The train operation scheme is the basis of urban rail transport organization. During peak hours, metro operations are characterized by overcrowded and unevenly distributed passenger demand. In this paper, a short-turning strategy is proposed to optimize train operations in combination with passenger flow variations to address the problem of concentrated traffic congestion on long metro lines. Firstly, an optimization model is developed for minimizing the total passenger waiting for time cost and train operating cost, taking into account the actual passing capacity of the line and the current constraints of train operations. Simultaneously, a genetic algorithm is designed to optimize the locations of the turn-back stations and the optimal ratio of different routings during the morning peak hours. Finally, the validity of the model is verified using Nanning Metro Line 1 as a case study, and the sensitivity of positions of turn-back stations is also analyzed. The results showed that the opening of short-turning routing during peak hours is conducive to accelerating train turnover, reducing the number of vehicles employed, and balancing passenger flow. It saved passenger waiting time by 4% and reduced vehicle kilometers traveled by 7% compared with full-length routing. This strategy improves the uneven spatial load of trains during peak passenger flows and provides theoretical support and technical reference for the optimized travel commissioning of urban rail transit line networks.
HeD.LiuC.JinZ.MaR.ChenY.ShanS.Fault Diagnosis of Flywheel Bearing Based on Parameter Optimization Variational Mode Decomposition Energy Entropy and Deep Learning. Energy, Vol. 239, 2022, p. 122108. https://doi.org/10.1016/j.energy.2021.122108.
2.
YangD.HeQ.YiS.Bilevel Optimization of Intercity Railway Alignment. Transportation Research Record: Journal of the Transportation Research Board, 2021. 2675: 985–1002.
3.
KennedyC. A.A Comparison of the Sustainability of Public and Private Transportation Systems: Study of the Greater Toronto Area. Transportation, Vol. 29, No. 4, 2002, pp. 459–493. https://doi.org/10.1023/A:1016302913909.
4.
VasebiS.HayeriY. M.SamarasC.HendricksonC.Low-Level Automated Light-Duty Vehicle Technologies Provide Opportunities to Reduce Fuel Consumption. Transportation Research Record: Journal of the Transportation Research Board, 2018. 2672: 60–74. https://doi.org/10.1177/0361198118796401.
5.
LiZ.ZhaoJ.PengQ.Train Service Design in an Urban Rail Transit Line Incorporating Multiple Service Routes and Multiple Train Compositions. Transportation Research Part C: Emerging Technologies, Vol. 123, 2021, p. 102959. https://doi.org/10.1016/j.trc.2020.102959.
6.
ZhouH.QiJ.YangL.ShiJ.PanH.GaoY.Joint Optimization of Train Timetabling and Rolling Stock Circulation Planning: A Novel Flexible Train Composition Mode. Transportation Research Part B: Methodological, Vol. 162, 2022, pp. 352–385. https://doi.org/10.1016/j.trb.2022.06.007.
7.
ShantzA.CaselloJ.WoudsmaC.GuerraE.Understanding Factors Associated with Commuter Rail Ridership: A Demand Elasticity Study of the GO Transit Rail Network. Transportation Research Record: Journal of the Transportation Research Board, 2022. 2676: 131–143.
8.
YangL.ZhangY.LiS.GaoY.A Two-Stage Stochastic Optimization Model for the Transfer Activity Choice in Metro Networks. Transportation Research Part B: Methodological, Vol. 83, 2016, pp. 271–297. https://doi.org/10.1016/j.trb.2015.11.010.
9.
CormanF.D’ArianoA.MarraA. D.PacciarelliD.SamàM.Integrating Train Scheduling and Delay Management in Real-Time Railway Traffic Control. Transportation Research Part E: Logistics and Transportation Review, Vol. 105, 2017, pp. 213–239. https://doi.org/10.1016/j.tre.2016.04.007.
10.
FurthP. G.Short Turning on Transit Routes. Transportation Research Record: Journal of the Transportation Research Board, 1987, 1108: 42–52.
11.
