Basis-Prediction Method for Short-Term Traffic Flow Prediction and Its Application

Abstract

Traffic flow forecasting is one of the key issues in smart traffic systems. The process of traffic flow changing involves a high degree of nonlinearity and randomness, environmental interference and measurement noise, which brings difficulties to accurate traffic flow prediction. Aiming at improving the accuracy of short-term traffic flow prediction, this paper proposes a method called Basis Prediction method. A raw traffic flow series can be deemed as a summation of a basis series that indicates the changeable trend of the traffic flow and a deviation series that represents the random interference information involved in the flow. The basis series comprises mainly low-frequency signals and the deviation series is composed of some high-frequency signals. Based on an appropriate extraction of the basis series, prediction merely of the basis series brings more precise prediction of the raw traffic flow. This paper suggests adopting wavelet decomposition to obtain the basis series and the deviation series from the raw traffic flow. To predict the basis series effectively, two algorithms, local weighted partial least squares (LW-PLS) and Kalman filtering, are adopted separately and a comparison between them is also provided. In this paper, real data of traffic flow of Xinbei city in Taiwan was collected and used for validation of the proposed basis prediction method. The results show that the use of that proposed method improves the accuracy of short-term traffic flow prediction by about 2% on average.