Research On Dynamic Route Guidance Based On Adaptive Quantum Artificial Fish Swarm Algorithm

Intelligent Transportation Systems, which is internationally recognized as the most economic and effective way to solve urban traffic congestion problems, received widespread attention in the community. Dynamic Route Guidance System, as one of the key technologies of Intelligent Transportation Systems, plays an important role in balancing the dynamic allocation of urban road traffic flow. This thesis is mainly based on backgrounds as mentioned and aims at Dynamic Route Guidance Algorithm research based on Adaptive Quantum Artificial Fish Swarm Algorithm, which is the hard core of the Dynamic Route Guidance System components. It is mainly composed of two parts:the establishing of the dynamic network model and the designing of Dynamic Route Guidance Algorithm.The first part:After mathematically and abstractly describing the urban road network, according to the characteristics of urban traffic network and the shortage of the dynamic network, a dynamic network model with link to information and real-time traffic flow information is established, and taking advantage of the road extraction method based on map color clustering feature, extracting the road skeleton of shenyang heping partial district electronic map as reference to build urban traffic network, urban traffic road network topology for solving the Dynamic Route Guidance problem is set up.The second part:the Dynamic Route Guidance Algorithm is the key to the Dynamic Route Guidance System solving optimal path. Therefore the advantages and disadvantages of designed algorithm will directly influence the performance of the real-time and effectiveness of the whole system. We choose the basic Artificial Fish Swarm Algorithm as the basic research object to the Dynamic Route Guidance Algorithm which should meet the requirements of selecting. In order to improve the global optimization ability, to increase the accuracy of searching optimization and improvement of the efficiency of iterative calculation, we improved the basic Artificial Fish Swarm Algorithm, and raised an Adaptive Quantum Artificial Fish Swarm Algorithm. Finally, we presents the solution to solve the dynamic route guidance problem taking advantage of the Adaptive Quantum Artificial Fish Swarm Algorithm.The final experimental results show that using Adaptive Quantum Artificial Fish Swarm Algorithm to solve the dynamic optimal path problem which is restrained with a link to information and with real-time traffic flow information is feasible and effective, and is superior to the basic Artificial Fish Swarm Algorithm and traditional Genetic Algorithm from angle of effeciency and precision.