CederA.Optimal Design of Transit Short-Turn Trips. Transportation Research Record: Journal of the Transportation Research Board, 1989. 1221: 8–22.
12.
CancaD.BarrenaE.LaporteG.OrtegaF. A.A Short-Turning Policy for the Management of Demand Disruptions in Rapid Transit Systems. Annals of Operations Research, Vol. 246, No. 1, 2016, pp. 145–166. https://doi.org/10.1007/s10479-014-1663-x.
13.
CortésC. E.Jara-DíazS.TirachiniA.Integrating Short Turning and Deadheading in the Optimization of Transit Services. Transportation Research Part A: Policy and Practice, Vol. 45, No. 5, 2011, pp. 419–434. https://doi.org/10.1016/j.tra.2011.02.002.
14.
TirachiniA.CortésC. E.Jara-DíazS. R.Optimal Design and Benefits of a Short Turning Strategy for a Bus Corridor. Transportation, Vol. 38, No. 1, 2011, pp. 169–189. https://doi.org/10.1007/s11116-010-9287-8.
15.
YangX.JiY.DuY.ZhangH. M.Bi-Level Model for Design of Transit Short-Turning Service Considering Bus Crowding. Transportation Research Record: Journal of the Transportation Research Board, 2017. 2649: 52–60.
16.
CuryJ.GomideF.MendesM.A Methodology for Generation of Optimal Schedules for an Underground Railway System. IEEE Transactions on Automatic Control, Vol. 25, No.2, 1980, pp. 217–222. https://doi.org/10.1109/TAC.1980.1102282.
17.
LusbyR.LarsenJ.RyanD.EhrgottM.Routing Trains Through Railway Junctions: A New Set-Packing Approach. Transportation Science, Vol. 45, No.2, 2011, pp. 228–245. https://doi.org/10.1287/trsc.1100.0362.
18.
GhaemiN.CatsO.GoverdeR. M.A Microscopic Model for Optimal Train Short-Turnings During Complete Blockages. Transportation Research Part B: Methodological, Vol. 105, 2017, pp. 423–437. https://doi.org/10.1016/j.trb.2017.10.002.
19.
ZhangM.WangY.SuS.TangT.NingB.A Short Turning Strategy for Train Scheduling Optimization in an Urban Rail Transit Line: The Case of Beijing Subway Line 4. Journal of Advanced Transportation, Vol. 2018, 2018, pp. 1–19. https://doi.org/10.1155/2018/5367295.
20.
XuY.YanX.SisiopikuV. P.MerlinL. A.XingF.ZhaoX.Micromobility Trip Origin and Destination Inference Using General Bikeshare Feed Specification Data. Transportation Research Record: Journal of the Transportation Research Board, 2022. 2676: 223–238.
21.
SchettiniT.JabaliO.MalucelliF.A Benders Decomposition Algorithm for Demand-Driven Metro Scheduling. Computers & Operations Research, Vol. 138, 2022, p. 105598. https://doi.org/10.1016/j.cor.2021.105598.
22.
ZhanS.KroonL. G.VeelenturfL. P.WagenaarJ. C.Real-Time High-Speed Train Rescheduling in Case of a Complete Blockage. Transportation Research Part B: Methodological, Vol. 78, 2015, pp. 182–201. https://doi.org/10.1016/j.trb.2015.04.001.
23.
ZhanS.KroonL. G.ZhaoJ.PengQ.A Rolling Horizon Approach to the High Speed Train Rescheduling Problem in Case of a Partial Segment Blockage. Transportation Research Part E: Logistics and Transportation Review, Vol. 95, 2016, pp. 32–61. https://doi.org/10.1016/j.tre.2016.07.015.
24.
DelgadoF.MunozJ. C.GiesenR.How Much Can Holding and/or Limiting Boarding Improve Transit Performance?Transportation Research Part B: Methodological, Vol. 46, No. 9, 2012, pp. 1202–1217. https://doi.org/10.1016/j.trb.2012.04.005.
25.
