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

Ping ZHANG, Xin YANG, Jianjun WU, Huijun SUN, Yun WEI, Ziyou GAO

PDF(1929 KB)
PDF(1929 KB)
Front. Eng ›› 2023, Vol. 10 ›› Issue (2) : 250-261. DOI: 10.1007/s42524-021-0180-2
Traffic Engineering Systems Management
RESEARCH ARTICLE

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

Author information +
History +

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).

Graphical abstract

Keywords

urban rail transit / coupling analysis / passenger–train interaction / large-scale simulation

Cite this article

Download citation ▾
Ping ZHANG, Xin YANG, Jianjun WU, Huijun SUN, Yun WEI, Ziyou GAO. Coupling analysis of passenger and train flows for a large-scale urban rail transit system. Front. Eng, 2023, 10(2): 250‒261 https://doi.org/10.1007/s42524-021-0180-2

References

[1]
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
CrossRef Google scholar
[2]
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): 437–459
CrossRef Google scholar
[3]
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 doi: 10.1007/s42524-021-0173-1
[4]
Cominetti R, Correa J (2001). Common-lines and passenger assignment in congested transit networks. Transportation Science, 35(3): 250–267
CrossRef Google scholar
[5]
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
CrossRef Google scholar
[6]
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
CrossRef Google scholar
[7]
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
CrossRef Google scholar
[8]
Gao Z Y, Yang L X (2019). Energy-saving operation approaches for urban rail transit systems. Frontiers of Engineering Management, 6(2): 139–151
CrossRef Google scholar
[9]
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
CrossRef Pubmed Google scholar
[10]
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
CrossRef Google scholar
[11]
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
CrossRef Google scholar
[12]
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
CrossRef Google scholar
[13]
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
CrossRef Google scholar
[14]
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
CrossRef Google scholar
[15]
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
CrossRef Google scholar
[16]
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
CrossRef Google scholar
[17]
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
CrossRef Google scholar
[18]
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
CrossRef Google scholar
[19]
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
CrossRef Google scholar
[20]
Poulhès A (2020). Dynamic assignment model of trains and users on a congested urban-rail line. Journal of Rail Transport Planning & Management, 14: 100178
CrossRef Google scholar
[21]
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
CrossRef Google scholar
[22]
Seriani S, Fernandez R (2015). Pedestrian traffic management of boarding and alighting in metro stations. Transportation Research Part C: Emerging Technologies, 53: 76–92
CrossRef Google scholar
[23]
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
CrossRef Google scholar
[24]
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
CrossRef Google scholar
[25]
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
CrossRef Google scholar
[26]
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
CrossRef Google scholar
[27]
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
CrossRef Google scholar
[28]
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
CrossRef Google scholar
[29]
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
CrossRef Pubmed Google scholar
[30]
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
CrossRef Google scholar
[31]
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
CrossRef Google scholar
[32]
Yang W W (2014). Study of sustainable urban rail transit development model in China. Frontiers of Engineering Management, 1(2): 195–201
CrossRef Google scholar
[33]
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
CrossRef Google scholar
[34]
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
CrossRef Google scholar
[35]
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
CrossRef Google scholar
[36]
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
[37]
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
CrossRef Google scholar
[38]
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
CrossRef Google scholar
[39]
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
CrossRef Google scholar

RIGHTS & PERMISSIONS

2021 Higher Education Press
AI Summary AI Mindmap
PDF(1929 KB)

Accesses

Citations

Detail

Sections
Recommended

/