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

doi:10.1007/s42524-021-0180-2

Front. Eng

2023, Vol. 10

Issue (2) : 250-261 https://doi.org/10.1007/s42524-021-0180-2

RESEARCH ARTICLE

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

Ping ZHANG¹, Xin YANG¹(

), Jianjun WU¹, Huijun SUN¹, Yun WEI², Ziyou GAO¹

¹. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
². Beijing Mass Transit Railway Operation Corp. Ltd., Beijing 100044, China

Download: PDF(1929 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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).

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

Corresponding Author(s): Xin YANG

Just Accepted Date: 19 November 2021 Online First Date: 10 January 2022 Issue Date: 29 May 2023

Cite this article:

Ping ZHANG,Xin YANG,Jianjun WU, et al. Coupling analysis of passenger and train flows for a large-scale urban rail transit system[J]. Front. Eng, 2023, 10(2): 250-261.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-021-0180-2
https://academic.hep.com.cn/fem/EN/Y2023/V10/I2/250

Tab.1 Characteristics comparison of closely related studies

Fig.1 Schematic indicating the passenger and train collaborative simulation.

Fig.2 Illustration of the path.

Fig.3 Illustration of the passenger generation process at each simulation.

Fig.4 Illustration of the inbound process at each simulation.

Fig.5 Illustration of the boarding and alighting process at each simulation.

Fig.6 Illustration of the transfer process at each simulation.

Fig.7 Illustration of the mechanism of passenger–train interaction.

Fig.8 Illustration of the Beijing URT network.

Fig.9 Spatiotemporal unbalanced passenger demand.

Tab.2 Illustration of a passenger’s travel trip

Fig.10 Statistical results of passenger travel time.

Fig.11 Statistical results of passenger transfer waiting time.

Fig.12 Train load factors of Beijing Metro Line 5.

