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

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.

[1]  J. Elíasson,et al.  A dynamic stochastic model for evaluating congestion and crowding effects in transit systems , 2016 .

[2]  Peng Qi-yuan Transmission Mechanism of Sudden Large Passenger Flow in Urban Rail Transit Network , 2012 .

[3]  Chris Wright,et al.  Traffic Jam Simulation , 2007 .

[4]  Xuesong Zhou,et al.  Simultaneous train rerouting and rescheduling on an N-track network: A model reformulation with network-based cumulative flow variables , 2014 .

[5]  S. Turns An Introduction to Combustion: Concepts and Applications , 2000 .

[6]  Marcos A. Capistrán,et al.  First Principles Modeling of Nonlinear Incidence Rates in Seasonal Epidemics , 2011, PLoS Comput. Biol..

[7]  Hu Lu,et al.  Width Design of Urban Rail Transit Station Walkway: A Novel Simulation-Based Optimization Approach , 2017 .

[8]  M. Keeling,et al.  Modeling Infectious Diseases in Humans and Animals , 2007 .

[9]  Odo Diekmann,et al.  Mathematical Tools for Understanding Infectious Disease Dynamics , 2012 .

[10]  Monica Menendez,et al.  Traffic performance on quasi-grid urban structures , 2014 .

[11]  David M Levinson,et al.  The Traffic and Behavioral Effects of the I-35W Mississippi River Bridge Collapse , 2010 .

[12]  Rob M.P. Goverde,et al.  Multi-train trajectory optimization for energy efficiency and delay recovery on single-track railway lines , 2017 .

[13]  N. Bailey The mathematical theory of epidemics , 1957 .

[14]  Antonio Placido,et al.  Methodology for Determining Dwell Times Consistent with Passenger Flows in the Case of Metro Services , 2017 .

[15]  Dennis Huisman,et al.  Adjusting a railway timetable in case of partial or complete blockades , 2012, Eur. J. Oper. Res..

[16]  Li Yong,et al.  Research on the Critical Value of Traffic Congestion Propagation Based on Coordination Game , 2016 .

[17]  Kai Lu,et al.  Smart Urban Transit Systems: From Integrated Framework to Interdisciplinary Perspective , 2018 .

[18]  S. Dowell,et al.  Seasonal variation in host susceptibility and cycles of certain infectious diseases. , 2001, Emerging infectious diseases.

[19]  Rob M.P. Goverde,et al.  A delay propagation algorithm for large-scale railway traffic networks , 2010 .

[20]  Horst R. Thieme,et al.  Mathematics in Population Biology , 2003 .

[21]  Thorsten Büker,et al.  Stochastic modelling of delay propagation in large networks , 2012, J. Rail Transp. Plan. Manag..

[22]  Zhili Liu,et al.  Walking Time Modeling on Transfer Pedestrians in Subway Passages , 2009 .

[23]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[24]  Luis Cadarso,et al.  Integration of timetable planning and rolling stock in rapid transit networks , 2012, Ann. Oper. Res..

[25]  Othman Che Puan,et al.  DETERMINING THE IMPACT OF DARKNESS ON HIGHWAY TRAFFIC SHOCKWAVE PROPAGATION , 2013 .

[26]  Darren M. Scott,et al.  Network Robustness Index : a new method for identifying critical links and evaluating the performance of transportation networks , 2006 .

[27]  Yue Yi-xiang Synchronized and Coordinated Train Connecting Optimization for Transfer Stations of Urban Rail Networks , 2011 .

[28]  Klaus Dietz,et al.  Bernoulli was ahead of modern epidemiology , 2000, Nature.

[29]  Weiwen Deng,et al.  Modeling and simulating traffic congestion propagation in connected vehicles driven by temporal and spatial preference , 2016, Wirel. Networks.

[30]  Malachy Carey,et al.  Stochastic approximation to the effects of headways on knock-on delays of trains , 1994 .

[31]  T. Britton,et al.  Statistical studies of infectious disease incidence , 1999 .