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.
Highlights
Over the past decades, with the sharp increase of car ownership, congestion has become a most troubling problem in urban areas
Three widely used measures are utilized to evaluate the performance of models, including mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE)
It can be seen that the original traffic flow is decomposed into trend, periodic, and remainder components
Summary
With the sharp increase of car ownership, congestion has become a most troubling problem in urban areas. Traffic flow forecasting tasks can be addressed as time series problems. In recent studies [3,10,19,20,21,22,23,24,25], a novel method combining time series decomposition approaches has attracted extensive interest. In order to give an insight into the characteristics of traffic flow, we proposed a novel method combining periodic-trend decomposition (PTD). PTD is developed to disaggregate the original data into three additive components, including trend, periodicity, and remainder. These three components show different characteristics of original traffic flow: the trend represents the day-to-day fluctuation; the periodicity represents variety within a day; the remainder represents noise.
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