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Traffic forecasting has recently attracted increasing interest due to thepopularity of online navigation services, ridesharing and smart city projects.Owing to the non-stationary nature of road traffic, forecasting accuracy isfundamentally limited by the lack of contextual information. To address thisissue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network(H-STGCN), which is able to "deduce" future travel time by exploiting the dataof upcoming traffic volume. Specifically, we propose an algorithm to acquirethe upcoming traffic volume from an online navigation engine. Taking advantageof the piecewise-linear flow-density relationship, a novel transformerstructure converts the upcoming volume into its equivalent in travel time. Wecombine this signal with the commonly-utilized travel-time signal, and thenapply graph convolution to capture the spatial dependency. Particularly, weconstruct a compound adjacency matrix which reflects the innate trafficproximity. We conduct extensive experiments on real-world datasets. The resultsshow that H-STGCN remarkably outperforms state-of-the-art methods in variousmetrics, especially for the prediction of non-recurring congestion.

Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data