基于客流需求的列车时刻表和车底调度协同优化

doi:10.3976/j.issn.1002-4026.2020.02.010

Abstract

Abstract: The demand for urban railways is increasing with the expansion of the scale of urban rail transit, resulting in a high requirement with respect to the operation quality of urban rail transit services. Given the demand of passengers, this study establishes a mixed integer model to minimize the total passenger waiting time and operational cost of the urban rail system. An annealing algorithm is simulated to design the train timetable and rolling stock schedule for a certain route, and calculations and examples are used to analyze their feasibility. The results denote that the proposed model can be used to design a proper train timetabling and rolling stock scheduling plan based on the passenger demand.

Key words: urban rail transit, train timetabling, rolling stock scheduling, simulated annealing algorithm, mixed integer programming, total passenger waiting time, cost of urban rail system

CLC Number:

U239.5

LIN Yu-tong, CAO Cheng-xuan, LIU Yu-tong, FENG Zi-yan. Collaborative optimization of train timetabling and rolling stock based on passenger demand[J].Shandong Science, 2020, 33(2): 63-70.

References 12

1	SCH-BEL A. An eigenmodel for iterative line planning, timetabling and vehicle scheduling in public transportation [J]. Transportation Research Part C: Emerging Technologies, 2017, 74: 348-365. doi: 10.1016/j.trc.2016.11.018
2	WANG Y H, D’ARIANO A, YIN J T, et al. Passenger demand oriented trainscheduling and rolling stock circulation planning for an urban rail transit line[J]. Transportation Research Part B: Methodological, 2018, 118: 193-227. doi: 10.1016/j.trb.2018.10.006
3	SHI J G, YANG L X, YANG J, et al. Service-oriented train timetabling with collaborative passenger flow control on an oversaturated metro line: an integer linear optimization approach [J]. Transportation Research Part B: Methodological, 2018, 110: 26-59. doi: 10.1016/j.trb.2018.02.003
4	ROBENEK T, AZADEH S S, MAKNOON Y, et al. Passenger centric train timetabling problem [J]. Transportation Research Part B: Methodological, 2016, 89: 107-126.
5	ROBENEK T, AZADEH S S, MAKNOON Y, et al. Train timetable design under elastic passenger demand [J]. Transportation Research Part B: Methodological, 2018, 111: 19-38. doi: 10.1016/j.trb.2018.03.002
6	YUE Y X, HAN J T, WANG S F, et al.Integrated train timetabling and rolling stock scheduling model based on time-dependent demand for urban rail transit[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(10): 856-873. doi: 10.1111/mice.12300
7	王世峰. 基于动态客流的城市轨道交通列车时刻表与车底运用一体化优化模型和算法研究[D]. 北京: 北京交通大学, 2016.
8	薛彤. 城市轨道交通列车开行计划调整优化方法研究[D]. 成都: 西南交通大学, 2018.
9	李峰. 网络环境下单线列车运行图编制的优化算法研究[D]. 北京：北京交通大学, 2010.
10	黎新华. 单线区段列车运行图铺划与运行调整优化方法研究[D]. 长沙：中南大学, 2005.
11	史峰, 黎新华，秦进，等. 单线列车运行图铺划的时间循环迭代优化方法[J].铁道学报,2005,27(1):1-5.
12	周翔翔, 刘鑫荣, 张永, 等. 基于遗传优化算法的城市轨道交通多列车运行节能计算方[J]. 城市轨道交通研究, 2018, 21(12): 67-70. doi: 10.16037/j.1007-869x.2018.12.015

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Collaborative optimization of train timetabling and rolling stock based on passenger demand

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