Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

Ziling Zeng , Taixun Li

Urban Rail Transit ›› 2018, Vol. 4 ›› Issue (3) : 130 -140.

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Urban Rail Transit ›› 2018, Vol. 4 ›› Issue (3) : 130 -140. DOI: 10.1007/s40864-018-0084-6
Original Research Papers

Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

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Abstract

Simulating the congestion propagation of urban rail transit system is challenging, especially under oversaturated conditions. This paper presents a congestion propagation model based on SIR (susceptible, infected, recovered) epidemic model for capturing the congestion prorogation process through formalizing the propagation by a congestion susceptibility recovery process. In addition, as congestion propagation is the key parameter in the congestion propagation model, a model for calculating congestion propagation rate is constructed. A gray system model is also introduced to quantify the propagation rate under the joint effect of six influential factors: passenger flow, train headway, passenger transfer convenience, time of congestion occurring, initial congested station and station capacity. A numerical example is used to illustrate the congestion propagation process and to demonstrate the improvements after taking corresponding measures.

Keywords

SIR epidemic model / Oversaturated conditions / Congestion propagation model / Congestion propagation rate / Gray system model

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Ziling Zeng, Taixun Li. Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model. Urban Rail Transit, 2018, 4(3): 130-140 DOI:10.1007/s40864-018-0084-6

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References

[1]

Ortigosa J, Menendez M. Analysis of quasi-grid urban structures. Cities, 2014, 36: 18-27

[2]

Lu K, Han B, Zhou X. Smart urban transit systems: from integrated framework to interdisciplinary perspective. Urban Rail Transit, 2018, 4: 49

[3]

Li SB, Wu JJ, Gao ZY, Lin Y,d Fu BB (2011) Bi-dynamic analysis of traffic congestion and propagation based on complex network. Acta Physica Sinica 60(5)

[4]

Khattak A, Yangsheng J, Lu H Width design of urban rail transit station walkway: a novel simulation-based optimization approach. Urban Rail Transit, 2017, 3: 112

[5]

Ji Y, Geroliminis N (2012) Modeling congestion propagation in urban transportation networks. Presented at the 12th Swiss Transport Research Conference, Locarno, Switzerland

[6]

Roberg-Orenstein P, Abbess CR, Wright C. Traffic jam simulation. J Maps, 2007, 2007: 107-121

[7]

Scott D, Novak D, Aultman-Hall L, Guo F. Network robustness index: a new method for identifying critical links and evaluating the performance of transportation networks. J Transp Geogr, 2006, 14(3): 215-227

[8]

D’Acierno L, Botte M, Placido A Methodology for determining dwell times consistent with passenger flows in the case of metro services. Urban Rail Transit., 2017, 3: 73

[9]

Zhu S, Levinson DM, Liu H, Harder K. The traffic and behavioral effects of the I-35W Mississippi river bridge collapse. Transp Res A Policy Pract, 2010, 44(10): 771-784

[10]

Sundara P, Puan OC, Hainin MR. Determining the impact of darkness on highway traffic shockwave propagation. Am J Appl Sci, 2013, 10(9): 1000-1008

[11]

Zhang XY. Traffic congestion dissipation model based on traffic wave theory under typical traffic incidents, 2014, Beijing: Beijing Jiaotong University

[12]

Zang JR, Song GH, Wan T, Gao Y, Fei WP, Yu L. A temporal and spatial model of congestion propagation and dissipation on expressway caused by traffic incidents. J Transp Syst Eng Inf Technol, 2017, 17: 179-185.

[13]

Meng L, Zhou X. Simultaneous train rerouting and rescheduling on an n-track network: a model reformulation with network-based cumulative flow variables. Transp Res B, 2014, 67(3): 208-234

[14]

Wang P, Goverde RMP. Multi-train trajectory optimization for energy efficiency and delay recovery on single-track railway lines. Transp Res B Methodol, 2017, 105(11): 340-361

[15]

Carey M, Kwieciński A. Stochastic approximation to the effects of headways on knock-on delays of trains. Transp Res B Methodol, 1994, 28(4): 251-267

[16]

Louwerse I, Huisman D. Adjusting a railway timetable in case of partial or complete blockades. Eur J Oper Res, 2014, 235(3): 583-593

[17]

Cadarso L, Marín Ángel. Integration of timetable planning and rolling stock in rapid transit networks. Ann Oper Res, 2012, 199(1): 113-135

[18]

Goverde RMP. A delay propagation algorithm for large-scale railway traffic networks. Transp Res C Emerg Technol, 2010, 18(3): 269-287

[19]

Büker T, Seybold B. Stochastic modelling of delay propagation in large networks. J Rail Transp Plan Manag, 2012, 2(1–2): 34-50.

