Traffic Forecasting via Dilated Temporal Convolution With Peak-Sensitive Loss
Ge Guo, Wei Yuan, Jinyuan Liu, Yisheng Lv, Wei Liu
Abstract
Deep learning-based traffic forecasting methods can capture intricate spatiotemporal features in traffic data and environmental factors. However, they have unsatisfactory performance around the minority peaks and are inefficient for modeling wide-range spatial correlations. This article gives a peak-aware deep learning architecture for traffic forecasting by involving a cost-sensitive loss function called peak-sensitive loss. This method can improve the performance since different costs are employed on the prevalent metrics such as mean-square loss and square of mean absolute percentage loss. A spatiotemporal convolutional architecture based on a dilated convolutional network (DCN) and a temporal convolutional network (TCN) is constructed that models the spatial features (both wide and short range) by the DCN and learns the time characteristics by the TCN. The effectiveness of the model is demonstrated with real-world data sets.