Simulation-Based Method for the Calculation of Passenger Flow Distribution in an Urban Rail Transit Network Under Interruption

Guanghui Su , Bingfeng Si , Kun Zhi , Ben Zhao , Xuanchuan Zheng

Urban Rail Transit ›› 2023, Vol. 9 ›› Issue (2) : 110 -126.

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Urban Rail Transit ›› 2023, Vol. 9 ›› Issue (2) : 110 -126. DOI: 10.1007/s40864-023-00188-z
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Simulation-Based Method for the Calculation of Passenger Flow Distribution in an Urban Rail Transit Network Under Interruption

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Abstract

In the extensive urban rail transit network, interruptions will lead to service delays on the current line and spread to other lines, forcing many passengers to wait, detour, or even give up their trips. This paper proposes an event-driven simulation method to evaluate the impact of interruptions on passenger flow distribution. With this method, passengers are regarded as individual agents who can obtain complete information about the current traffic situation, and the impact of the occurrence, duration, and recovery of interruption events on passengers’ travel decisions is analyzed in detail. Then, two modes are used to assign passenger paths: experience-based pre-trip mode and response-based entrap mode. In the simulation process, the train is regarded as an individual agent with a fixed capacity. With the advance of the simulation clock, the network loading is completed through the interaction of the three agents of passengers, platforms, and trains. Interruption events are considered triggers, affecting other agents by affecting network topology and train schedules. Finally, taking Chongqing Metro as an example, the accuracy and effectiveness of the model are analyzed and verified. And the impact of interruption on passenger flow distribution indicators such as inbound volume, outbound volume, and transfer volume is studied from both the individual and overall dimensions. The results show that this study provides an effective method for calculating the passenger flow distribution of an extensive urban rail transit network in the case of interruption.

Keywords

Urban rail transit / Passenger flow distribution / Interruption / Simulation method

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Guanghui Su, Bingfeng Si, Kun Zhi, Ben Zhao, Xuanchuan Zheng. Simulation-Based Method for the Calculation of Passenger Flow Distribution in an Urban Rail Transit Network Under Interruption. Urban Rail Transit, 2023, 9(2): 110-126 DOI:10.1007/s40864-023-00188-z

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References

[1]

Mo P, D’Ariano A, Yang L An exact method for the integrated optimization of subway lines operation strategies with asymmetric passenger demand and operating costs. Transp Res Part B Methodol, 2021, 149: 283-321

[2]

Su G, Si B, Zhao F, Li H. Data-driven method for passenger path choice inference in congested subway network. Complexity, 2022, 2022: 13

[3]

Niu H, Zhou X, Gao R. Train scheduling for minimizing passenger waiting time with time-dependent demand and skip-stop patterns: nonlinear integer programming models with linear constraints. Transp Res Part B Methodol, 2015, 76: 117-135

[4]

Han B, Li Y, Lu F Statistical analysis of urban rail transit operations in the world in 2021: a review. Urban Rapid Rail Transit, 2022, 35: 5-11

[5]

Wang X (2020) Study on the adjustment of urban rail train operation and the dynamic deduction of passenger flow in case of emergency. Dissertation, Beijing Jiaotong University

[6]

Sun DJ, Guan S. Measuring vulnerability of urban metro network from line operation perspective. Transp Res Part A Policy Pract, 2016, 94: 348-359

[7]

Yang J, Jin JG, Wu J, Jiang X. Optimizing passenger flow control and bus-bridging service for commuting metro lines. Comput Civ Infrastruct Eng, 2017, 32: 458-473

[8]

Yin Y, Liu H, Zhang S Joint optimization of modular vehicle schedule and fair passenger flow control under heterogeneous passenger demand in a rail transit system. Comput Ind Eng, 2022, 173: 108749

[9]

Derrible S, Kennedy C. Characterizing metro networks: state, form, and structure. Transportation (Amst), 2010, 37: 275-297

[10]

