Analysis of Congestion Propagation in Urban Road Networks Based on the SIR Model

NAVIGATE

Table of Contents

STATISTICS

Viewed52

Downloads30

Analysis of Congestion Propagation in Urban Road Networks Based on the SIR Model

PDF下载 (30)

[1]ZHENG Changjiang,ZHOU Sida,ZHENG Shukang,et al.Analysis of Congestion Propagation in Urban Road Networks Based on the SIR Model[J].Journal of Zhengzhou University (Engineering Science),2025,46(01):51-58.[doi:10.13705/j.issn.1671-6833.2025.01.019]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 01 Page number: 51-58 Column: Public date: 2024-12-23

Title:: Analysis of Congestion Propagation in Urban Road Networks Based on the SIR Model

Author(s):: ZHENG Changjiang¹; ZHOU Sida¹; ZHENG Shukang²; MA Genghua³; ZHANG Bo¹; DAI Jinwen¹; 1.College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China; 2.College of Environment, Hohai University, Nanjing 210098, China; 3.College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China

Keywords:: SIR model; urban traffic; congestion propagation; road node degree; road saturation

CLC:: U491TP301

DOI:: 10.13705/j.issn.1671-6833.2025.01.019

Abstract:: It is crucial to studying the propagation patterns of urban traffic congestion in alleviating traffic problems. A propagation model of urban traffic congestion based on the SIR to analyze the propagation process of urban traffic congestion was established. Firstly, the road dual topology network was constructed according to the actual urban road network. The traffic congestion propagation model was developed based on the SIR. Considering the complex network characteristics of road networks and the relevant attributes of roads themselves, the random forest algorithm was introduced to compute the corresponding weights, determining key parameters such as the propagation rate in the congestion model. Subsequently, using a specific area road network in Qinhuai District, Nanjing City as an example, a city road dual topology network with 69 nodes and 163 links was constructed for simulation experiments. The results indicated that road node degree and road saturation were key factors influencing the spread of road congestion. The impact of road node degree was relatively small, with the propagation range increasing by within 5% and the recovery time being affected by around 10%. In contrast, the impact of road saturation was relatively large. As saturation increased, the propagation range could grow by up to 40%, and the recovery time could be affected by around 20%.

