A Hybrid Short-term Traffic Flow Forecasting Method Based on EMDW-LSSVM

Abstract

Robust and accurate short-term traffic flow prediction plays an important role in ITS. In terms of promoting the prediction accuracy and stability, prediction models and traffic flow characteristics are of equal importance. However, most of the existing literature concentrate on the performance of models and ignore the predictability of traffic flow data itself. In this paper, in order to make a breakthrough in predicting traffic flow with large fluctuation, a traffic flow prediction model based on decomposition and reconstruction is established. First, empirical mode decomposition (EMD) and wavelet threshold (WT) methods are combined to produce an EMDW method, which can decompose and reconstruct original traffic flow into stable sub-sequences with enhanced predictability and reduced noise. Second, combining the proposed EMDW method with least square support vector machine (LSSVM), a hybrid EMDW-LSSVM model is introduced to conduct short term traffic prediction. At last, the field data of Beijing 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> ring road are employed to conduct experiments, proving that the proposed decomposition and reconstruction method can dramatically improve the accuracy of short-term traffic flow prediction.