[20]

Zhou YF, Zhou LSH, Yue YX. Synchronized and coordinated train connecting optimization for transfer stations of urban rail networks. J China Railw Soc, 2011, 33: 9-16.

[21]

Turns Stephen R. An introduction to combustion concepts and applications, 2012 2 New York: McGraw Hill Education

[22]

Duan LW, Wen C, Peng QY. Transmission mechanism of sudden large passenger flow in urban rail transit network. J Railw Transp Econ, 2012, 34: 79-84.

[23]

Li P (2012) Research on passenger flow distribution characteristics and congestion transmission for urban rail transit operation network. Beijing Jiaotong University Master Degree Thesis

[24]

Li Y, Liu Y, Zou K. Research on the critical value of traffic congestion propagation based on coordination game. J Procedia Eng, 2016, 137: 754-761

[25]

Liu LF (2013) Crowding propagation and organization coordination of urban rail network under larger passenger flow. China’s Southwest Jiaotong University Master Degree Thesis

[26]

Wu L (2013) Analysis of unexpected passenger demand and resulting congestion in urban rail transit. China’s Southwest Jiaotong University Master Degree Thesis

[27]

Kermack W, McKendrick A. A contribution to the mathematical theory of epidemics. Proc R Soc Lond A, 1927, 115: 700-721

[28]

Thieme HR. Mathematics in population biology, 2003, Princeton: Princeton University Press

[29]

Dietz K, Heesterbeek JAP. Bernoulli was ahead of modern epidemiology. Nature, 2000, 408(6812): 513-514

[30]

Anderson RM, May RM. Infective diseases of humans: dynamics and control, 1991, Oxford: Oxford University Press

[31]

Bailey NTJ. The mathematical theory of epidemics, 1957, London: Charles Griffin

[32]

Brauer F, van den Driessche P, Wu J. Mathematical epidemiology, 2008, Berlin: Springer

[33]

Diekmann O, Heesterbeek H, Britton T. Mathematical tools for understanding infectious disease dynamics, 2013, Princeton: Princeton University Press

[34]

Keeling M, Rohani P. Modeling infectious diseases in humans and animals, 2007, Princeton: Princeton University Press

[35]

Becker NG, Britton T. Statistical studies of infectious disease incidence. J R Stat Soc Ser B Stat Methodol, 1999, 61: 287-307

[36]

Ponciano JM, Capistrán MA. First principles modeling of nonlinear incidence rates in seasonal epidemics, 2011, Biol: PLoS Comput 7

[37]

Dowell SF. Seasonal variation in host susceptibility and cycles of certain infectious diseases. Emerg Infect Dis, 2001, 7: 369-374

[38]

Cats O, West J, Eliasson J Dynamic stochastic model for evaluating congestion and crowding effects in transit systems. J Transp Res Methodol, 2016, 89: 43-57

[39]

Deng JL. The basis of grey theory, 2002, Wuhan: The Press of Huazhong University of Science and Technology

[40]

Li Z, Liu Y, Wang J. Modeling and simulating traffic congestion propagation in connected vehicles driven by temporal and spatial preference. J Wirel Netw, 2015, 22(4): 1121-1131

[41]

Yu HX (2008) The research of transfer passenger organization in the Xizhimen Station. Beijing Jiaotong University Master Degree Thesis

[42]

Xu QW (2008) Analysis and modeling of individual passenger behavior in urban railway transit hub. Beijing Jiaotong University Master Degree Thesis

[43]

Du P, Liu CH, Liu ZHL. Walking time modeling on transfer pedestrians in subway passages. J Transp Syst Eng Inf Technol, 2009, 4: 103-109.

Funding

Fundamental Research Funds for Central Universities(2017JBM029)

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