References:: [1]SUN H J, WU J J, MA D, et al. Spatial distribution complexities of traffic congestion and bottlenecks in different network topologies[J]. Applied Mathematical Modelling, 2014, 38(2): 496-505.
[2]WEI L, CHEN P, MEI Y, et al. A hierarchical control framework for alleviating network traffic bottleneck congestion using vehicle trajectory data[J]. Journal of Intelligent Transportation Systems, 2023: 1-23.
[3]HAMEDMOGHADAM H, ZHENG N, LI D Q, et al. Percolation-based dynamic perimeter control for mitigating congestion propagation in urban road networks [J]. Transportation Research Part C: Emerging Technologies, 2022, 145: 103922.
[4]罗荣辉, 袁航, 钟发海, 等. 基于卷积神经网络的道路拥堵识别研究[J]. 郑州大学学报(工学版), 2019, 40(2): 18-22.
LUO R H, YUAN H, ZHONG F H, et al. Traffic jam detection based on convolutional neural network[J]. Journal of Zhengzhou University (Engineering Science), 2019, 40(2): 18-22.
[5]ZENG J, XIONG Y, LIU T Y, et al. Uncovering the spatiotemporal patterns of traffic congestion from largescale trajectory data: a complex network approach [J]. Physica A: Statistical Mechanics and Its Applications, 2022, 604: 127871.
[6]WANG J W, HE J L, CHEN W, et al. Abnormal dynamics of cascading edge failures with congestion effect [J]. International Journal of Modern Physics C, 2018, 29(10): 1850095.
[7]LUAN S, KE R M, HUANG Z, et al. Traffic congestion propagation inference using dynamic Bayesian graph convolution network[J]. Transportation Research Part C: Emerging Technologies, 2022, 135: 103526.
[8]CHEN Y T, MAO J N, ZHANG Z, et al. A quasi-contagion process modeling and characteristic analysis for realworld urban traffic network congestion patterns[J]. Physica A: Statistical Mechanics and Its Applications, 2022, 603: 127729.
[9]WANG Z S, GUO Q T, SUN S W, et al. The impact of awareness diffusion on SIR-like epidemics in multiplex networks [J]. Applied Mathematics and Computation, 2019, 349: 134-147.
[10]代晓旭, 胡明华, 田文, 等. 利用传染病模型研究空中交通拥挤传播规律[J]. 交通运输系统工程与信息, 2015, 15(6): 121-126.
DAI X X, HU M H, TIAN W, et al. Mechanisms of congestion propagation in air traffic management based on infectious diseases model[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(6): 121-126.
[11]贾锦秀, 朱昌锋, 方劲皓, 等. 基于SIR的多层次轨道交通客流拥堵传播研究[J]. 兰州交通大学学报, 2023, 42(3): 39-45.
JIA J X, ZHU C F, FANG J H, et al. Research on congestion propagation of multi-level rail transit passenger flow based on SIR[J]. Journal of Lanzhou Jiaotong University, 2023, 42(3): 39-45.
[12] ZENG Z L, LI T X. Analyzing congestion propagation on urban rail transit oversaturated conditions: a framework based on SIR epidemic model[J]. Urban Rail Transit, 2018, 4(3): 130-140.
[13] SABERI M, HAMEDMOGHADAM H, ASHFAQ M, et al. A simple contagion process describes spreading of traffic jams in urban networks[J]. Nature Communications, 2020, 11: 1616.
[14]姚佼, 鲍雨婕, 李俊杰. 交通事故下基于CA-SIR模型的高速公路拥挤传播[J]. 公路交通科技, 2023, 40 (4): 170-178.
YAO J, BAO Y J, LI J J. CA-SIR model based congestion propagation of expressway in accident scenario[J]. Journal of Highway and Transportation Research and Development, 2023, 40(4): 170-178.
[15]陈玉婷, 晏启鹏, 毛剑楠, 等. 基于SIS传播理论的城市交通拥堵传播模型[J]. 重庆交通大学学报(自然科学版), 2023, 42(6): 103-110.
CHEN Y T, YAN Q P, MAO J N, et al. Urban traffic congestion propagation model based on SIS propagation theory[J]. Journal of Chongqing Jiaotong University (Natural Science), 2023, 42(6): 103-110.
[16]张俊锋, 马昌喜, 吴芳, 等. 复杂城市交通网络拥堵传播的改进SIS模型[J]. 交通运输研究, 2015, 1 (6): 20-25.
ZHANG J F, MA C X, WU F, et al. Improved SIS model of congestion propagation of complex urban traffic network[J]. Transport Research, 2015, 1(6): 20-25.
[17]WANG J W, ZOU L Z, ZHAO J, et al. Dynamic capacity drop propagation in incident-affected networks: traffic state modeling with SIS-CTM[J]. Physica A: Statistical Mechanics and Its Applications, 2024, 637: 129536.
[18] PORTA S, CRUCITTI P, LATORA V. The network analysis of urban streets: a primal approach[J]. Environment and Planning B: Planning and Design, 2006, 33 (5): 705-725.
[19] MARFIA G, ROCCETTI M. Vehicular congestion detection and short-term forecasting: a new model with results [J]. IEEE Transactions on Vehicular Technology, 2011, 60(7): 2936-2948.
[20] CHOW A H F, SANTACREU A, TSAPAKIS I, et al. Empirical assessment of urban traffic congestion[J]. Journal of Advanced Transportation, 2014, 48(8): 1000-1016.
[21] NGUYEN H, LIU W, CHEN F. Discovering congestion propagation patterns in spatio-temporal traffic data[J]. IEEE Transactions on Big Data, 2017, 3(2): 169-180.
[22] MA X L, DAI Z, HE Z B, et al. Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction[J]. Sensors, 2017, 17(4): 818.
[23]叶晓飞, 包哲宁, 邓社军, 等. 信息化条件下交通拥堵瓶颈识别与扩散判别[J]. 武汉理工大学学报(交通科学与工程版), 2014, 38(5): 1060-1064.
YE X F, BAO Z N, DENG S J, et al. Evaluating the impacts of travel information on urban traffic congestion propagation and bottlenecks identification[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2014, 38(5): 1060-1064.
[24]中华人民共和国交通运输部. 公路工程技术标准: JTG B01—2014[S]. 北京: 人民交通出版社, 2015.
Ministry of Tansport of the People′s Republic of China. Technical standard of highway engineering: JTG B01— 2014[S]. Beijing: China Communications Press, 2015.
[25] LI Y, LIU Y L, ZOU K. Research on the critical value of traffic congestion propagation based on coordination game [J]. Procedia Engineering, 2016, 137: 754-761.

Similar References:

Memo

Last Update: 2024-12-30