Development of optimal real‐time metro operation strategy minimizing total passenger travel time and train energy consumption
Abstract The optimization of the total passenger travel time and total train energy consumption are critical factors in metro operation optimization. However, deriving an optimal train operation plan that incorporates both passenger travel time and total train energy consumption is a complex task be...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Intelligent Transport Systems |
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| Online Access: | https://doi.org/10.1049/itr2.12582 |
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| author | Yoonseok Oh Ho‐Chan Kwak Seungmo Kang |
| author_facet | Yoonseok Oh Ho‐Chan Kwak Seungmo Kang |
| author_sort | Yoonseok Oh |
| collection | DOAJ |
| description | Abstract The optimization of the total passenger travel time and total train energy consumption are critical factors in metro operation optimization. However, deriving an optimal train operation plan that incorporates both passenger travel time and total train energy consumption is a complex task because it should consider numerous variables representing the operational status of the urban railway, such as the number of boarding and alighting passengers, number of on‐board passengers in each train, and entire train operation status along the line. Moreover, owing to the fluctuating nature of passenger demand, which can change rapidly over time, its optimization becomes challenging. To address this challenge, this study develops a recurrent neural network‐based real‐time metro operation optimization model trained using data representing the moments when the trains departed from the stations. These data are derived and reconstructed from various simulated operation plans while searching for optimal daily metro timetable. Consequently, the proposed model derives the real‐time optimal operation strategies for trains departing from the next station within an average of 0.18 s. The result of metro operation simulations using proposed optimal operation strategies reveals a 7–14% improvement in efficiency compared to the current train operation strategies. |
| format | Article |
| id | doaj-art-8d4797ce702746b9af7863e55dd96d39 |
| institution | OA Journals |
| issn | 1751-956X 1751-9578 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj-art-8d4797ce702746b9af7863e55dd96d392025-08-20T02:19:34ZengWileyIET Intelligent Transport Systems1751-956X1751-95782024-12-0118122440245810.1049/itr2.12582Development of optimal real‐time metro operation strategy minimizing total passenger travel time and train energy consumptionYoonseok Oh0Ho‐Chan Kwak1Seungmo Kang2School of Civil, Environmental, and Architectural Engineering Korea University Seoul Republic of KoreaRailroad Policy Research Team, Future Transport Policy Research Division Korea Railroad Research Institute Uiwang Republic of KoreaSchool of Civil, Environmental, and Architectural Engineering Korea University Seoul Republic of KoreaAbstract The optimization of the total passenger travel time and total train energy consumption are critical factors in metro operation optimization. However, deriving an optimal train operation plan that incorporates both passenger travel time and total train energy consumption is a complex task because it should consider numerous variables representing the operational status of the urban railway, such as the number of boarding and alighting passengers, number of on‐board passengers in each train, and entire train operation status along the line. Moreover, owing to the fluctuating nature of passenger demand, which can change rapidly over time, its optimization becomes challenging. To address this challenge, this study develops a recurrent neural network‐based real‐time metro operation optimization model trained using data representing the moments when the trains departed from the stations. These data are derived and reconstructed from various simulated operation plans while searching for optimal daily metro timetable. Consequently, the proposed model derives the real‐time optimal operation strategies for trains departing from the next station within an average of 0.18 s. The result of metro operation simulations using proposed optimal operation strategies reveals a 7–14% improvement in efficiency compared to the current train operation strategies.https://doi.org/10.1049/itr2.12582big dataoptimisationpublic transportrail trafficrail traffic controlrail transportation |
| spellingShingle | Yoonseok Oh Ho‐Chan Kwak Seungmo Kang Development of optimal real‐time metro operation strategy minimizing total passenger travel time and train energy consumption IET Intelligent Transport Systems big data optimisation public transport rail traffic rail traffic control rail transportation |
| title | Development of optimal real‐time metro operation strategy minimizing total passenger travel time and train energy consumption |
| title_full | Development of optimal real‐time metro operation strategy minimizing total passenger travel time and train energy consumption |
| title_fullStr | Development of optimal real‐time metro operation strategy minimizing total passenger travel time and train energy consumption |
| title_full_unstemmed | Development of optimal real‐time metro operation strategy minimizing total passenger travel time and train energy consumption |
| title_short | Development of optimal real‐time metro operation strategy minimizing total passenger travel time and train energy consumption |
| title_sort | development of optimal real time metro operation strategy minimizing total passenger travel time and train energy consumption |
| topic | big data optimisation public transport rail traffic rail traffic control rail transportation |
| url | https://doi.org/10.1049/itr2.12582 |
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