A Spatio-Temporal Multimodal Deep Learning Framework for Taxi Demand Prediction From Multi-Source and Mixed-Type Data
A multimodal deep learning framework that integrates historical taxi Global Positioning System trajectories, metro passenger flows, and categorical spatial identifiers that is the most accurate at every tested history length at the hourly resolution and competitive with the strongest baselines at the daily resolution i...