Short-term Travel Time Prediction Model Based on Secondary Correction

Abstract:

In order to increase both of the accuracy and the robustness for freeway short-term travel time prediction, as well as easing the over-fitting effect, which was brought by the extra training, a hybrid model was proposed on the basis of wavelet neural network and Markov chain. The forecasting performance of different models was examined by three measures, i.e., mean absolute error, mean absolute percentage error, root mean square error. The results show that the proposed hybrid model enjoys obvious superiority over the other models after the break point of travel time. Furthermore, no predictiondelay was observed in the prediction of break point of travel time. In conclusion, the higher prediction accuracy and the better robustness were found in the hybrid model in peak hours.