Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach

Understand

Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services.

• Conventionally, urban mobility data has been structured as spatiotemporal videos, treating longitude and latitude grids as fundamental pixels.
• Consequently, video prediction methods, relying on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have been instrumental in this domain.
• In our research, we introduce a fresh perspective on urban mobility prediction.