Multi-Channel Spatio-Temporal Graph Convolutional Networks for Accurate Micromobility Demand Prediction Integrating Public Transport Data

Accurately predicting city-wide, short-term micromobility (MM) demand, particularly for docked bike-sharing systems, is essential for optimizing urban transportation networks and promoting sustainable mobility. The integration of multimodal data, especially from public transportation (PT) systems like subways, plays a critical role in enhancing the precision of these predictions. However, current data-driven models often overlook the direct integration of public transport checkout data.