STNN: A Spatio-Temporal Neural Network for Traffic Predictions

Abstract

Traffic is very important to route planning and people’s daily lives. Traffic prediction is still very challenging as it is affected by many complex factors including dynamic spatio-temporal dependencies and external factors (e.g., road types and nearby points of interest) in the road network. Dynamic spatio-temporal dependencies simultaneously contain spatial and temporal dependencies. Existing models for predicting traffic of links only consider the spatial dependencies from the perspective of links or the whole road network by ignoring the spatial dependencies among regions. To this end, this paper proposes a new Spatio-Temporal Neural Network (STNN) with the encoder-decoder architecture to improve the accuracy of traffic predictions by additionally taking into account the region-based spatial dependencies and external factors. Specifically, STNN learns dynamic spatio-temporal dependencies from historical traffic time series via an encoder in the perspective of the road network, with two spatial models, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">region-based spatial model</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">link-based spatial attention model</i> in the perspectives of regions and links, respectively. Further, STNN decodes the output from the encoder via a decoder with a temporal attention model for recording long-term dependencies and fuses external factors in the road network, to improve network-wide traffic predictions. We conduct extensive experiments to evaluate the performance of STNN on three real-world traffic datasets, which shows that STNN is significantly better than the state-of-the-art models.