Travel time prediction for highway network based on the ensemble empirical mode decomposition and random vector functional link network

Travel time prediction supplies data support for the management and operation of the highway network. To deal with this problem, a model based on ensemble empirical mode decomposition and random vector functional link network is proposed in this paper. Ensemble empirical mode decomposition is firstly employed to decompose the complex travel time data series into several simple functions, which are then represented by the same number of random vector functional link networks. Finally, the outputs of all networks are combined by linear addition as the prediction results. A historical travel time data series ( from 1 August 2016 to 1 November 2016) of two highways in China is investigated by the proposed models. For comparison, five individual prediction models and their respective ensemble variants are implemented for the same task. The results show that the proposed model outperforms all the other models in terms of symmetric mean absolute percentage error and normalized root mean square error. As for computational speed, the proposed model ranks the first among all the ensemble models. Moreover, the ensemble empirical mode decomposition is better than the empirical mode decomposition. The Friedman statistical test also confirms the results of the comparison. Experimental results reveal that the proposed model reaches the best overall performance and is a very promising model for complex travel time prediction. (C) 2018 Elsevier B.V. All rights reserved.