development of reinforcement learning (RL) provides innovative solutions for various decision-making problems in transportation, often pertaining to integrating advanced vehicular technologies such as connected and autonomous vehicles and electric vehicles. This paper reviews transportation research with RL-based methods over the recent decades. We start with a bibliometric analysis through extensive literature retrieval of 1030 papers from 1996 to 2023. We identify different research areas of RL in transportation, summarizing the most visited research problems. We find that, at the vehicle level, motion and route planning and energy-efficient driving problems have attracted the most attention. Meanwhile, adaptive traffic signal control and management have been the most visited problems at the network level. We discuss several potential future directions, including the migration of RL models from simulations to real-world cases, designing tailored control architectures for complex transportation systems, exploring explainable RL in transportation research to ensure transparency and accountability in decision-making processes, and integrating people and vehicles into transportation systems in a sustainable and equitable manner.http://www.sciencedirect.com/science/article/pii/S2772586324000455Reinforcement learningTransportation research frontiersLiterature reviewEmerging technologiesFuture applications |
| spellingShingle | Xiongfei Lai Zhenyu Yang Jiaohong Xie Yang Liu Reinforcement learning in transportation research: Frontiers and future directions Multimodal Transportation Reinforcement learning Transportation research frontiers Literature review Emerging technologies Future applications |
| title | Reinforcement learning in transportation research: Frontiers and future directions |
| title_full | Reinforcement learning in transportation research: Frontiers and future directions |
| title_fullStr | Reinforcement learning in transportation research: Frontiers and future directions |
| title_full_unstemmed | Reinforcement learning in transportation research: Frontiers and future directions |
| title_short | Reinforcement learning in transportation research: Frontiers and future directions |
| title_sort | reinforcement learning in transportation research frontiers and future directions |
| topic | Reinforcement learning Transportation research frontiers Literature review Emerging technologies Future applications |
| url | http://www.sciencedirect.com/science/article/pii/S2772586324000455 |
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