  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. Instead of oversimplifying urban
mobility data as traditional video data, we regard it as a complex multivariate
time series. This perspective involves treating the time-varying values of each
grid in each channel as individual time series, necessitating a thorough
examination of temporal dynamics, cross-variable correlations, and
frequency-domain insights for precise and reliable predictions. To address this
challenge, we present the Super-Multivariate Urban Mobility Transformer
(SUMformer), which utilizes a specially designed attention mechanism to
calculate temporal and cross-variable correlations and reduce computational
costs stemming from a large number of time series. SUMformer also employs
low-frequency filters to extract essential information for long-term
predictions. Furthermore, SUMformer is structured with a temporal patch merge
mechanism, forming a hierarchical framework that enables the capture of
multi-scale correlations. Consequently, it excels in urban mobility pattern
modeling and long-term prediction, outperforming current state-of-the-art
methods across three real-world datasets.
