The Research On Cascading Failure And Control Methods In Urban Road Traffic Networks

In urban road traffic networks, some vital nodes or sections may fail due to the bad weather or the traffic accident, and these failures may result in the failure of other nodes or sections because of interconnection of nodes and sections, setting off a chain reaction, triggering the failure of global local traffic network, it was called "cascading failure in urban road traffic networks". The possibility of cascading failure of traffic networks is becoming greater because the traffic congestion is increasingly serious, and the extend of damage is greater due to people’s increasing dependence on vehicles. On the basis of the cascading failure model in urban road traffic networks, this dissertation mainly studies three aspects,the capacity of road network, the key nodes, the strategies to control cascading failure. The main research contents of this dissertation are listed as follows:1. Research on the cascading failure Lode-Capacity model in urban road traffic networksFirst, the characteristic of urban road traffic network which includes the structure of bi-level traffic network, the path choice of travelers, blocking effect of sections. Based on it, through the improving of the existing cascading failure model, two cascading failure models were built. One is called cascading failure ML model where the node was deleted once the node was overloaded, the other is called cascading failure CLM model where the practical capacity of the nearest neighbor declined when the node was overloaded.2. Research on Capacity of Urban Road Network Considering Cascading Failure (diffusion of congestion)Due to the process of diffusion and dissipation of congestion, the capacity of road network is changing, while the variation of section or node capacity influences the congestion propagation. Firstly, this paper presents a bi-level programming model of road network capacity considering congestion propagation. On the one hand, we built cascading failure model describing the process of diffusion and dissipation of congestion in the lower-level programming. On the other hand, we calculated the maximum demand which the network can satisfy. in the level of road network service which is measured by the degree of road network congestion. Secondly, a heuristic algorithm is designed to slove the bi-level programming model. Finally, we apply this model to a simple network to prove the model and algorithm, simulation results show that cascading failure has a great influence on road network capacity.3. Identification of the key node based on the cascading failure ML model and CLM model in urban road traffic networksSince the failure of some key nodes or sections have potential to trigger a serious cascade in urban traffic network, it was effective to prevent cascading failure through the protection of key nodes or sections in advance. The key nodes identification methods and algorithm procedures based on two cascading failure model (the ML model and CLM model) were proposed. The experiment ananysis showed the differences of key node identification in different cascading failure model, and it was effective to improve the accuracy of identification through the coordinated identification of different cascading failure model.4. Research on strategies of controlling cascading failure in urban road traffic networks from the road network and trip networkOwing to the characteristic of bi-level network, trategies of controlling cascading failure were discussed from two aspects:the optimization of the road network and the optimization of the road network. Three trategies of controlling cascading failure were proposed in the dissertation:(1) Nodes and sections closing strategies. A effective strategy by closing the nodes which are trip generation or attraction nodes (named directly connected nodes)and the sections which is in the upper of important sections was posed to decrease the size of cascade. Moreover, algorithm procedures which have good scalability are designed. (2) Traffic guidance strategies. A traffic guidance strategy based on inductive link resistance(ILR) function was posed and was proved by the experiment. (3) Travel demand management and control strategies. ①Trip network optimization algorithm was built based upon improving the degree of match between trip network and road network. The experiment analysis showed that the algorithm could optimize the structure of trip network and alleviate traffic congestion effectively. ② Demand control strategiy considing of cascading failure was posed. The experiment analysis showed that it can prevent the cascade from propagating through the traffic network.