Approach for Short-Term Traffic Flow Prediction Based on Empirical Mode Decomposition and Combination Model Fusion

Abstract

Accurate prediction of the traffic state can help to address the issue of traffic congestion, providing guiding advices for people's travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction approach based on empirical mode decomposition and combination model fusion. First, we explore the amplitude-frequency characteristics of short-term traffic flow series, and use empirical mode decomposition to decompose traffic flow to several components with different frequency. Second, based on the results of self-similarity analysis of each component, improved extreme learning machine, seasonal auto regressive integrated moving average and auto regressive moving average are selected to predict different components. Meanwhile, an improved fruit fly optimization algorithm is proposed to optimize the weight coefficient of the combination model. Third, the prediction results of each prediction model are multiplied by their respective weight coefficient to get the final prediction results. We evaluate our prediction approach by doing thorough experiment on a real traffic data set. Moreover, experimental results show that the proposed approach has superior performance than state-of-the-art prediction methods or models in short-term traffic flow prediction.