Highway Travel Time Prediction Based on Multi-source Data Fusion

In order to predict highway travel time accurately, toll collection data and microwave detection data are fused for travel time prediction. First, based on two prediction results, the decision level fusion strategy is determined. Then, the weight distribution model and back propagation neural network model are selected as the basic models. The next, because the neural network convergence is slow and easy to fall into local optimum, the Genetic algorithm is adopted to optimize the neural network model. Finally, on Beijing segment of Jingha Highway, suitable performance indices are proposed to compare the performance of three models in different traffic states, including weekday and weekend. The results show that the fusion model based on GA- BP neural network based on microwave detection data and toll data produced sufficient accuracy and stability. The Mean Relative Error (MPE) of all prediction periods are less than 10% for the