Mitigation strategy of cascading failures in urban traffic congestion based on complex networks

Rongrong Yin () and Xudan Song
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Rongrong Yin: School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, P. R. China
Xudan Song: School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, P. R. China

International Journal of Modern Physics C (IJMPC), 2023, vol. 34, issue 02, 1-20

Abstract: Urban road traffic network is becoming increasingly complex. The increasing travel demand has exceeded facility supply of transportation system. Thus, a scientific way of transportation can solve the problem of traffic congestion. If some central intersections are congested, the road network could easily lose its travel efficiency and further lead to cascading failure. In this regard, from the viewpoint of complex networks, this paper provides a cascading failure mitigation strategy for the urban road traffic network. A load redistribution strategy is represented which can redistribute congestion load reasonably. Random and intentional failures are simulated with MATLAB on Barabási and Albert (BA) scale-free network. The indices related to complex network of different methods are compared on BA network and part of the real road network in Baoding City. The load redistribution strategy in this paper is implemented combined with user equilibrium assignment, then the travel time related to urban road network is compared with another method on the Sioux Falls network. The simulation results suggest that when the capacity parameter reaches the threshold, further increase has little effect on the mitigation time. Comparisons of other mitigation approaches verify the effectiveness of our approach in lessening cascading failure caused by traffic congestion. The increase in total network travel time and additional delay by reason of load redistribution is acceptable.

Keywords: Complex network; traffic congestion; cascading failure mitigation; load redistribution (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0129183123500225

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