Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian theory

Abstract

Considering the chaotic characteristics of traffic flow, this study proposes a Bayesian theory-based multiple measures chaotic time series prediction algorithm. In particular, a time series of three traffic measures, i.e., speed, occupancy, and flow, obtained from different sources is used to reconstruct the phase space using the phase space reconstruction theory. Then, data from the multiple sources are combined using Bayesian estimation theory to identify the chaotic characteristics of traffic flow. In addition, a radial basis function (RBF) neural network is designed to predict the traffic flow. Compared to the consideration of a single source, results from numerical experiments demonstrate the improved effectiveness of the proposed multi-measure method in terms of accuracy and timeliness for the short-term traffic flow prediction.