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Coupling analysis of passenger and train flows for a large-scale urban rail transit system

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Abstract

Coupling analysis of passenger and train flows is an important approach in evaluating and optimizing the operation efficiency of large-scale urban rail transit (URT) systems. This study proposes a passenger—train interaction simulation approach to determine the coupling relationship between passenger and train flows. On the bases of time-varying origin—destination demand, train timetable, and network topology, the proposed approach can restore passenger behaviors in URT systems. Upstream priority, queuing process with first-in-first-serve principle, and capacity constraints are considered in the proposed simulation mechanism. This approach can also obtain each passenger’s complete travel chain, which can be used to analyze (including but not limited to) various indicators discussed in this research to effectively support train schedule optimization and capacity evaluation for urban rail managers. Lastly, the proposed model and its potential application are demonstrated via numerical experiments using real-world data from the Beijing URT system (i.e., rail network with the world’s highest passenger ridership).

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References

  • Barrena E, Canca D, Coelho L C, Laporte G (2014). Exact formulations and algorithm for the train timetabling problem with dynamic demand. Computers & Operations Research, 44: 66–74

    Article  MathSciNet  MATH  Google Scholar 

  • Cepeda M, Cominetti R, Florian M (2006). A frequency-based assignment model for congested transit networks with strict capacity constraints: Characterization and computation of equilibria. Transportation Research Part B: Methodological, 40(6): 437–159

    Article  Google Scholar 

  • Cheng Y, Yin J, Yang L (2021). Robust energy-efficient train speed profile optimization in a scenario-based position-time-speed network. Frontiers of Engineering Management, 8(4): 595–614

    Article  Google Scholar 

  • Cominetti R, Correa J (2001). Common-lines and passenger assignment in congested transit networks. Transportation Science, 35(3): 250–267

    Article  MATH  Google Scholar 

  • Ding L Y, Guo S Y (2015). Study on big data-based behavior modification in metro construction. Frontiers of Engineering Management, 2(2): 131–136

    Article  Google Scholar 

  • Ding L Y, Xu J (2017). A review of metro construction in China: Organization, market, cost, safety and schedule. Frontiers of Engineering Management, 4(1): 4–19

    Article  Google Scholar 

  • Fu Q, Liu R, Hess S (2012). A review on transit assignment modelling approaches to congested networks: A new perspective. Procedia: Social and Behavioral Sciences, 54: 1145–1155

    Google Scholar 

  • Gao Z Y, Yang L X (2019). Energy-saving operation approaches for urban rail transit systems. Frontiers of Engineering Management, 6(2): 139–151

    Article  Google Scholar 

  • Han B, Zhou W, Li D, Yin H (2015). Dynamic schedule-based assignment model for urban rail transit network with capacity constraints. The Scientific World Journal, 2015: 940815

    Article  Google Scholar 

  • Ingvardson J B, Nielsen O A, Raveau S, Nielsen B F (2018). Passenger arrival and waiting time distributions dependent on train service frequency and station characteristics: A smart card data analysis. Transportation Research Part C: Emerging Technologies, 90: 292–306

    Article  Google Scholar 

  • Jamili A, Pourseyed Aghaee M (2015). Robust stop-skipping patterns in urban railway operations under traffic alteration situation. Transportation Research Part C: Emerging Technologies, 61: 63–74

    Article  Google Scholar 

  • Jiang Z B, Li F, Xu R H, Gao P (2012). A simulation model for estimating train and passenger delays in large-scale rail transit networks. Journal of Central South University, 19(12): 3603–3613

    Article  Google Scholar 

  • Li W, Zhu W (2016). A dynamic simulation model of passenger flow distribution on schedule-based rail transit networks with train delays. Journal of Traffic and Transportation Engineering, 3(4): 364–373

    Google Scholar 

  • Li Z, Lo S M, Ma J, Luo X W (2020). A study on passengers’ alighting and boarding process at metro platform by computer simulation. Transportation Research Part A: Policy and Practice, 132:840–854

    Google Scholar 

  • Liu X B, Huang M H, Qu H Z, Chien S (2018). Minimizing metro transfer waiting time with AFCS data using simulated annealing with parallel computing. Journal of Advanced Transportation, 2018: 4218625

    Article  Google Scholar 

  • Ma Z L, Koutsopoulos H N (2019). Optimal design of promotion based demand management strategies in urban rail systems. Transportation Research Part C: Emerging Technologies, 109: 155–173

    Article  Google Scholar 

  • Nökel K, Wekeck R (2009). Boarding and alighting in frequency-based transit assignment. Transportation Research Record: Journal of the Transportation Research Board, 2111(1): 60–67

    Article  Google Scholar 

  • Nuzzolo A, Crisalli U, Rosati L (2012). A schedule-based assignment model with explicit capacity constraints for congested transit networks. Transportation Research Part C: Emerging Technologies, 20(1): 16–33

    Article  Google Scholar 

  • Paulsen M, Rasmussen T K, Nielsen O A (2021). Impacts of real-time information levels in public transport: A large-scale case study using an adaptive passenger path choice model. Transportation Research Part A: Policy and Practice, 148: 155–182

    Google Scholar 

  • Poulhès A (2020). Dynamic assignment model of trains and users on a congested urban-rail line. Journal of Rail Transport Planning & Management, 14: 100178

