Research On Optimal Path Of Dynamic Route Guidance System

Now with the development of economy and technology, traffic guidance system gets more and more attention, and intelligent transportation system is the important part and theoretical basis of the traffic guidance system, using a variety of advanced intelligent algorithms to provide real-time and excellence information of paths to travelers that making travelers choose better travel routes, reducing travel time and avoiding traffic congestion, reducing energy consumption, reducing environmental pollution.As the new research direction of the traffic guidance, intelligent traffic guidance systems have become the focus of the study. After this paper studies the various factors affecting transport road and all kinds of bionics algorithms studying for traffic guidance systems deeply, the focus study on traffic guidance model, and based on this study, the two models of dynamic traffic guidance algorithm are proposed.Today bionics algorithm has become the mainstream algorithm of intelligent traffic, and the traditional route guidance algorithm has made a lot of success in the static state, after this paper studies A * algorithm and ant colony algorithm deeply, based on this study an algorithm called A* algorithm based on concurrent reward ant colony system is proposed, this algorithm mainly integrate the maturity of the A* algorithm and the dynamic nature of ant colony algorithm, using ant colony algorithm to make a research for the h(x) belonged to the A* algorithm of the evaluation function: f(x)=g(x)+h(x),what make A* algorithm to be dynamic. Meanwhile, considering the various road traffic dynamic elements, so can improve the efficiency of the algorithm. Based on the theory that the vicinity of the optimal path exist a better one, the concurrent ant colony system is proposed, giving the additional information elements to the path near the optimal path that can avoid the algorithm into the local optimum, and find a better path.Ant colony algorithm is a sophisticated algorithm for dynamic route guidance, which has advantages of the dynamic nature of ant colony system algorithm, positive feedback and distributed computing, but the inherent disadvantage of ant colony system algorithm is easy to fall into local optimization and evolve slowly, in order to improve the shortcoming of the local optimum of ant colony system algorithm, using the global search of immune genetic to avoid the shortcoming of the local optimum of ant colony system algorithm, a kind of algorithm of ant colony dynamic route guidance of multi-route searching was proposed. In order to improve the speed of algorithm evolution, this paper presents ant system algorithm based on multi-search, to speed up the convergence better and met the needs of dynamic changes of traffic, meet the needs of travelers. In the course of studying two algorithms, this paper has carried two experiments, in the eil51 and oliver30 problems, the proposed algorithm compared with other algorithms can get a better solution. Using MapX to simulate real traffic environment and to make use of algorithms to look for a better path, showing that the proposed algorithm can find a better path that can satisfied traveler’s needs in the actual road condition.