Study Of Intelligent Control Strategy For Urban Road Traffic

With the increasing development of social economy and urbanization, the urban population and vehicles increase rapidly. Traffic congestion and traffic jam have become prevalent problems for metropolis all over the world. Traffic accident, energy wasting, air pollution caused by exhaust gas and other problems resulted from traffic congestion and traffic jam not only seriously restrict the sustainable development of social economy, but also severely influence the urban living environment.Intelligent transportation system is one of the important ways to solve the antinomy of traffic demand and supply in the modem society. The application of intelligent transportation system will not only improve the transportation safety, production efficiency and revenues, but also connect with land resources and energies exploitation, environment improvement, and national economic and social revenue development.As a developing multidisciplinary subject, intelligent transportation system relates to many facets of the traffic transportation systems. Urban road traffic is the most important part of the traffic transportation systems. Urban traffic intelligent control is the first step to implement the intelligent transportation systems engineering, and also a hot issue in control engineering and traffic engineering fields. Urban road traffic control is a complex systems engineering problem. It involves many subjects of science and technology, such as traffic engineering, automatic control, systems engineering, and optimization scheduling. Urban traffic intelligent control is a multidisciplinary research subject, and the development of urban traffic intelligent control technology depends on the newest research outcomes of science and technology.Applying the newest research of science and technology to urban traffic control systems can consummate traffic control theories and solve increasingly serious urban traffic problems, which is of the most important significance for meeting the social demand, accelerating the progress ofnation and society, and driving the development of subject.Urban road traffic system, as a time-dependent complex great system into which integrates human, vehicles, roads, environment and other complex factors, is of high complexity, time-dependence and randomicity. It is difficult for traditional control methods based on precise mathematical models to solve complex modern urban traffic problems, and the intelligent control technology based on artificial intelligence is a feasible way to solve urban traffic problems.Based upon fuzzy logical, chaos optimization, artificial annual networks, artificial immune systems, rough sets and other artificial intelligence technologies, this dissertation makes a comprehensive and deep research on urban traffic signal control strategies, dynamic route guidance system and the concerned traffic control technologies. After reviewing the traditional traffic control studies, this dissertation puts forward several advanced traffic control ideas, algorithms and models. The main contents in this dissertation include:1) Reviewing the urban road traffic control technology in detail, and deducing the main research contents after making a discussion on the difficulties and problems in this field.2) The fuzzy control of adjacent intersections is studied. A traffic coordination control algorithm for two adjacent intersections using hierarchical fuzzy logic and the idea of adjusting membership functions automatically using chaos optimization are put forward. The two-stage fuzzy controller with this algorithm is designed and simulation results show that the average delay of vehicles in intersections is less using this algorithm than that using the conventional control strategies.3) Concerning the fact that the span between some natural intersections of urban traffic trunk roads is smaller than usual, this dissertation brings forward a new idea to integrate two or three natural intersections whose span is smaller than usual to a built-up intersection, and the concept of "Big Intersection" as well as the control model and algorithm for it is given. In this research, the traffic coordination control algorithm for two adjacent intersections using hierarchical fuzzy logic and the idea of adjustingmembership functions automatically using chaos optimization are generalized to urban trunk traffic control systems, and the urban trunk traffic fuzzy control mechanism using "Big Intersection" is established.4) The urban road traffic route guidance algorithm is studied. To improve the performance of Kth shortest route algorithms in dynamitic route guidance systems, this dissertation suggests a practical dynamitic route guidance stagery, in which a new algorithm based on the metaphors of vertebrate immune system and the ideals of intelligent optimization is proposed. Combined the urban traffic network models built via " extended node method" , this dissertation studies the ATth shortest route search problems in urban dynamitic route guidance systems using the artificial immune optimization algorithm, and discusses the interrelated issues of dynamic route guidance systems. The experiment results show that this route guidance stagery is effective and advanced.5) Combined rough sets theory with fuzzy reasoning together, a new method for modeling traffic fuzzy control systems is put forward to meet the needs of designing fuzzy controllers with high performance in intelligent transportation systems. This method extracts the fuzzy control rules from historical traffic data by knowledge reasoning in rough sets theory and solves the bottleneck problems for modeling urban traffic signal fuzzy control systems. A rough fuzzy model of four-phase intersections is constructed using the method given in this dissertation, and the rough fuzzy modeling method of multi-intersections with more practicabilities than isolated intersections is studied.6) To solve the problems of programming traffic intervals automatically for time of day control scheme in urban traffic control systems, an artificial immune data clustering algorithm based on metaphors from the vertebrate immune systems is put forward. This algorithm has been successfully used in making the time of day schemes of urban traffic control systems. It can get over the irrational intervals from manually programming methods and the hierarchical clustering algorithms based on genetic algorithm. This work supplies a new idea for programming time of day intervals and making urbantraffic control schemes.7) The traffic flow forecasting method based on the artificial neural networks and rough sets is studied. A new forecasting idea with the " similar intervals" is put forward to improve the flow forecasting performance. Combined rough sets with orthogonal wavelet networks together, a new traffic flow forecasting model is put forward, and the model has been successfully used for urban traffic flow forecasting applications. Integrating the excellent performances of wavelet networks and rough networks, combaining with the idea of " similar intervals" , the model can be used for the applications of real time flow accurate forecasting.Finally, we make a conclusion and propose the future research directions in this field.