MoST: A Foundation Model for Multi-modality Spatio-temporal Traffic Prediction

Accurate spatio-temporal traffic prediction is essential for optimizing urban traffic management and resource allocation. To reduce the cost and complexity of cross-city deployment, recent studies have explored spatio-temporal foundation models capable of accurate zero-shot prediction. However, these models are limited to single-modal data, which restricts their capacity to capture the complexity of real-world traffic dynamics. The increasing availability of multi-modality data—such as satellite imagery and points of interest (POI)—offers a promising avenue for enhancing cross-city traffic prediction by providing richer background contexts. Despite this potential, developing foundational models for multi-modality spatio-temporal prediction presents two challenges: the availability and quality of multi-modality data vary significantly across cities, with some cities lacking certain modalities or containing noisy information; and spatial patterns are highly localized and specific to individual regions, which hinders generalization. To address these challenges, we propose MoST, a foundation model for multi-modality spatio-temporal traffic prediction. We introduce a Multi-modality Refinement Module that encodes available modality data and adaptively selects task-relevant modalities while suppressing noisy modalities. Furthermore, we design a Spatio-Temporal Prediction Module that incorporates a spatial expert selection mechanism guided by multi-modality cues. This mechanism dynamically identifies region-specific spatial patterns and assigns appropriate spatial experts to model local dependencies. Finally, we conduct extensive experiments on real-world datasets to validate the superior performance and strong generalization capability of MoST.