Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning

KDD '19 2019 Zheyi PanYuxuan LiangWeifeng WangYong YuYu ZhengJunbo Zhang

Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging because of two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among timestamps; 2) diversity of such spatiotemporal correlations, which vary from location to location and depend on the surrounding geographical information, e.g., points of interests and road networks. To tackle these challenges, we proposed a deep-meta-learning based model, entitled ST-MetaNet, to collectively predict traffic in all location at once... (read more)

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