Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition

Wei Zhou, Wei Wang, Xuedong Hua and Yi Zhang
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Wei Zhou: School of Transportation, Southeast University, Nanjing 211189, China
Wei Wang: School of Transportation, Southeast University, Nanjing 211189, China
Xuedong Hua: School of Transportation, Southeast University, Nanjing 211189, China
Yi Zhang: School of Transportation, Southeast University, Nanjing 211189, China

Sustainability, 2020, vol. 12, issue 15, 1-23

Abstract: Accurate and timely traffic flow forecasting is a critical task of the intelligent transportation system (ITS). The predicted results offer the necessary information to support the decisions of administrators and travelers. To investigate trend and periodic characteristics of traffic flow and develop a more accurate prediction, a novel method combining periodic-trend decomposition (PTD) is proposed in this paper. This hybrid method is based on the principle of “decomposition first and forecasting last”. The well-designed PTD approach can decompose the original traffic flow into three components, including trend, periodicity, and remainder. The periodicity is a strict period function and predicted by cycling, while the trend and remainder are predicted by modelling. To demonstrate the universal applicability of the hybrid method, four prevalent models are separately combined with PTD to establish hybrid models. Traffic volume data are collected from the Minnesota Department of Transportation (Mn/DOT) and used to conduct experiments. Empirical results show that the mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) of hybrid models are averagely reduced by 17%, 17%, and 29% more than individual models, respectively. In addition, the hybrid method is robust for a multi-step prediction. These findings indicate that the proposed method combining PTD is promising for traffic flow forecasting.

Keywords: intelligent transportation system; traffic flow forecasting; traffic flow decomposition; time series decomposition approach; hybrid prediction method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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