Optimization for Express/Local Train Stop Plans on City Rapid Rail Transit Lines
[Objective] To minimize passenger travel time while reducing operational costs for enterprises, it is necessary to identify an optimal balance between these two competing objectives. A systematic study on the stop plan for express/local trains on city rapid rail transit lines should be conducted bas...
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| Main Author: | |
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| Format: | Article |
| Language: | zho |
| Published: |
Urban Mass Transit Magazine Press
2025-07-01
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| Series: | Chengshi guidao jiaotong yanjiu |
| Subjects: | |
| Online Access: | https://umt1998.tongji.edu.cn/journal/paper/doi/10.16037/j.1007-869x.20245725.html |
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| Summary: | [Objective] To minimize passenger travel time while reducing operational costs for enterprises, it is necessary to identify an optimal balance between these two competing objectives. A systematic study on the stop plan for express/local trains on city rapid rail transit lines should be conducted based on both passenger travel costs and enterprise operational costs. [Method] Based on the characteristics of city rapid rail transit lines, a bi-objective nonlinear optimization model is developed, aiming to minimize passenger travel time and enterprise operational costs. According to genetic algorithm theory, a corresponding genetic algorithm program is constructed and the model is solved. Using an actual city rapid rail transit line as a study case, passenger flow OD (origin-destination) data during the morning peaks on a typical working day is used as the input. The optimal express/local train stop plan is obtained after line optimization using genetic algorithm. A comparative analysis is conducted on enterprise operational costs and passenger travel costs before and after the stop plan optimization. [Result & Conclusion] The stop plan verifies the effectiveness of the proposed model and algorithm. While the optimized stop plan for express/local trains on city rapid rail transit lines reduces passenger travel costs by 19.48%, increases enterprise operational costs by 4.23%, and leads to an overall cost reduction of 4.18%. Although the algorithm slightly increases operational costs for enterprises, it significantly reduces passenger travel time costs and improves overall passenger travel accessibility. |
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| ISSN: | 1007-869X |