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Singal Control Method And Algorithm Research Based On Fuzzy Control For Single Intersection

Posted on:2009-04-26Degree:DoctorType:Dissertation
Country:ChinaCandidate:W G MaFull Text:PDF
GTID:1102360248455013Subject:Transportation planning and management
Abstract/Summary:PDF Full Text Request
With rapid economic growth, the level of urbanization is improved and t, economic and cultural activities of residents have become increasingly frequent. This led to the rapid urban growth in demand for transport. However, with the rapid development of urban traffic, it also brings some issues such as urban traffic congestion, traffic delays, traffic accidents and traffic pollution. Intersection is the point of intersection of Urban Roads, and plays an important role in the road network and traffic flow. Therefore, it is necessary for the intersection signal control method to in-depth study. Because the transport system is a nonlinear, time-varying, lagging large-scale systems, traditional control methods are very difficult to get satisfactory results. Fuzzy control does not need to build a precise mathematical model, so it will be used to control intersection in this paper.In this paper, the reseaching subject is the single-intersection signal control method in main road, and control objective is to reduce the vehicle average delays. Based on detecting the length of vehicles in every import, the traffic flows of the next cycle will be predicted using fuzzy time series theory. According to predicted data of traffic flow, suitable allocation program for the next cycle is selected. That is, the optimal phase sequence arrangement is determined by the expertise and mathematical derivation. In a cycle, real-time traffic flows are inputing parameters of adaptive fuzzy controller, and the green light delays of the passage phase are outputing parameters . Then to optimize the fuzzy controller using respectively ant colony algorithm and genetic algorithms, MATLAB is used to simulate the optimized fuzzy controller under the different traffic environments, comparing with the general fuzzy controller which worked under the same traffic environment. The results indicates the algorithm can effectively reduce the average delay time of vehicles in the intersection, Correspondingly enhance the passing capacity of the intersection.
Keywords/Search Tags:ITS, Fuzzy control, Single intersection, Multi-phase, Ant colony algorithm
PDF Full Text Request
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