Assessment Method for Dynamic Impact of Large Passenger Flow on Urban Rail Transit Network: A Case Study on Chengdu East Railway Station

Abstract

Large-scale activities, holidays, and emergencies often cause a significantly large burst of passenger flow demand in some urban rail transit (URT) stations in a short time, called large passenger flow (LPF). The LPF will propagate through the entire URT network of the city. The impact of the frequent occurrence of LPF on network service levels is crucial and unpredictable. This article describes an analysis of how this LPF propagates through the entire network inspired by how radionuclide imaging is done in clinical medicine. In this study, with LPF of URT as the research object, a propagation model of LPF in URT based on AFC data, train operation data, and URT network topology data was developed, which was inspired by the concept of radionuclide imaging in clinical medicine. In the condition of obtaining the list of passenger route selection ratios, the dynamic propagation state matrix of the LPF in the network is solved. The contribution value matrix of the LPF was proposed to evaluate the impact of the LPF on the URT network. Considering the LPF in Chengdu East Railway Station, China, as an example, the propagation effect of LPF in the Chengdu Metro network was analyzed, and the effectiveness of the proposed model was confirmed.

Keywords

data and data science urban transportation data and information systems spatial data trip purpose data visualization in transportation analytic data visualization data visualization public transportation rail transit systems subway urban rail passenger rail transportation passengers

Get full access to this article

View all access options for this article.

References

Jiang

Fan

Liu

Zhu

Reinforcement Learning Approach for Coordinated Passenger Inflow Control of Urban Rail Transit in Peak Hours. Transportation Research Part C: Emerging Technologies, Vol. 88, 2018, pp. 1–16.

Yuan

Sun

Kang

Passenger Flow Control Strategies for Urban Rail Transit Networks. Applied Mathematical Modelling, Vol. 82, 2020, pp. 168–188.

Jiang

Shao

Zhu

Simulation Study on Train Delay and Propagation Characteristics of Urban Mass Transit Systems. Journal of the China Railway Society, Vol. 28, No. 2, 2006, pp. 7–10.

Corman

Meng

A Review of Online Dynamic Models and Algorithms for Railway Traffic Management. IEEE Transactions on Intelligent Transportation Systems. Vol. 16, No. 3, 2015, pp. 1274–1284.

Ding

Zhao

Liu

The Modeling of Urban Rail Transit Emergency Delay Propagation Scope Under Network Operation Mode. Concurrency and Computation: Practice and Experience, Vol. 32, No. 23, 2020, p. e5530.

Luo

Hou

Zhang

Study on the Propagation Mechanism of Large Passenger Flow in Urban Rail Transit. Proc., 2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE), Singapore, IEEE, New York, NY, 2018, pp. 100–104.

Xiong

Yao

Congestion Propagation Quantization Model About Rail Transit System. Journal of Transportation Systems Engineering and Information Technology, Vol. 18, No. 3, 2018, pp. 146–151.

Zhang

Xiao

Pan

A CA-Based Simulation Model of Urban Railway Large Passenger Flow Congestion Transmission. Journal of Transportation Systems Engineering and Information Technology, Vol. 17, No. 4, 2017, pp. 83–89.

Cornet

Buisson

Ramond

Bouvarel

Rodriguez

Methods for Quantitative Assessment of Passenger Flow Influence on Train Dwell Time in Dense Traffic Areas. Transportation Research Part C: Emerging Technologies, Vol. 106, 2019, pp. 345–359.

10.

Hong

Gao

Calculation Method of Emergency Passenger Flow in Urban Rail Network. Journal of Tongji University (Natural Science), Vol. 39, No. 10, 2011, pp. 1485–1489.

11.

Pulugurtha

Duddu

Venigalla

Evaluating Spatial and Temporal Effects of Planned Special Events on Travel Time Performance Measures. Transportation Research Interdisciplinary Perspectives, Vol. 6, 2020, p. 100168.

