Dynamic Cascading Failure Analysis in Congested Urban Road Networks With Self-Organization

An efficient freight transportation system is a core part of the modern urban logistics. The transportation system depends on an efficient urban road network. In urban road networks, the failure of some components leads to others failing in succession, triggering cascading failures. This may cause the collapse of the freight transportation system. A cascading failure model is proposed for analyzing the propagation of failures on urban road networks, which can provide guidance for the planning of freight transportation routes in case of emergency. When analyzing the cascading failure process, failed components are commonly deleted permanently. Travelers will avoid failed components, resulting in no increase in traffic flow on the failed components. At the same time, vehicles on these failed components will evacuate slowly to alleviate congestion. Considering this point, this paper proposes a dynamic cascading failure model. This model is established based on a hybrid routing strategy in which traffic flow is distributed according to global and local information. Specifically, the new input traffic demand is assigned along efficient paths in an urban road network (using global information). Efficient paths are determined according to the weights of all edges in urban road networks. Meanwhile, failed edges transfer some traffic flow to their normally working neighbors by self-organization (using local information). The proportion of transferred traffic flow is determined by the residual capacity of the failed edges’ normally working neighbors. The feasibility of the proposed model is verified in a partial road network in Changchun, China.

[1]  Erik Jenelius,et al.  Vulnerability and resilience of transport systems : A discussion of recent research , 2015 .

[2]  E. Jenelius Network structure and travel patterns: explaining the geographical disparities of road network vulnerability , 2009 .

[3]  Liang Chen,et al.  A Stochastic Model of Cascading Failure Dynamics in Communication Networks , 2018, IEEE Transactions on Circuits and Systems II: Express Briefs.

[4]  MengChu Zhou,et al.  Disassembly Sequence Planning Considering Fuzzy Component Quality and Varying Operational Cost , 2018, IEEE Transactions on Automation Science and Engineering.

[5]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[6]  F. Webster TRAFFIC SIGNAL SETTINGS , 1958 .

[7]  K B Davidson,et al.  THE THEORETICAL BASIS OF A FLOW-TRAVEL TIME RELATIONSHIP FOR USE IN TRANSPORTATION PLANNING , 1978 .

[8]  MengChu Zhou,et al.  Dual-Objective Scheduling of Rescue Vehicles to Distinguish Forest Fires via Differential Evolution and Particle Swarm Optimization Combined Algorithm , 2016, IEEE Transactions on Intelligent Transportation Systems.

[9]  Ziyou Gao,et al.  Cascading failures on weighted urban traffic equilibrium networks , 2007 .

[10]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[11]  Yixiong Feng,et al.  Big Data Analytics for System Stability Evaluation Strategy in the Energy Internet , 2017, IEEE Transactions on Industrial Informatics.

[12]  Ziyou Gao,et al.  Modeling cascading failures in congested complex networks , 2007 .

[13]  Massimo Marchiori,et al.  Model for cascading failures in complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  D. McFadden Conditional logit analysis of qualitative choice behavior , 1972 .

[15]  U. Brandes A faster algorithm for betweenness centrality , 2001 .

[16]  H. J. Herrmann,et al.  Failure and recovery in dynamical networks , 2016, Scientific Reports.

[17]  Adilson E Motter,et al.  Cascade-based attacks on complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Chuan Ding,et al.  Analysis of Road Traffic Network Cascade Failures with Coupled Map Lattice Method , 2015 .

[19]  V. E. Lynch,et al.  Critical points and transitions in an electric power transmission model for cascading failure blackouts. , 2002, Chaos.

[20]  Tom Petersen,et al.  Importance and Exposure in Road Network Vulnerability Analysis , 2006 .

[21]  Marc Timme,et al.  Dynamically induced cascading failures in power grids , 2017, Nature Communications.

[22]  Ziyou Gao,et al.  Effects of the cascading failures on scale-free traffic networks , 2007 .

[23]  C. Bhat Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model , 2001 .

[24]  Makan Fardad,et al.  On the Induction of Cascading Failures in Transportation Networks , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[25]  Wolfgang A. Halang,et al.  Understanding the cascading failures in Indian power grids with complex networks theory , 2013 .

[26]  E. Cascetta Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator , 1984 .

[27]  Vito Latora,et al.  The network analysis of urban streets: A dual approach , 2006 .

[28]  Tao Zhou,et al.  A limited resource model of fault-tolerant capability against cascading failure of complex network , 2007, 0708.4023.

[29]  Li Feng,et al.  Cascading failures in congested complex networks with feedback , 2009 .

[30]  V. Latora,et al.  The Network Analysis of Urban Streets: A Primal Approach , 2006 .

[31]  Yang Yang,et al.  The unfolding and control of network cascades , 2017 .

[32]  Yong Peng,et al.  A hybrid multi-objective optimization approach for energy-absorbing structures in train collisions , 2019, Inf. Sci..

[33]  Vito Latora,et al.  Modeling cascading failures in the North American power grid , 2005 .