    Article  Google Scholar 

  • Schmöcker J D, Fonzone A, Shimamoto H, Kurauchi F, Bell M G H (2011). Frequency-based transit assignment considering seat capacities. Transportation Research Part B: Methodological, 45(2): 392–408

    Article  Google Scholar 

  • Seriani S, Fernandez R (2015). Pedestrian traffic management of boarding and alighting in metro stations. Transportation Research Part C: Emerging Technologies, 53: 76–92

    Article  Google Scholar 

  • Shi J G, Yang L X, Yang J, Zhou F, Gao Z Y (2019). Cooperative passenger flow control in an oversaturated metro network with operational risk thresholds. Transportation Research Part C: Emerging Technologies, 107: 301–336

    Article  Google Scholar 

  • Teng J, Liu W R (2015). Development of a behavior-based passenger flow assignment model for urban rail transit in section interruption circumstance. Urban Rail Transit, 1(1): 35–46

    Article  Google Scholar 

  • Wang Y H, Tang T, Ning B, van den Boom T J J, de Schutter B (2015). Passenger-demands-oriented train scheduling for an urban rail transit network. Transportation Research Part C: Emerging Technologies, 60: 1–23

    Article  Google Scholar 

  • Wu J J, Liu M H, Sun H J, Li T F, Gao Z Y, Wang D Z W (2015). Equity-based timetable synchronization optimization in urban subway network. Transportation Research Part C: Emerging Technologies, 51: 1–18

    Article  Google Scholar 

  • Wu J J, Qu Y C, Sun H J, Yin H D, Yan X Y, Zhao J D (2019). Data-driven model for passenger route choice in urban metro network. Physica A, 524: 787–798

    Article  Google Scholar 

  • Xie J, Wong S, Zhan S, Lo S, Chen A (2020). Train schedule optimization based on schedule-based stochastic passenger assignment. Transportation Research Part E: Logistics and Transportation Review, 136: 101882

    Article  Google Scholar 

  • Xu G, Zhao S, Shi F, Zhang F (2017). Cell transmission model of dynamic assignment for urban rail transit networks. PLoS One, 12(11): e0188874

    Article  Google Scholar 

  • Xu X M, Li C, Xu Z (2021). Train timetabling with stop-skipping, passenger flow, and platform choice considerations. Transportation Research Part B: Methodological, 150: 52–74

    Article  Google Scholar 

  • Yang J F, Jin J G, Wu J J, Jiang X (2017). Optimizing passenger flow control and bus-bridging service for commuting metro lines. Computer-Aided Civil and Infrastructure Engineering, 32(6): 458–473

    Article  Google Scholar 

  • Yang W W (2014). Study of sustainable urban rail transit development model in China. Frontiers of Engineering Management, 1(2): 195–201

    Article  MathSciNet  Google Scholar 

  • Yang X, Wu J J, Sun H J, Gao Z Y, Yin H D, Qu Y C (2019). Performance improvement of energy consumption, passenger time and robustness in metro systems: A multi-objective timetable optimization approach. Computers & Industrial Engineering, 137: 106076

    Article  Google Scholar 

  • Yin H D, Han B M, Li D W, Lu F (2011). Modeling and application of urban rail transit network for path finding problem. In: Proceedings of the 6th International Conference on Intelligent Systems and Knowledge Engineering — Practical Applications of Intelligent Systems. Shanghai: IEEE, 689–695

    Google Scholar 

  • Yin J T, Wang Y H, Tang T, Xun J, Su S (2017). Metro train rescheduling by adding backup trains under disrupted scenarios. Frontiers of Engineering Management, 4(4): 418–427

    Article  Google Scholar 

  • Zhang Q, Han B M (2010). Modeling and simulation of transfer performance in Beijing metro stations. In: 8th IEEE International Conference on Control and Automation. Xiamen, 1888–1891

  • Zhang T Y, Li D W, Qiao Y (2018). Comprehensive optimization of urban rail transit timetable by minimizing total travel times under time-dependent passenger demand and congested conditions. Applied Mathematical Modelling, 58: 421–446

    Article  MathSciNet  MATH  Google Scholar 

  • Zhao J J, Qu Q, Zhang F, Xu C Z, Liu S Y (2017). Spatio—temporal analysis of passenger travel patterns in massive smart card data. IEEE Transactions on Intelligent Transportation Systems, 18(11): 3135–3146

    Article  Google Scholar 

  • Zhu Y, Koutsopoulos H N, Wilson N (2017). A probabilistic passenger-to-train assignment model based on automated data. Transportation Research Part B: Methodological, 104: 522–542

    Article  Google Scholar 

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Correspondence to Xin Yang.

Additional information

This research was supported by the National Key R&D Program of China (Grant No. 2020YFB1600702), the National Natural Science Foundation of China (Grant Nos. 71621001, 72071015, 71701013, and 71890972/71890970), the Beijing Municipal Natural Science Foundation (Grant No. L191024), the 111 Project (Grant No. B20071), and the State Key Laboratory of Rail Traffic Control and Safety (Grant No. RCS2021ZZ001).

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Zhang, P., Yang, X., Wu, J. et al. Coupling analysis of passenger and train flows for a large-scale urban rail transit system. Front. Eng. Manag. 10, 250–261 (2023). https://doi.org/10.1007/s42524-021-0180-2

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  • DOI: https://doi.org/10.1007/s42524-021-0180-2

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