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Travel time is a fundamental measure in transportation, and accurate travel time forecasting is crucial in intelligent transportation systems (ITSs). Currently, many techniques have been applied to travel time forecasting; however, the reliability of the prediction has not been studied in these approaches. In this paper, we propose an approach using the generalized autoregressive conditional heteroscedasticity (GARCH) model to study the volatility of travel time and supply the information about reliability for travel time forecasting. Three examples on real urban vehicular traffic data show the whole modeling process. In the experiments, we utilize the conditional predicted standard deviation (PSD) to express the reliability of travel time forecasting and screen out the sample points that are thought to be reliable forecasting. The results show that the root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) are all decreasing with an increase in the demand of the reliability. It proves that the model well depicts the reliability of travel time forecasting and that the proposed approach is feasible.
Yang et al. (Fri,) studied this question.