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Understanding the Propagation and Control Strategies of Congestion in Urban Rail Transit Based on Epidemiological Dynamics Model

Intelligent Transportation System Research Center, Southeast University, Nanjing 210008, China
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Information 2019, 10(8), 258; https://doi.org/10.3390/info10080258
Received: 5 July 2019 / Revised: 5 August 2019 / Accepted: 12 August 2019 / Published: 14 August 2019
PDF [509 KB, uploaded 14 August 2019]

Abstract

With the construction of the urban rail transit (URT) network, the explosion of passenger volume is more rapid than the increased capacity of the newly built infrastructure, which results in serious passenger flow congestion (PLC). Understanding the propagation process of PLC is the key to formulate sustainable policies for reducing congestion and optimizing management. This study proposes a susceptible-infected-recovered (SIR) model based on the theories of epidemiological dynamics and complex network to analyze the PLC propagation. We simulate the PLC propagation under various situations, and analyze the sensitivity of PLC propagation to model parameters. Finally, the control strategies of restricting PLC propagation are introduced from two aspects, namely, supply control and demand control. The results indicate that both of the two control strategies contribute to relieving congestion pressure. The propagating scope of PLC is more sensitive when taking mild supply control, whereas, the demand control strategy shows some advantages in flexibly implementing and dealing with serious congestion. These results are of important guidance for URT agencies to understand the mechanism of PLC propagation and formulate appropriate congestion control strategies.
Keywords: passenger flow congestion; propagation; control strategy; complex network theory passenger flow congestion; propagation; control strategy; complex network theory
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Shi, Z.; Zhang, N.; Lei, Z. Understanding the Propagation and Control Strategies of Congestion in Urban Rail Transit Based on Epidemiological Dynamics Model. Information 2019, 10, 258.

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