Energy-efficient train timetable optimization for tram system under multi-signal priority strategies
Abstract Energy-efficient operation is an effective approach for diminishing the operational expenses and carbon emissions of the tram system. Tram operation strategies and signal priority play a vital role in energy-efficient train timetable optimization. However, the impact of trams on road traffi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-91891-3 |
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| Summary: | Abstract Energy-efficient operation is an effective approach for diminishing the operational expenses and carbon emissions of the tram system. Tram operation strategies and signal priority play a vital role in energy-efficient train timetable optimization. However, the impact of trams on road traffic efficiency has limited consideration in traditional optimization methods. This paper develops a bi-objective mixed-integer linear programming (MILP) model incorporating multi-signal priority strategies (e.g., active signal priority strategy, no-signal priority strategy) and road traffic flow to obtain an energy-saving operation scheme. First, the signal priority strategy that trams should adopt based on the intersection traffic flow is determined to improve road traffic efficiency. Second, the speed profiles and stop plans of trams at the intersections are considered to reduce energy consumption and improve passenger comfort. The bi-objective MILP model can be solved by a commercial solver like CPLEX. A variety of comparative experiments using Lijiang Tram Line 1 in China are able to verify the effectiveness of the model. Compared with the timetable obtained by the active signal priority strategy (initial timetable), the proposed model could achieve the total energy consumption reduced by 5.01%, passenger comfort improved by 8.23%, and the number of vehicles delayed reduced by 27.21%. |
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| ISSN: | 2045-2322 |