Traffic forecasting has recently attracted increasing interest due to the
popularity of online navigation services, ridesharing and smart city projects.
Owing to the non-stationary nature of road traffic, forecasting accuracy is
fundamentally limited by the lack of contextual information. To address this
issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network
(H-STGCN), which is able to "deduce" future travel time by exploiting the data
of upcoming traffic volume. Specifically, we propose an algorithm to acquire
the upcoming traffic volume from an online navigation engine. Taking advantage
of the piecewise-linear flow-density relationship, a novel transformer
structure converts the upcoming volume into its equivalent in travel time. We
combine this signal with the commonly-utilized travel-time signal, and then
apply graph convolution to capture the spatial dependency. Particularly, we
construct a compound adjacency matrix which reflects the innate traffic
proximity. We conduct extensive experiments on real-world datasets. The results
show that H-STGCN remarkably outperforms state-of-the-art methods in various
metrics, especially for the prediction of non-recurring congestion