Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast

We propose a novel approach for network-wide traffic state prediction where the statistical time series model ARIMA is used to postprocess the residuals out of the fundamental machine learning algorithm MLP. This approach is named as NN-ARIMA. Neural Network MLP is employed to capture network-scale co-movement pattern of all traffic flows, and ARIMA is used to further extract location-specific traffic features in the residual time series out of Neural Network. The experiment results show that the postprocessing the residuals of Neural Network by the ARIMA analysis helps to significantly improve accuracy of traffic state prediction by 8.9–13.4% in term of mean squared error reduction. In order to verify the efficiency of the ARIMA analysis in the postprocessing, Multidimensional Support Vector Regression (MSVR) model is also employed to replace the role of Neural Network in the comparative experiment. Two streams of comparisons, (1) NN vs. NN-ARIMA and (2) MSVR vs. MSVR-ARIMA, are performed and show consistent results. The proposed approach not only can capture network-wide co-movement pattern of traffic flows, but also seize location-specific traffic characteristics as well as sharp nonlinearity of macroscopic traffic variables. The case study indicates that the accuracy of prediction can be significantly improved when both network-scale traffic features and location-specific characteristics are taken into account.