12.

Sharma

Galicia

Susen

Cornejo

Employing Big Data Analytics to Monitor Transportation Impacts in El Paso During Pope’s Visit to the US-Mexico Border City of JuÁRez. International Journal of Transportation Science and Technology, Vol. 6, No. 2, 2017, pp. 127–142.

13.

Wang

Attanucci

Wilson

Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection Systems. Journal of Public Transportation, Vol. 14, No. 4, pp. 131–150.

14.

Xie

Qin

Learning the Route Choice Behavior of Subway Passengers From AFC Data. Expert Systems With Applications, Vol. 95, 2018, pp. 324–332.

15.

Sun

Yin

Yan

Zhao

Data-Driven Model for Passenger Route Choice in Urban Metro Network. Physica A: Statistical Mechanics and Its Applications, Vol. 524, 2019, pp. 787–798.

16.

Liu

Huang

Chien

Minimizing Metro Transfer Waiting Time With AFCS Data Using Simulated Annealing With Parallel Computing. Journal of Advanced Transportation, Vol. 2018, No. 1753, 2018, pp. 1–17.

17.

Barrena

Canca

Coelho

Laporte

Single-Line Rail Rapid Transit Timetabling Under Dynamic Passenger Demand. Transportation Research Part B: Methodological, Vol. 70, 2014, pp. 134–150.

18.

Jia

Jiang

Deep Learning-Based Hybrid Model for Short-Term Subway Passenger Flow Prediction Using Automatic Fare Collection Data. IET Intelligent Transport Systems, Vol. 13, No. 11, 2019, pp. 1708–1716.

19.

Jiang

Hsu

Zhang

Zou

Evaluating Rail Transit Timetable Using Big Passengers’ Data. Journal of Computer and System Sciences, Vol. 82, No. 1, 2016, pp. 144–155.

20.

Cai

Yao

Liu

Zhang

Liu

Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit. Discrete Dynamics in Nature and Society, Vol. 2015, No. 6, 2015, pp. 1–7.

21.

Wang

Zhou

Huang

Research on Evolution of Large Passenger Flow in Urban Metro Based on Anomalous Mobility Network. Journal of University of Electronic Science and Technology of China, Vol. 49, No. 5, 2020, pp. 732–738.

22.

Yang

Zhao

Yao

A Method to Estimate URT Passenger Spatial-Temporal Trajectory With Smart Card Data and Train Schedules. Sustainability, Vol. 12, No. 6, 2020, p. 2574.

23.

Zhou

Song

Optimization of Urban Rail Transit Connection Scheme for Evacuating Large Volumes of Arriving Railway Passengers. IEEE Access, Vol. 8, 2020, pp. 68772–68786.

24.

Langer

Haubner

Pichler

Gawaz

Radionuclide Imaging: A Molecular Key to the Atherosclerotic Plaque. Journal of the American College of Cardiology, Vol. 52, No. 1, 2008, pp. 1–12.

25.

Zhou

Model of Passenger Flow Assignment for Urban Rail Transit Based on Entry and Exit Time Constraints. Transportation Research Record: Journal of the Transportation Research Board, 2012. 2284(1): 57–61.

26.

Luo

Gao

Passenger Flow Distribution Model and Algorithm for Urban Rail Transit Network Based on Multi-Route Choice. Journal of the China Railway Society, Vol. 31, No. 2, 2009, pp. 110–114.

27.

Zhou

Huang

Fan

Fare Incentive Strategies for Managing Peak-Hour Congestion in Urban Rail Transit Networks. Transportmetrica A: Transport Science, Vol. 3, 2020, pp. 1–37.

28.

Yin

Liu

Yang

Sun

Optimizing the Release of Passenger Flow Guidance Information in Urban Rail Transit Network via Agent-Based Simulation. Applied Mathematical Modelling, Vol. 72, 2019, pp. 337–355.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.12 MB