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Open AccessArticle

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

by 1,2,3, 1,2,3, 1,2,3,* and 1
1
School of Transportation, Southeast University, Nanjing 211189, China
2
Jiangsu Key Laboratory of Urban ITS, Nanjing 211189, China
3
Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Nanjing 211189, China
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(15), 5891; https://doi.org/10.3390/su12155891
Received: 26 May 2020 / Revised: 6 July 2020 / Accepted: 21 July 2020 / Published: 22 July 2020
(This article belongs to the Special Issue Road Traffic Engineering and Sustainable Transportation)
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. View Full-Text
Keywords: intelligent transportation system; traffic flow forecasting; traffic flow decomposition; time series decomposition approach; hybrid prediction method intelligent transportation system; traffic flow forecasting; traffic flow decomposition; time series decomposition approach; hybrid prediction method
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MDPI and ACS Style

Zhou, W.; Wang, W.; Hua, X.; Zhang, Y. Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition. Sustainability 2020, 12, 5891. https://doi.org/10.3390/su12155891

AMA Style

Zhou W, Wang W, Hua X, Zhang Y. Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition. Sustainability. 2020; 12(15):5891. https://doi.org/10.3390/su12155891

Chicago/Turabian Style

Zhou, Wei; Wang, Wei; Hua, Xuedong; Zhang, Yi. 2020. "Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition" Sustainability 12, no. 15: 5891. https://doi.org/10.3390/su12155891

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