The Research Of Equilibrium Approach Of Local Road Traffic

With the national economic development and social progress, our city transportation won a great development, greatly enhanced the level of transport infrastructure. Because of the quantity of motor vehicles and path volume of traffic sharp growth, presented such as the traffic congestion jamming, the labor productivity to drop, the energy waste greatly and so on. Not only brings inconveniently for people's daily life and work, but also as an important factor of constraint to the economic development. How to solve contradiction between the path supply-demand and the traffic flow magnitude, seek the convenient routes and enhance the capacity of the road network has become a urgent question to be solved.In this thesis, based on the knowledge and technology of Heuristic Search, Machine Learning within the field of Artificial Intelligence to solve the issue of the future development of urban road planning, its fundamental purpose is to improve the efficiency and convenience of the path planning.At home and abroad, the Intelligent Transportation System has evolved from static to dynamic induction so that vehicles on the road make use of road resources to achieve optimal allocation.In this thesis, comprehensive consideration of road path, road traffic flow and other factors based on the graph and Dijkstra algorithm, designed a Intelligent Transportation System, it takes VC++6.0 as development environment and applies Hydromechanics knowledge to the city traffic. it can search the shortest path and give alternate paths by the improved Dijkstra algorithm, and according to path selected by user to obtain real-time traffic information of the specified road section, access to the meteorological database and road sections database to provide real-time information for the traveler and dynamically regulate the road traffic flow, so as to achieve a balanced use of road, avoid network congestion.In the last chapter of this thesis, designs and implements the system and all module functions that attached the specific instructions, and gives the result of each module.