  Spatiotemporal urban data (STUD) displays complex correlational patterns.
Extensive advanced techniques have been designed to capture these patterns for
effective forecasting. However, because STUD is often massive in scale,
practitioners need to strike a balance between effectiveness and efficiency by
choosing computationally efficient models. An alternative paradigm called
MLP-Mixer has the potential for both simplicity and effectiveness. Taking
inspiration from its success in other domains, we propose an adapted version,
named NexuSQN, for STUD forecast at scale. We identify the challenges faced
when directly applying MLP-Mixers as series- and window-wise multivaluedness
and propose the ST-contextualization to distinguish between spatial and
temporal patterns. Experimental results surprisingly demonstrate that
MLP-Mixers with ST-contextualization can rival SOTA performance when tested on
several urban benchmarks. Furthermore, it was deployed in a collaborative urban
congestion project with Baidu, specifically evaluating its ability to forecast
traffic states in megacities like Beijing and Shanghai. Our findings contribute
to the exploration of simple yet effective models for real-world STUD
forecasting.
