Research on urban mixed intersections traffic flow base on cellular automaton

The problem of urban traffic intelligent control is a key technology of modern urban construction. An improve ChSch model base on cellular automaton is constructed in order to conform to the actual situation of traffic. The simulation results show that big intersection is the main bottleneck and the synchronous and fixed traffic signal makes the road in serious congestion when car flow is large. This paper presents an adaptive fuzzy neural network to control semaphores method is used to improve road congestion. Based on fuzzy neural network method, we use RBF neural network as fuzzy rules for learning network, the control signal is improved, and the results show that the two level fuzzy neural network control of traffic flow of the city's main thoroughfares are obviously improved, road more clear.