A reproducible grid-based pipeline converts Austin e-scooter trips into spatiotemporal demand images; a correlation-plus-error method plus ablation study on UNET selects temporal inputs that cut next-hour MSE by up to 37% and next-24-hour MSE by up to 35% versus simple baselines.
Equity implications of emerging mobility services and public transit coopetition: A review,
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A Grid-Based Framework for E-Scooter Demand Representation and Temporal Input Design for Deep Learning: Evidence from Austin, Texas
A reproducible grid-based pipeline converts Austin e-scooter trips into spatiotemporal demand images; a correlation-plus-error method plus ablation study on UNET selects temporal inputs that cut next-hour MSE by up to 37% and next-24-hour MSE by up to 35% versus simple baselines.