1	E Barrena, D Canca, L C Coelho, G Laporte (2014). Exact formulations and algorithm for the train timetabling problem with dynamic demand. Computers & Operations Research, 44: 66–74 https://doi.org/10.1016/j.cor.2013.11.003
2	M Cepeda, R Cominetti, M Florian (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 https://doi.org/10.1016/j.trb.2005.05.006
3	Y Cheng, J Yin, L Yang (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	R Cominetti, J Correa (2001). Common-lines and passenger assignment in congested transit networks. Transportation Science, 35(3): 250–267 https://doi.org/10.1287/trsc.35.3.250.10154
5	L Y Ding, S Y Guo (2015). Study on big data-based behavior modification in metro construction. Frontiers of Engineering Management, 2(2): 131–136 https://doi.org/10.15302/J-FEM-2015037
6	L Y Ding, J Xu (2017). A review of metro construction in China: Organization, market, cost, safety and schedule. Frontiers of Engineering Management, 4(1): 4–19 https://doi.org/10.15302/J-FEM-2017015
7	Q Fu, R Liu, S Hess (2012). A review on transit assignment modelling approaches to congested networks: A new perspective. Procedia: Social and Behavioral Sciences, 54: 1145–1155 https://doi.org/10.1016/j.sbspro.2012.09.829
8	Z Y Gao, L X Yang (2019). Energy-saving operation approaches for urban rail transit systems. Frontiers of Engineering Management, 6(2): 139–151 https://doi.org/10.1007/s42524-019-0030-7
9	B Han, W Zhou, D Li, H Yin (2015). Dynamic schedule-based assignment model for urban rail transit network with capacity constraints. The Scientific World Journal, 2015: 940815 https://doi.org/10.1155/2015/940815 pmid: 25918747
10	J B Ingvardson, O A Nielsen, S Raveau, B F Nielsen (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 https://doi.org/10.1016/j.trc.2018.03.006
11	A Jamili, M Pourseyed Aghaee (2015). Robust stop-skipping patterns in urban railway operations under traffic alteration situation. Transportation Research Part C: Emerging Technologies, 61: 63–74 https://doi.org/10.1016/j.trc.2015.09.013
12	Z B Jiang, F Li, R H Xu, P Gao (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 https://doi.org/10.1007/s11771-012-1448-9
13	W Li, W Zhu (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 https://doi.org/10.1016/j.jtte.2015.09.009
14	Z Li, S M Lo, J Ma, X W Luo (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 https://doi.org/10.1016/j.tra.2019.12.017
15	X B Liu, M H Huang, H Z Qu, S Chien (2018). Minimizing metro transfer waiting time with AFCS data using simulated annealing with parallel computing. Journal of Advanced Transportation, 2018: 4218625 https://doi.org/10.1155/2018/4218625
16	Z L Ma, H N Koutsopoulos (2019). Optimal design of promotion based demand management strategies in urban rail systems. Transportation Research Part C: Emerging Technologies, 109: 155–173 https://doi.org/10.1016/j.trc.2019.10.008
17	K Nökel, R Wekeck (2009). Boarding and alighting in frequency-based transit assignment. Transportation Research Record: Journal of the Transportation Research Board, 2111(1): 60–67 https://doi.org/10.3141/2111-08
18	A Nuzzolo, U Crisalli, L Rosati (2012). A schedule-based assignment model with explicit capacity constraints for congested transit networks. Transportation Research Part C: Emerging Technologies, 20(1): 16–33 https://doi.org/10.1016/j.trc.2011.02.007
19	M Paulsen, T K Rasmussen, O A Nielsen (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 https://doi.org/10.1016/j.tra.2021.03.011
20	A Poulhès (2020). Dynamic assignment model of trains and users on a congested urban-rail line. Journal of Rail Transport Planning & Management, 14: 100178 https://doi.org/10.1016/j.jrtpm.2020.100178
21	J D Schmöcker, A Fonzone, H Shimamoto, F Kurauchi, M G H Bell (2011). Frequency-based transit assignment considering seat capacities. Transportation Research Part B: Methodological, 45(2): 392–408 https://doi.org/10.1016/j.trb.2010.07.002
22	S Seriani, R Fernandez (2015). Pedestrian traffic management of boarding and alighting in metro stations. Transportation Research Part C: Emerging Technologies, 53: 76–92 https://doi.org/10.1016/j.trc.2015.02.003
23	J G Shi, L X Yang, J Yang, F Zhou, Z Y Gao (2019). Cooperative passenger flow control in an oversaturated metro network with operational risk thresholds. Transportation Research Part C: Emerging Technologies, 107: 301–336 https://doi.org/10.1016/j.trc.2019.08.008
24	J Teng, W R Liu (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 https://doi.org/10.1007/s40864-015-0002-0
25	Y H Wang, T Tang, B Ning, J J van den Boom T, de B Schutter (2015). Passenger-demands-oriented train scheduling for an urban rail transit network. Transportation Research Part C: Emerging Technologies, 60: 1–23 https://doi.org/10.1016/j.trc.2015.07.012
26	J J Wu, M H Liu, H J Sun, T F Li, Z Y Gao, D Z W Wang (2015). Equity-based timetable synchronization optimization in urban subway network. Transportation Research Part C: Emerging Technologies, 51: 1–18 https://doi.org/10.1016/j.trc.2014.11.001
27	J J Wu, Y C Qu, H J Sun, H D Yin, X Y Yan, J D Zhao (2019). Data-driven model for passenger route choice in urban metro network. Physica A, 524: 787–798 https://doi.org/10.1016/j.physa.2019.04.231
28	J Xie, S Wong, S Zhan, S Lo, A Chen (2020). Train schedule optimization based on schedule-based stochastic passenger assignment. Transportation Research Part E: Logistics and Transportation Review, 136: 101882 https://doi.org/10.1016/j.tre.2020.101882
29	G Xu, S Zhao, F Shi, F Zhang (2017). Cell transmission model of dynamic assignment for urban rail transit networks. PLoS One, 12(11): e0188874 https://doi.org/10.1371/journal.pone.0188874 pmid: 29190682
30	X M Xu, C Li, Z Xu (2021). Train timetabling with stop-skipping, passenger flow, and platform choice considerations. Transportation Research Part B: Methodological, 150: 52–74 https://doi.org/10.1016/j.trb.2021.06.001
31	J F Yang, J G Jin, J J Wu, X Jiang (2017). Optimizing passenger flow control and bus-bridging service for commuting metro lines. Computer-Aided Civil and Infrastructure Engineering, 32(6): 458–473 https://doi.org/10.1111/mice.12265
32	W W Yang (2014). Study of sustainable urban rail transit development model in China. Frontiers of Engineering Management, 1(2): 195–201 https://doi.org/10.15302/J-FEM-2014026
33	X Yang, J J Wu, H J Sun, Z Y Gao, H D Yin, Y C Qu (2019). Performance improvement of energy consumption, passenger time and robustness in metro systems: A multi-objective timetable optimization approach. Computers & Industrial Engineering, 137: 106076 https://doi.org/10.1016/j.cie.2019.106076
34	H D Yin, B M Han, D W Li, F Lu (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 https://doi.org/10.1007/978-3-642-25658-5_81
35	J T Yin, Y H Wang, T Tang, J Xun, S Su (2017). Metro train rescheduling by adding backup trains under disrupted scenarios. Frontiers of Engineering Management, 4(4): 418–427 https://doi.org/10.15302/J-FEM-2017044
36	Q Zhang, B M Han (2010). Modeling and simulation of transfer performance in Beijing metro stations. In: 8th IEEE International Conference on Control and Automation. Xiamen, 1888–1891
37	T Y Zhang, D W Li, Y Qiao (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 https://doi.org/10.1016/j.apm.2018.02.013
38	J J Zhao, Q Qu, F Zhang, C Z Xu, S Y Liu (2017). Spatio–temporal analysis of passenger travel patterns in massive smart card data. IEEE Transactions on Intelligent Transportation Systems, 18(11): 3135–3146 https://doi.org/10.1109/TITS.2017.2679179
39	Y Zhu, H N Koutsopoulos, N Wilson (2017). A probabilistic passenger-to-train assignment model based on automated data. Transportation Research Part B: Methodological, 104: 522–542 https://doi.org/10.1016/j.trb.2017.04.012

[1]	Ziyou GAO, Lixing YANG. Energy-saving operation approaches for urban rail transit systems[J]. Front. Eng, 2019, 6(2): 139-151.
[2]	Wen-wu Yang. Study of Sustainable Urban Rail Transit Development Model in China[J]. Front. Eng, 2014, 1(2): 195-201.

Viewed

Full text

Abstract

Cited

Shared

Discussed