EngineeringComputer ScienceForecastingMachine LearningTransportation EngineeringIntelligent Transportation SystemsReal-time ForecastingTime Series PredictionTraffic EngineeringTraffic Flow AnalysisTraffic Flow TheoryMachine Learning For Traffic AnalysisNeural ForecastingReal-time Traffic ForecastingSimulation AnalysisTraffic Flow ForecastingTraffic Flow PredictionTraffic ForecastingTraffic Prediction
Intelligent transportation system (ITS) is an effective measure to solve the problem of traffic jam. Accurate real-time predication of traffic flow is the key technology of ITS. In this paper a dynamic traffic flow forecasting model based on neural network is proposed. BP and RBF neural network are used to build the forecasting models. The data pre-handle method and the judgment criterion of the forecasting model are given. Simulation shows the traffic flow forecasting method is effective, and the RBF can be more fast and effective in forecasting the traffic flow by simulation analysis.
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