1. School of Economics and Management, Qiqihar University, Qiqihar Heilongjiang 161006, China;
2. School of Economics and Management, Harbin Engineering University, Harbin Heilongjiang 150001, China
Urban Traffic Congestion Load Redistribution Control based on Complex Networks
WANG Shi-bo1,2, ZHAO Jin-lou2, ZHENG Ji-xing1, LI Ai-ping1
1. School of Economics and Management, Qiqihar University, Qiqihar Heilongjiang 161006, China;
2. School of Economics and Management, Harbin Engineering University, Harbin Heilongjiang 150001, China
摘要In recent years, urban traffic congestion has become an important issue that besets the economic development of cities and brings inconvenience to people's productivity and lives. However, traffic congestion is inevitable due to certain factors, such as urban planning, population density, and traffic facilities. Thus, addressing traffic congestion, redistributing congestion load reasonably, and preventing large-scale cascade congestion offer practical research value. In this work, to solve urban traffic congestion, we set an initial load for every traffic network node and then determine whether the node fails or not according to the load threshold value. On the basis of the results, a nonlinear model for failure nodes' load redistribution is proposed. A condition in which the model triggers cascading failure is analyzed, and random and intentional attacks are executed on the Qiqihar urban traffic network. The cascading failures caused by the congestion nodes are simulated with MATLAB, and an understandable method of determining the destructive effect of the attacks is selected. Simulation results show that the model can achieve enhanced robustness against cascading failure by adjusting failure nodes' load redistribution reasonably, reducing the number of cascade failure nodes, and avoiding large-scale cascading failure. This study finds that intentional attacks are more destructive than random attacks when the capacity coefficient is small, but a supersized capacity coefficient is impossible in real life due to various constraints. Therefore, we need to properly adjust the redistribution coefficient of traffic load, redistribute congestion load, effectively reduce the number of failure nodes, and prevent large-scale cascading failure.
Abstract：In recent years, urban traffic congestion has become an important issue that besets the economic development of cities and brings inconvenience to people's productivity and lives. However, traffic congestion is inevitable due to certain factors, such as urban planning, population density, and traffic facilities. Thus, addressing traffic congestion, redistributing congestion load reasonably, and preventing large-scale cascade congestion offer practical research value. In this work, to solve urban traffic congestion, we set an initial load for every traffic network node and then determine whether the node fails or not according to the load threshold value. On the basis of the results, a nonlinear model for failure nodes' load redistribution is proposed. A condition in which the model triggers cascading failure is analyzed, and random and intentional attacks are executed on the Qiqihar urban traffic network. The cascading failures caused by the congestion nodes are simulated with MATLAB, and an understandable method of determining the destructive effect of the attacks is selected. Simulation results show that the model can achieve enhanced robustness against cascading failure by adjusting failure nodes' load redistribution reasonably, reducing the number of cascade failure nodes, and avoiding large-scale cascading failure. This study finds that intentional attacks are more destructive than random attacks when the capacity coefficient is small, but a supersized capacity coefficient is impossible in real life due to various constraints. Therefore, we need to properly adjust the redistribution coefficient of traffic load, redistribute congestion load, effectively reduce the number of failure nodes, and prevent large-scale cascading failure.
基金资助:Supported by the National Naturnal Science Foundation of China (No.71271062)
通讯作者:
WANG Shi-bo
E-mail: wangshibo05@163.com
引用本文:
王世波, 赵金楼, 郑继兴, 李爱萍. 基于复杂网络的城市交通堵塞载荷再分配控制研究[J]. Journal of Highway and Transportation Research and Development, 2019, 13(3): 80-86.
WANG Shi-bo, ZHAO Jin-lou, ZHENG Ji-xing, LI Ai-ping. Urban Traffic Congestion Load Redistribution Control based on Complex Networks. Journal of Highway and Transportation Research and Development, 2019, 13(3): 80-86.
[1] NEWMAN M E J. The Structure and Function of Complex Networks[J]. SIAM Review, 2003, 45(2):167-256.
[2] WATTS D J, STROGATZ S H. Collective Dynamics of "Small-World" Networks[J]. Nature, 1998, 393:440-442.
[3] BARABASI A L, ALBERT R. Emergence of Scaling in Random Networks[J]. Science, 1999, 286:509-512.
