Study Of Urban Trffic Route Guidance Under Vehicle Network

With the development of social economy and the improvement of people's living standard, the capacity of existing urban road network is difficult to meet the surging traffic demand, and traffic route choice was at work in traffic flow distribution. As travelers disunderstand the status of real-time road network,The traffic flow of each section is unevenly distributed, leading to traffic congestion,which makes the traffic capacity of the road network decline. Therefore, the real time of urban traffic route guidance needs to break through. This paper combines vehicle networking technology to research the key technology of urban traffic route guidance, providing real-time and efficient information service for vehicles, achieving the equilibrium distribution of traffic flow in road network, maximizing the capacity of the existing road network, easing traffic jams.Firstly, analyze the demand of traffic information collection, and a frame of traffic information collection technology undering the vehicle network is built based on the existing information collection technology, which can ensure real-time and comprehensive of traffic information. Secondly in order to improve the accuracy of vehicle location information, put forward a RFID assisted GPS positioning technology with considering the interference of high buildings, parking lots, tunnels on GPS. The RFID assisted GPS positioning technology using RFID information to correct the positioning error of GPS. Then summarize and analyze the current research situation and existing problems of the short-term traffic flow forecasting technology, and give the research trend of the future short-term traffic prediction technology. Lastly Ant Colony Algorithm is used as route optimization algorithm. It is improved from three aspects:the heuristic factor,the dynamic route replanningand the different pheromone attenuation parameter.The simulation environment of Matlab is used to realize the traffic route guidance, and it confirm the feasibility of Ant Colony Algorithm in real-time traffic guidance.