Reliability analysis of urban road traffic network under targeted attack strategies considering traffic congestion diffusion

•Reliability of URTNs considering the traffic congestion diffusion is studied.•An improved NLC model under different attack strategies is proposed to simulate cascading failures.•A load redistribution method with the impedance function is provided.•Formation time, diffusion speed, and scale of cascading failures are analyzed. The cascading failures caused by traffic congestion diffusion may deteriorate traffic network reliability. Comprehending urban traffic congestion mechanisms is essential for road network planning and traffic management against cascading failures. To uncover this, the reliability of urban road traffic network (URTN) under cascading failure considering different attack strategies is analyzed. The cascading failure model is established based on the improved nonlinear load-capacity relationship. Five kinds of attack strategies including Strength Attack (SA), Betweenness Centrality Attack (BCA), Eigenvector Centrality Attack (ECA), Closeness Centrality Attack (CCA), and Random Attack (RA) are selected. In particular, the capacity affected by traffic congestion is considered, providing a new perspective for the study of traffic congestion diffusion. A state update equation for networks is proposed to simulate the network congestion diffusion. Finally, a case study is conducted by using the URTN of Shanghai as the background. The results show that the network will experience large-scale congestion when high-importance nodes are attacked. The congestion degree is the highest under CCA strategy, network efficiency is the lowest under ECA strategy, and traffic quality is the poorest under CCA strategy. As the congestion critical failure threshold decreases, the speed and scale of cascading failures caused by traffic congestion diffusion are greater. Maintaining proper traffic management and control capability can largely reduce the cascading effect to a great extent and improve the reliability of the network. The results can provide a research basis for traffic management to improve network reliability.