市域快速轨道交通线路快慢车停站方案优化研究
郭靖凡
Optimization for Express/Local Train Stop Plans on City Rapid Rail Transit Lines
GUO Jingfan
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作者信息:广州地铁集团有限公司, 510310, 广州
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Affiliation:Guangzhou Metro Group Co., Ltd., 510310, Guangzhou, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.20245725
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中图分类号/CLCN:U239.5
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栏目/Col:运营管理
摘要:
［目的］为了最大限度缩短乘客出行时间，降低企业运营成本，需寻求两者博弈的平衡点。应基于乘客出行成本与企业运营成本，对市域快速轨道交通线路快慢车停站方案进行系统性研究。［方法］基于市域快速轨道交通线路特性，建立了以乘客出行时间最少及企业运营成本最低为目标的市域快速轨道交通线路双目标非线性优化模型。根据遗传算法理论，构建了模型的遗传算法程序并求解模型。以某市域快速轨道交通线路为例，将该线某工作日早高峰小时的OD(起讫点)客流数据作为输入条件，通过遗传算法得到该线优化后的快慢车最佳停站方案。对比分析了停站方案优化前后的企业运营成本及乘客出行成本。［结果及结论］停站方案结果验证了所建模型和算法的有效性。采用优化后的市域快速轨道交通线路快慢车停站方案，乘客出行成本降低了19.48%，企业运营成本增加了4.23%，总成本降低了4.18%。该算法虽导致企业运营成本小幅增加，但有效降低了乘客出行时间成本，提升了乘客出行的可达性。
Abstracts:
［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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