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

被引：13

作者：

Zeng Z. ^{[1
]}

Li T. ^{[1
]}

机构：

[1] School of Traffic and Transportation, Beijing Jiaotong University, Beijing

来源：

Urban Rail Transit | 2018年 / 4卷 / 3期

关键词：

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

D O I：

10.1007/s40864-018-0084-6

中图分类号：

学科分类号：

摘要：

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. © 2018, The Author(s).

引用

页码：130 / 140

页数：10

共 43 条

[1]

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

[2]

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

[3]

Li S.B., Wu J.J., Gao Z.Y., Lin Y., D Fu B.B., Bi-dynamic analysis of traffic congestion and propagation based on complex network, Acta Physica Sinica, 60, 5, (2011)

[4]

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

[5]

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

[6]

Roberg-Orenstein P., Abbess C.R., Wright C., Traffic jam simulation, J Maps, 2007, pp. 107-121, (2007)

[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, 14, 3, pp. 215-227, (2006)

[8]

D'Acierno L., Botte M., Placido A., Et al., Methodology for determining dwell times consistent with passenger flows in the case of metro services, Urban Rail Transit., 3, (2017)

[9]

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

[10]

Sundara P., Puan O.C., Hainin M.R., Determining the impact of darkness on highway traffic shockwave propagation, Am J Appl Sci, 10, 9, pp. 1000-1008, (2013)

← 1 2 3 4 5 →