[4] WANG Shi-bo, ZHAO Jin-lou. Multi-attribute Integrated Measurement of Node Importance in Complex Networks[J]. Chaos:An Interdisciplinary Journal of Nonlinear Science, 2015, 25(11):113105.
[5] TAN Yue-jin, WU Jun, DENG Hong-zhong et al. Invulnerability of Complex Networks:A Survey[J]. Systems Engineering, 2006, 24(10):1-5. (in Chinese)
[6] WANG Bing-hong, ZHOU Tao. The Trend of Recent Research on Statistical Physics and Complex Systems[J]. China Basic Science, 2005, 7(3):37-43. (in Chinese)
[7] ALBERT R, JEONG H, BARABASI A L. Error and Attack Tolerance of Complex Networks[J]. Nature, 2000, 406(6794):378-382.
[8] MOTTER A E. Cascade Control and Defense in Complex Networks[J]. Physical Review Letters, 2004, 93(9):098701.
[9] CRUCITI P, LATORA V, MARCHIORI M. Model for Cascading Failures in Complex Networks"[J]. Physical Review E:Statistical Nonlinear & Soft Matter Physics, 2004, 69(4):04510
[10] HE Bin, WANG Ruo-en. Matter-element Deductive Inference[J]. Systems Engineering-Theory & Practice, 1998, 18(1):85-92. (in Chinese)
[11] ZHOU Ming-zheng, YANG Yi-min. Implementation of Extension Neural Network for Rhombus-Thinking Process[J]. Systems Engineering Theory & Practice, 2000, 20(6):123-130. (in Chinese)
[12] HONG Yun, ZHANG Bo. Biological Information of Bionics and Extension Data Mining[J]. Information Science, 2009, 27(4):622-625. (in Chinese)
[13] CHEN Wen-wei. Acquisition of an Extensional Knowledge Chain Based on Ontology[J]. CAAI Transactions on Intelligent Systems, 2007, 2(6):68-71. (in Chinese)
[14] YIN Hong-ying, QUAN Xiao-feng. The Cascading Influence Law and Influence Scope of a Failure in Transportation Networks[J]. Journal of Systems & Management, 2013, 22(6):869-881. (in Chinese)
[15] MOTTER A E, LAI Y C. Cascade-based Attacks on Complex Networks"[J]. Physical Review E, 2002,66(6):065102
[16] WANG B, KIM B J. A High-robustness and Low-cost Model for Cascading Failures[J]. Europhysics Letters, 2007, 78:48001.
[17] LI P, WANG B H, SUN H. A Limited Resource Model of Fault-tolerant Capability Against Cascading Failure of Complex Network[J]. European Physical Journal B, 2008, 62(l):101-104.
[18] HUANG Ying-yi, JIN Chun, RONG Li-li. Cascading Failure Model on Logistics Network Based on the Overall Importance of Nodes[J]. Operations Research and Management Science, 2014, 23(6):108-114. (in Chinese)
[19] LEHMANN J, BERNASCONI J. Stochastic Load-redistribution Model for Cascading Failure Propagation[J]. Physical Review E:Statistical, Nonlinear, and Soft Matter Physics, 2010, 81(3):031229.
[20] KIM D H, KIM B J, JEONG H. Universality Class of the Fiber Bundle Model on Complex Networks[J]. Physical Review Letters, 2005, 94(2):025501.
[21] WANG W X, CHEN G R. Universal Robustness Characteristic of Weighted Networks Against Cascading Failure[J]. Physical Review E:Statistical, Nonlinear, and Soft Matter Physics, 2008, 77(2):026202.
[22] YUAN Ming. A Cascading Failure Model of Complex Network with Hierarchy Structure[J]. Acta Physica Sinica, 2014, 63(22):220501.(in Chinese)
[23] AILING H. MICHAEL Z H, WEI G et al. Cascading Failures in Weighted Complex Networks of Transit Systems Based on Coupled Map Lattices[J]. Mathematical Problems in Engineering. 2015.2015:1-16.
[24] LI Cheng-bing, WEI Lei, GAO Wei et al. Invulnerability of Urban Agglomeration Compound Traffic Network against Cascading Failure[J]. Journal of Highway and Transportation Research and Development, 2018, 35(6):95-104. (in Chinese)
2019, Vol. 13 
