Two-Step Optimization of Urban Rail Transit Marshalling and Real-Time Station Control at a Comprehensive Transportation Hub

Abstract

Urban rail transit connecting with a comprehensive transportation hub should meet passenger demands not only within the urban area, but also from outer areas through high-speed railways or planes, which leads to different characteristics of passenger demands. This paper discusses two strategies to deal with these complex passenger demands from two aspects: transit train formation and real-time holding control. First, we establish a model to optimize the multi-marshalling problem by minimizing the trains’ vacant capacities to cope with the fluctuation of demand in different periods. Then, we establish another model to control the multi-marshalling trains in real time to minimize the passengers’ total waiting time. A genetic algorithm (GA) is designed to solve the integrated two-step model of optimizing the number, timetable and real-time holding control of the multi-marshalling trains. The numerical results show that the combined two-step model of multi-marshalling operation and holding control at stations can better deal with the demand fluctuation of urban rail transit connecting with the comprehensive transportation hub. This method can efficiently reduce the number of passengers detained at the hub station as well as the waiting time without increasing the passengers’ on-train time even with highly fluctuating passenger flow.

Keywords

Multi-marshalling optimization / Real-time holding control / Comprehensive transportation hub / Urban rail transit / Genetic algorithm

References

[1] Guo X, Sun H, Wu J. Multiperiod-based timetable optimization for metro transit networks. Transp Res Part B: Methodol, 2017, 96: 46-67

[2] Zhao J, Bukkapatnam S, Dessouky MM. Distributed architecture for real-time coordination of bus holding in transit networks. Intell Transp Syst IEEE Trans, 2005, 4(1): 43-51

[3] Yu B, Yang Z. A dynamic holding strategy in public transit systems with real-time information. Appl Intell, 2009, 31(1): 69-80

[4] Grube P, Cipriano A. Comparison of simple and model predictive control strategies for the holding problem in a metro train system. J Transp Res Board, 2010, 2343(1): 17-24.

[5] Sánchez-Martinez GE, Koutsopoulos HN, Wilson NHM. Real-time holding control for high-frequency transit with dynamics. Transp Res Part B, 2016, 83: 1-19

[6] Wu W, Liu R, Jin W. Designing robust schedule coordination scheme for transit networks with safety control margins. Transp Res Part B, 2016, 93: 495-519

[7] Daganzo CF. A headway-based approach to eliminate bus bunching: Systematic analysis and comparisons. Transp Res Part B: Methodol, 2009, 43(10): 913-921

[8] Bellei G, Gkoumas K. Threshold - and information-based holding at multiple stops. IET Intel Transport Syst, 2009, 3(3): 304-313

[9] Bartholdi JJ, Eisenstein DD. A self-coordinating bus route to resist bus bunching. Transp Res Part B, 2012, 46(4): 481-491

[10] Newell GF. Control of pairing of vehicles on a public transportation route, two vehicles, one control point. Transp Sci, 2016, 8(3): 248-264

[11] Delgado F, Munoz JC, Giesen R. Real-time control of buses in a transit corridor based on vehicle holding and boarding limits. Transp Res Record J Transp Res Board, 2009, 2090(2090): 59-67

[12] Delgado F, Munoz JC, Giesen R. How much can holding and/or limiting boarding improve transit performance. Trans Res Part B Methodol, 2012, 46(9): 1202-1217

[13] Su S, Wilson NHM (2001) An optimal integrated real-time disruption control model for rail transit systems. Computer Aided Scheduling of Public Transport 335-363.

[14] Li DW, Liu ZJ, Wang XQ Routing plan for y-type line of Urban rail transit considering passenger choice behavior. China Railway Sci, 2018, 39(4): 114-122.

[15] Ding XQ, Guan ST, Sun DJ Short turning pattern for relieving metro congestion during peak hours: the substance coherence of Shanghai China. Eur Transp Res Rev, 2018, 10(2): 1-11

[16] Fioole PJ A rolling stock circulation model for combining and splitting of passenger trains. Eur J Oper Res, 2005, 174(2): 1281-1297

[17] Li ZY, Zhao J, Peng QY. Optimizing the train service route plan in an Urban rail transit line with multiple service routes and multiple train sizes. J China Railway Soc, 2020, 42(06): 1-11.

[18] Niu H, Zhang M (2012) An Optimization to schedule train operations with phase-regular framework for intercity rail lines. Discrete Dynamic in Nature and Society: 348-349.

[19] Du XT, Guo JL. Research on grouping optimization of Urban rail transit trains. Softw Guide, 2019, 18(5): 146-150.

Funding

national natural science foundation of china(71621001)