Predicting citywide crowd flows using deep spatio-temporal residual networks


Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dependencies (nearby and distant), temporal dependencies (closeness, period, trend), and external conditions (e.g. weather and events). We propose a deep-learning-based approach… (More)
DOI: 10.1016/j.artint.2018.03.002


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