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Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting
[article]
2022
arXiv
pre-print
Traffic flow forecasting is a crucial task in urban computing. The challenge arises as traffic flows often exhibit intrinsic and latent spatio-temporal correlations that cannot be identified by extracting the spatial and temporal patterns of traffic data separately. We argue that such correlations are universal and play a pivotal role in traffic flow. We put forward spacetime interval learning as a paradigm to explicitly capture these correlations through a unified analysis of both spatial and
arXiv:2109.05225v2
fatcat:q7njhzvbxzd4be5z24tawygsxy