Real-Time Prediction of Seasonal Heteroscedasticity in Vehicular Traffic Flow Series

Abstract

Over the past decade, traffic heteroscedasticity has been investigated with the primary purpose of generating prediction intervals around point forecasts constructed usually by short-term traffic condition level forecasting models. However, despite considerable advancements, complete traffic patterns, in particular the seasonal effect, have not been adequately handled. Recently, an offline seasonal adjustment factor plus GARCH model was proposed in Shi et al. 2014 to model the seasonal heteroscedasticity in traffic flow series. However, this offline model cannot meet the real-time processing requirement proposed by real-world transportation management and control applications. Therefore, an online seasonal adjustment factors plus adaptive Kalman filter (OSAF+AKF) approach is proposed in this paper to predict in real time the seasonal heteroscedasticity in traffic flow series. In this approach, OSAF and AKF are combined within a cascading framework, and four types of online seasonal adjustment factors are developed considering the seasonal patterns in traffic flow series. Empirical results using real-world station-by-station traffic flow series showed that the proposed approach can generate workable prediction intervals in real time, indicating the acceptability of the proposed approach. In addition, compared with the offline model, the proposed online approach showed improved adaptability when traffic is highly volatile. These findings are important for developing real-time intelligent transportation system applications.