多网融合条件下市域列车开行方案编制方法

李依娜; 孟学雷; 秦永胜; 韩正; 王越

doi:10.3963/j.jssn.1674-4861.2023.02.014

多网融合条件下市域列车开行方案编制方法

doi: 10.3963/j.jssn.1674-4861.2023.02.014

李依娜^1,,
孟学雷^1, ,,
秦永胜²,
韩正¹,
王越¹

1.
兰州交通大学交通运输学院兰州 730070
2.
中铁上海设计院集团有限公司上海 200070

基金项目:

国家自然科学基金项目 71861022

甘肃省教育厅优秀研究生“创新之星”项目 2022CXZX-579

详细信息

作者简介:
李依娜（1997—），硕士研究生. 研究方向：轨道交通运行管理、决策优化.E-mail：3384337472@qq.com

通讯作者:
孟学雷（1979—），博士，教授. 研究方向：轨道交通运行管理、决策优化. E-mail：mxl@mail.lzjtu.cn

中图分类号: U239.1
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- 文章访问数: 558
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- 被引次数: 0
出版历程
- 收稿日期: 2022-06-28
- 网络出版日期: 2023-06-19

A Method for Developing Operation Plans of Community Railways under the Condition of Multi-network Integration

1.
School of Traffic and Transportation, Lanzhou Jiaotong University, Gansu Lanzhou 730070, China
2.
China Railway Shanghai Design Institute group corporation limited, Shanghai 200070, China

摘要

摘要: 为解决铁路系统中市域铁路与其他制式轨道交通之间列车的到发时序及运能的匹配性问题，研究了多网融合条件下的市域列车开行方案编制方法。考虑枢纽站的跨制式换乘客流需求，根据客流的时间分布特征及不同乘客的时间价值对客流精细划分，以最小化列车总运营成本、旅客广义总出行成本、跨制式换乘客流对研究线路的短时冲击性为目标；考虑列车满载率、各时段区间通过能力、各类时间间隔、列车到发时刻、不同时段各类旅客的换乘协调性等约束，构建了分时段精细化编制列车开行方案的多目标非线性整数规划模型。采用线性加权法将多目标转化为单目标，引入模糊精英保留策略并对逃逸能量更新策略进行非线性化处理，以改进基本哈里斯鹰算法，降低算法陷入局部最优解的可能，提高模型的求解质量。以台州市域铁路S1线及其衔接高速铁路的相关数据为例进行实验，结果表明：与算法初始解相比，模型的总目标函数值降低36.7%；与基本哈里斯鹰算法相比，列车运营成本、旅客总广义出行成本、模型总目标函数值分别降低8.09%，7.04%，7.56%。验证了所设计的算法适合模型的特点，且具有较高的求解质量；所建模型可在不同时段兼顾列车运能与乘客换乘效率，有助于提升多网融合条件下不同制式轨道交通之间的协同作用。
- 交通规划 /
- 市域铁路 /
- 列车开行方案 /
- 哈里斯鹰算法 /
- 多网融合
Abstract: A method for developing operation plans of community railways under the condition of multi-network integration is studied, in order to match arrival-departure time sequence and capacity between community railways and other types of railways. The flows of cross-mode transfer are considered and divided according to its time distribution and the value of time of passengers. A multi-objective nonlinear integer programming model, whose objective is to minimize train operation cost, generalized total travel cost of passengers, and the short-term impact of cross-mode transfer on the studied railway line, is developed with the constraints of load rate, capacity, time interval, arrival-departure time of trains and coordination of transfers of passengers. A linear weighted method is used to convert the multi-objective problem into a single-objective one. An improved Harris Hawks optimization, where a fuzzy elitism strategy and a nonlinear escape energy updating strategy are introduced, is designed to avoid local optimum solution, and improve the quality of solution. The data from Line S1 railway of the City of Taizhou and its connecting high-speed railways is taken as the case study. Study results show that the value of the objective function of the model decreases by 36.7%, compared with its initial solution. Compared with the benchmark Harris Hawks optimization, the train operation cost, the total generalized travel cost, and generalized objective of the model decreases by 8.09%, 7.04%, and 7.56%, respectively. These results prove that the designed algorithm is suitable and has a higher accuracy. Within the proposed model, train capacity and efficiency of passenger transfer in different time periods have been taken into account in the model, which is helpful to improve the coordination among different railway lines under the condition of multi-network integration.
- traffic planning /
- community railway /
- train operation plan /
- Harris Hawks optimization /
- multi-network integration

HTML全文

图 1 旅客的同站换乘过程

Figure 1. The passenger's transfer process at the same station

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图 2 多网融合条件下市域铁路客流结构

Figure 2. Regional passenger flow structure under the condition of multi-network integration

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图 3 逃逸能量的变化过程

Figure 3. The process of changing the escape energy

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图 4 初始解生成步骤

Figure 4. The initial solution generation step

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图 5 算法流程图

Figure 5. Algorithm flow

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图 6 市域铁路站间距及运行时分

Figure 6. Station spacing and running time of regional railway

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图 7 算法迭代过程

Figure 7. Algorithm iteration process

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图 8 市域铁路S1线上行方向列车运行图

Figure 8. Train timetable on the upward direction of line S1 of regional railway

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图 9 算法收敛代数对比

Figure 9. Comparison of convergence iterations of algorithms

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表 1 高峰小时各区间断面客流量

Table 1. Number of section passenger flow during peak hours

车站	断面客流量/人	车站	断面客流量/人
S1	7 243	S9	5 205
S2	7 243	S10	4 844
S3	8 550	S11	4 273
S4	8 546	S12	3 424
S5	8 508	S13	2 502
S6	7 349	S14	1 645
S7	6 417	S15	822
S8	5 662

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表 2 style="class: table_bottom_border"

Table 2. The arrival time and transfer passenger flow of high-speed trains in the study period

车次	到达时刻	换乘客流量/人	车次	到达时刻	换乘客流量/人
G7711	08：13	246	D3145	10：08	219
G7541	09：04	224	D3145	10：13	258
D3239	09：32	225	G7543	10：23	214

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表 3 不同客流时段列车的开行频率对比

Table 3. Comparison of train operation frequency in different passenger flow periods 单位: 列/h

研究时段分布		高速列车到达强度	不考虑跨制式换乘客流需求	考虑跨制式换客流需求	增减情况
高峰时段	06：30—07：30	0	8	8	0
高峰时段	07：30—08：30	1	9	9	0
平峰时段	08：30—09：30	1	7	7	0
平峰时段	09：30—10：30	4	6	7	+1

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表 4 不同算法计算结果对比分析

Table 4. Comparative analysis of calculation results of different algorithms

算法	Z₁/元	Z₂/元	^Z3	Z
改进HHO	243 768	316 340	0.30	0.29
基本HHO	265 228	340 290	0.30	0.365 6

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