Traffic Flow Prediction With The Assignment Of The Study

By analyzing limitation of the traditional Neural Network, this paper presents intelligent neuron model based on linear independently function . The knowledge storing capacity of the intelligent neuron is analyzed. It is proved that the intelligent neuron has greater knowledge storing capacity than normal neuron and can strengthen the information processing capability of neural network. In the paper, traffic flow data of DALIAN city is predicted by applying such neural network. Experimental results show that intelligent neural network has higher astringency rate and precision, and has greater advantages than BP network.Promoted by the research work in ITS, dynamic traffic assignment (DTA) theory became a focus research area. Genetic algorithm (GA), as an excellent global optimization algorithm, has been applied extensively. Based on above theoretical bases, in this paper an optimal control model of Master/Slave parallel genetic algorithm to solve DTA problem is proposed. Based on the Drawing cluster with distributed storage and message passing system, this algorithm is implemented in the Master/Slave mode of the PVM parallel platform. An example transportation network is applied to test the model. The experimental results show excellent system operation indices and parallel efficiency, which prove the validity of the model.The application of grid computing on scientific research and engineering problem is support by aggregate distributed high performance compute , large database, scientific research equipment and other substantial computational resources to form a supercomputer. Base on exist hardware and network equipment the grid computing experimental environment is constituted in this paper. The parallel Dijkstra algorithm to solve the short path problem is tested in the experimental environment. Base on exist hardware and network equipment of DUT and DALIAN traffic control system, the DUT computing grid model and traffic control grid system are designed. The implement of traffic flow prediction on grid environment is discussed base on this model.