Fuzzy Control And Algorithm Of Intersections In ITS

Intelligent transportation system (ITS) is one of the important ways to solve the important approach of traffic demand and supply in the modem society and the best way to solve the contradiction between the slow-increasing basic traffic establishment and the fast-increasing number of vehicles. More and more phenomenon indicates that the city traffic jam is usually manifested in the intersections, which are the converge points and play important role in road net and traffic flow.This thesis is mainly focused on designing new models and control arithmetic to single intersection and trunk road traffic. In this paper, fuzzy control method in multi-phase intersections based on variable phase sequence is put forward. The average delay time are used to determine the overtime of green light and the numbers of delay on all next time for green light phase. MATLAB is exploited to simulate the multiphase fuzzy control system. The result showed that which can reduce the average delay time and to improve he traffic power of the traffic vehicle in grade intersection effectively. And, this paper use hierarchy fuzzy coordinated control method to solve the problem of traffic control for the trunk roads. The first level is manipulative level that calculates the parameter .The second level is coordinated level that correct the cyclical, phase and split of intersections by consider the traffic of trunk roads and intersections. Simulation result shows that hierarchical control fuzzily algorithms is better than the fixed coordination control algorithms in the traffic junction coordination control. To meet the needs of designing fuzzy controllers with high performance in intelligent transportation systems, in this paper, ant colony algorithm is used, in order to get better control results with less control rules and select several good fuzzy rules from a completed fuzzy rule base which is used to construct the Fuzzy controller. MATLAB is used to simulate the optimized fuzzy controller under the different traffic environments, and comparing with the out optimized fuzzy controller which worked under the same traffic environment. The results indicates that the intelligent control technology which combined fuzzy control with Ant colony algorithm could lower the average delay time inthe flat surface for the vehicle which was going through the single intersection than the time controlling and it is easier to adapt to the complicated and versatile transportation environment.