Pattern-Based Short-Term Traffic Forecasting under Urban Heterogeneous Conditions

Abstract

Short-term traffic flow forecasting plays a significant role in the Intelligent Transportation Systems (ITS). Previous research shows that the successful predictability decreases sharply in multiple-steps-ahead prediction due to the fact that freeway traffic state changes abruptly under heterogeneous traffic conditions. This paper proposes a pattern-based forecasting framework that is consistent with the evolution of traffic flow and has the ability to exploit past traffic pattern information in order to enhance predictability. For the purpose of prediction, several historical traffic patterns are developed for each traffic state according to the classified historical traffic data. The future value of traffic flow can be quickly predicted with the real-time traffic data by means of pattern matching techniques to identify the current traffic pattern. Finally, a comparative study on a section of the Third-Loop Freeway, LIULIQIAO, Beijing, indicates that pattern-based forecasting that considers traffic state is more accurate when compared to classical ARIMA models, which only operate under the time series consideration.