Sun L, Huang Y, Chen Y, Yao L. Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China. Transp Res Part A Policy Pract, 2018, 108: 12-24

[11]

Schipper D, Gerrits L. Differences and similarities in European railway disruption management practices. J Rail Transp Plan Manag, 2018, 8: 42-55

[12]

Jenelius E, Cats O. The value of new public transport links for network robustness and redundancy. Transp A Transp Sci, 2015, 11: 819-835

[13]

Lu QC. Modeling network resilience of rail transit under operational incidents. Transp Res Part A Policy Pract, 2018, 117: 227-237

[14]

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

[15]

Sun H, Wu J, Wu L Estimating the influence of common disruptions on urban rail transit networks. Transp Res Part A Policy Pract, 2016, 94: 62-75

[16]

Eltved M, Breyer N, Ingvardson JB, Nielsen OA. Impacts of long-term service disruptions on passenger travel behaviour: a smart card analysis from the Greater Copenhagen area. Transp Res Part C Emerg Technol, 2021, 131: 103198

[17]

Silva R, Kang SM, Airoldi EM. Predicting traffic volumes and estimating the effects of shocks in massive transportation systems. Proc Natl Acad Sci USA, 2015, 112: 5643-5648

[18]

Florian M, Mahut M, Tremblay N. Application of a simulation-based dynamic traffic assignment model. Eur J Oper Res, 2008, 189: 1381-1392

[19]

Nuzzolo A, Crisalli U, Rosati L. A schedule-based assignment model with explicit capacity constraints for congested transit networks. Transp Res Part C Emerg Technol, 2012, 20: 16-33

[20]

Verbas Ö, Mahmassani HS, Hyland MF. Gap-based transit assignment algorithm with vehicle capacity constraints: simulation-based implementation and large-scale application. Transp Res Part B Methodol, 2016, 93: 1-16

[21]

Yao X, Han B, Yu D, Ren H. Simulation-based dynamic passenger flow assignment modelling for a schedule-based transit network. Discret Dyn Nat Soc, 2017

[22]

Yin Y, Li D, Zhao K, Yang R. Optimum equilibrium passenger flow control strategies with delay penalty functions under oversaturated condition on urban rail transit. J Adv Transp, 2021

[23]

Liu T, Ma Z, Koutsopoulos HN. Unplanned disruption analysis in urban railway systems using smart card data. Urban Rail Transit, 2021, 7: 177-190

[24]

Cong C, Li X, Yang S Impact estimation of unplanned urban rail disruptions on public transport passengers: a multi-agent based simulation approach. Int J Environ Res Public Health, 2022

[25]

Poon MH, Wong SC, Tong CO. A dynamic schedule-based model for congested transit networks. Transp Res Part B Methodol, 2004, 38: 343-368

[26]

Si B, Zhong M, Liu J Development of a transfer-cost-based logit assignment model for the Beijing rail transit network using automated fare collection data. J Adv Transp, 2013, 47: 297-318

[27]

Leurent F, Xie X. On individual repositioning distance along platform during train waiting. J Adv Transp, 2018

[28]

Larrain H, Suman HK, Muñoz JC. Route based equilibrium assignment in congested transit networks. Transp Res Part C Emerg Technol, 2021

[29]

Hörcher D, Graham DJ, Anderson RJ. Crowding cost estimation with large scale smart card and vehicle location data. Transp Res Part B Methodol, 2017, 95: 105-125

[30]

Su G, Si B, Zhi K, Li H. A calculation method of passenger flow distribution in large-scale subway network based on passenger—train matching probability. Entropy, 2022, 24: 1026

[31]

Singh R, Hörcher D, Graham DJ, Anderson RJ. Decomposing journey times on urban metro systems via semiparametric mixed methods. Transp Res Part C Emerg Technol, 2020, 114: 140-163

[32]

Small KA. Valuation of travel time. Econ Transp, 2012, 1: 2-14

Funding

National Natural Science Foundation of China(72091513)

National key research and development program(2019YFB1600200)

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