Research Of Urban Traffic Signal Control Based On Artificial Intelligence

With the development of ITS (Intelligent Transportation Systems), urban traffic signal control has been growing into one of the most important research aspects. As the complexity of urban transportation, traditional methods are not able to settle the problem of signal control efficiently. This paper deals with urban signal control by means of intelligent control algorithm, and focuses on the following aspects:1. a self-learning signal control system based-on RBF Neural Network is established. This system can simulate the traffic police's experience. According to the queue length in each intersection, the system can give out both the signal cycle and the split of each intersection. Furthermore, it can evaluate the effect of the control with the changing of the traffic, and then adjust the signal. Simulation results reveal that the system can much more perfectly control the actual traffic condition and improve the passing ability of the intersection.2. a kind of real time arterial signal control method based-on phase sequence optimization and Fuzzy Neural Networks is put forward in this paper, and a coordinated control module is established using two-stage controller: intersection controller (The lower course) and coordinated controller (The upper course). The lower course of the module consists of two controllers: phase sequencer and green-time delayer. The two controllers are used to optimize the phase sequence and adjust the green-time delay respectively. The upper course of the module is responsible for adjusting the signal cycle of the traffic trunk road and the offset between two intersections. The results of simulation prove that the proposed module has better performance than traditional fixed-time control.3. In the end, summarizing the whole work and pointing out some content which would. be researched in the future.