The Control Technique Research On Multiphase Traffic Signal In Isolated Intersection Based On Flow Prediction

In modern times, the intelligent control of urban traffic is an important part of ITS. The intersection acts as the key factor in deciding the road traffic. The traffic control system bases on real time control to isolated intersection. The precondition and key of intelligent control of urban traffic is real time and exact traffic flow prediction. Collecting the exact and complete traffic information is the basic ensure of finishing intelligent traffic. Besides, the veracity of data samples decides the veracity of predicting traffic flow. Therefore, developing the intelligent technology of urban traffic, researching the following technologies: intelligent traffic control of urban isolated intersection, traffic flow prediction and traffic parameters collection are the development direction of ITS.The intelligent traffic control of urban iaolated intersection, flow prediction and image collection technologies are researched and discussed in this paper. Firstly, the paper puts forward the whole structure design of urban traffic, which includes three modules: collecting vehicle parameters, predicting vehicle flow and controlling traffic signal. Secondly, the detailed design of each module is introduced. In the module of collecting vehicle parameters, the image processing technology based on video is adopted. The paper adopts background difference based on YCbCr color space to divide up the image, and math morphologic and the connectedness of image to eliminate the noise of image. The way of grads extremum is putted forward to check the vehicle flow. In the module of predicting vehicle flow, after analyzing the existent question in traffic flow prediction and the characteristic of traffic flow, a fuzzy neural network(FNN) prediction model and the learning algorithm of FNN based on the associative way of ant colony optimization(ACO) algorithm and particle swarm optimization(PSO) algorithm.are putted forward The learning algorithm is formulated in a form of hierarchical structure. The global search is performed by ant population at the master level, while the local search is carried out by particle population at the slave level and the best solution is fed to the ant population. In the module of controlling traffic signal, the paper establishes a traffic signal control model adopting FNN and the learning algorithm of FNN based on PSO. Finally, the traffic signal controller based DSP is designed, the hardware and software designs are presented. The simulation results demonstrate the proposed models can improve accuracy in taking count of vehicle flow, predicting vehicle flow and controlling traffic signal.