Hourly traffic flow forecasting using a new hybrid modelling method

Abstract

Short-term traffic flow forecasting is a significant part of intelligent transportation system. In some traffic control scenarios, obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures. In hence, it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning. This paper proposed a new hybrid model for traffic flow forecasting, which is composed of the variational mode decomposition (VMD) method, the group method of data handling (GMDH) neural network, bi-directional long and short term memory (BILSTM) network and ELMAN network, and is optimized by the imperialist competitive algorithm (ICA) method. To illustrate the performance of the proposed model, there are several comparative experiments between the proposed model and other models. The experiment results show that 1) BILSTM network, GMDH network and ELMAN network have better predictive performance than other single models; 2) VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model. The effect of VMD method is better than that of EEMD method and FEEMD method. To conclude, the proposed model which is made up of the VMD method, the ICA method, the BILSTM network, the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.

Keywords

traffic flow forecasting / intelligent transportation system / imperialist competitive algorithm / variational mode decomposition / group method of data handling / bi-directional long and short term memory / ELMAN

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