BieY.HaoM.GuoM.Optimal Electric Bus Scheduling Based on the Combination of All-Stop and Short-Turning Strategies. Sustainability, Vol. 13, No. 4, 2021, p. 1827. https://doi.org/10.3390/su13041827.
26.
LiS.DessoukyM. M.YangL.GaoZ.Joint Optimal Train Regulation and Passenger Flow Control Strategy for High-Frequency Metro Lines. Transportation Research Part B: Methodological, Vol. 99, 2017, pp. 113–137. https://doi.org/10.1016/j.trb.2017.01.010.
27.
HeD.YangY.ChenY.DengJ.ShanS.LiuJ.LiX.An Integrated Optimization Model of Metro Energy Consumption Based on Regenerative Energy and Passenger Transfer. Applied Energy, Vol. 264, 2020, p. 114770. https://doi.org/10.1016/j.apenergy.2020.114770.
28.
HeD.ZhangL.GuoS.ChenY.ShanS.JianH.Energy-Efficient Train Trajectory Optimization Based on Improved Differential Evolution Algorithm and Multi-Particle Model. Journal of Cleaner Production, Vol. 304, 2021, p. 127163. https://doi.org/10.1016/j.jclepro.2021.127163.
29.
ZhangY.PengQ.LuG.ZhongQ.YanX.ZhouX.Integrated Line Planning and Train Timetabling Through Price-Based Cross-Resolution Feedback Mechanism. Transportation Research Part B: Methodological, Vol. 155, 2022, pp. 240–277. https://doi.org/10.1016/j.trb.2021.11.009.
30.
AounJ.QuagliettaE.GoverdeR. M.Investigating Market Potentials and Operational Scenarios of Virtual Coupling Railway Signaling. Transportation Research Record: Journal of the Transportation Research Board, 2020. 2674: 799–812. https://doi.org/10.1177/0361198120925074.
31.
LiuR.LiS.YangL.Collaborative Optimization for Metro Train Scheduling and Train Connections Combined with Passenger Flow Control Strategy. Omega, Vol. 90, 2020, p. 101990. https://doi.org/10.1016/j.omega.2018.10.020.
32.
ShiJ.YangL.YangJ.GaoZ.Service-Oriented Train Timetabling with Collaborative Passenger Flow Control on an Oversaturated Metro Line: An Integer Linear Optimization Approach. Transportation Research Part B: Methodological, Vol. 110, 2018, pp. 26–59. https://doi.org/10.1016/j.trb.2018.02.003.
33.
HuangJ.ZhangT.WeiR.Urban Railway Transit Timetable Optimisation Based on Passenger-and-Trains Matching - A Case Study of Beijing Metro Line. Promet-Traffic&Transportation, Vol. 33, No. 5, 2021, pp. 671–687. https://doi.org/10.7307/ptt.v33i5.3736.
34.
YuanJ.GaoY.LiS.LiuP.YangL.Integrated Optimization of Train Timetable, Rolling Stock Assignment and Short-Turning Strategy for A Metro Line. European Journal of Operational Research, Vol. 301, No. 3, 2022, pp. 855–874. https://doi.org/10.1016/j.ejor.2021.11.019.
35.
MoodleyS.VenterC.Measuring the Service Quality at Multimodal Public Transport Interchanges: A Needs-Driven Approach. Transportation Research Record: Journal of the Transportation Research Board, 2022. 2676: 194–206.
36.
ZhouW.HuangY.DengL.QinJ.Collaborative Optimization of Energy-Efficient Train Schedule and Train Circulation Plan for Urban Rail. Energy, Vol. 263, 2023, p. 125599. https://doi.org/10.1016/j.energy.2022.125599.
37.
TianS.LiX.LiuJ.MaH.YuH.A Short-Turning Strategy to Alleviate Bus Bunching. Journal of Ambient Intelligence and Humanized Computing, Vol. 13, No. 1, 2022, pp.117–128. https://doi.org/10.1007/s12652-020-02891-2.
