Urban Traffic Congestion Load Redistribution Control Based on Complex Networks

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.