Context-Aware Traffic Prediction Framework Based on Series Decomposition

Forecasting traffic flow is a typical time series problem, which has attracted increasing attention due to the urgent need in intelligent transportation systems. Although numerous time series forecasting methods have been investigated in past decades, from statistics based models to deep neural networks models, the main disadvantages of aforementioned work could be summarized as follows: 1) incapable to handle the complexity and uncertainty of series; 2) incapable to consider external features such as spatial information and importance of points during the learning process; 3) unstable performance on forecasting task given various data patterns. In this study, a novel strategy was proposed to extract context-awareness information and then integrated with Temporal Convolution Network(TCN) model, namely Context-Aware Temporal Convolution Network(CATCN), which utilized local sub-segments to portrait the potential patterns of a series based on series decomposition. The experiments were conducted using three sets of field-captured traffic datasets. The results were presented and compared to state-of-the-art methodologies. The results showed that the performance of proposed method is significantly improved, especially, on the auto-correlation series corpora.