Repeatability and Similarity of Freeway Traffic Flow and Long-Term Prediction Under Big Data

Abstract

In this paper, by splitting a traffic flow series into basis series and deviation series, the concepts of similarity and repeatability of traffic flow patterns are defined using the statistic average values of the basis series and the deviation series and are further verified through the real-time big traffic data of 82 days with a sampling period of 5 min collected from two typical ones among a total of 102 detecting sites in Shenzhen, China. Meanwhile, based on the repeatability and the similarity of the traffic flow series, a novel long-term forecasting method for traffic flow is developed, and hybrid forecasting algorithms for short-/long-term traffic flow prediction are also proposed. The effectiveness of these algorithms is verified by using the real-time data.