Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train
Optimal energy-efficient train operation optimization is one of the widely studied areas in transportation science, which can significantly reduce energy consumption that accounts for a large proportion of operating costs. In order to adapt to the complex and changeable railway line conditions such...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/5964428 |
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| _version_ | 1850178672008364032 |
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| author | Chengda Yang Kun Miao Jieyuan Wang |
| author_facet | Chengda Yang Kun Miao Jieyuan Wang |
| author_sort | Chengda Yang |
| collection | DOAJ |
| description | Optimal energy-efficient train operation optimization is one of the widely studied areas in transportation science, which can significantly reduce energy consumption that accounts for a large proportion of operating costs. In order to adapt to the complex and changeable railway line conditions such as gradient, slope length, and speed limit and avoid the error in tracking speed curve, an optimal driving strategy decision-making (ODSD) model is proposed in this paper. The model considers the non-fixed sequence of driving regimes, and the regimes are directly selected in the discrete micro-subsegments of an equal time-division pattern. To solve this model efficiently, an improved ant colony system algorithm with the difference edges (ACSd) is proposed, which takes the heuristic effect of the difference between the best solutions of two adjacent iterations, i.e., “the difference edges,” into account. Additionally, energy-efficient heuristic factor and speed heuristic factor are presented to balance energy saving and speed. The results demonstrate that ACSd performs better than the basic ant colony system algorithm in solving traveling salesman problem (TSP) and provides more flexible driving strategies for the ODSD model. |
| format | Article |
| id | doaj-art-c5cb310d1ddb4813a8e70c73be4fc3e4 |
| institution | OA Journals |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c5cb310d1ddb4813a8e70c73be4fc3e42025-08-20T02:18:39ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/5964428Driving Strategy Using an Improved Ant Colony System for Energy-Efficient TrainChengda Yang0Kun Miao1Jieyuan Wang2School of Civil EngineeringSchool of Civil EngineeringSchool of Civil EngineeringOptimal energy-efficient train operation optimization is one of the widely studied areas in transportation science, which can significantly reduce energy consumption that accounts for a large proportion of operating costs. In order to adapt to the complex and changeable railway line conditions such as gradient, slope length, and speed limit and avoid the error in tracking speed curve, an optimal driving strategy decision-making (ODSD) model is proposed in this paper. The model considers the non-fixed sequence of driving regimes, and the regimes are directly selected in the discrete micro-subsegments of an equal time-division pattern. To solve this model efficiently, an improved ant colony system algorithm with the difference edges (ACSd) is proposed, which takes the heuristic effect of the difference between the best solutions of two adjacent iterations, i.e., “the difference edges,” into account. Additionally, energy-efficient heuristic factor and speed heuristic factor are presented to balance energy saving and speed. The results demonstrate that ACSd performs better than the basic ant colony system algorithm in solving traveling salesman problem (TSP) and provides more flexible driving strategies for the ODSD model.http://dx.doi.org/10.1155/2024/5964428 |
| spellingShingle | Chengda Yang Kun Miao Jieyuan Wang Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train Journal of Advanced Transportation |
| title | Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train |
| title_full | Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train |
| title_fullStr | Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train |
| title_full_unstemmed | Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train |
| title_short | Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train |
| title_sort | driving strategy using an improved ant colony system for energy efficient train |
| url | http://dx.doi.org/10.1155/2024/5964428 |
| work_keys_str_mv | AT chengdayang drivingstrategyusinganimprovedantcolonysystemforenergyefficienttrain AT kunmiao drivingstrategyusinganimprovedantcolonysystemforenergyefficienttrain AT jieyuanwang drivingstrategyusinganimprovedantcolonysystemforenergyefficienttrain |