KDD '19 2019

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

KDD '19 2019 panzheyi/ST-MetaNet

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.

META-LEARNING SPATIO-TEMPORAL FORECASTING TIME SERIES TRAFFIC PREDICTION