Forecasting Model for Urban Traffic Flow with BP Neural Network based on Genetic Algorithm

In this paper, urban traffic flow forecasting model is built with back propagation (BP) neural network based on genetic algorithm (GA). The model is established based on the real-time traffic data includes the phase start time, phase length, signal cycle length and traffic flow. To make sure that the collected traffic data is accurate, the traffic data is preprocessed by correcting the unusual data with data filtering method. BP neural network is then used to establish the traffic flow prediction model. The weights and thresholds are optimized by using GA. Finally, the measured data is used to test the prediction model and the results show that the proposed model can forecast the traffic flow effectively.

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