UrbanDiT: A Foundation Model for Open-World Urban Spatio-Temporal Learning

• URL: http://arxiv.org/abs/2411.12164v1
• Date: Tue, 19 Nov 2024 02:01:07 GMT
• Title: UrbanDiT: A Foundation Model for Open-World Urban Spatio-Temporal Learning
• Authors: Yuan Yuan, Chonghua Han, Jingtao Ding, Depeng Jin, Yong Li,
• Abstract summary: UrbanDiTT is a foundation model open-world urban-temporal learning. It integrates diverse-temporal data sources and types across different cities and scenarios.
• Score: 25.217842149162735
• License:
• Abstract: The urban environment is characterized by complex spatio-temporal dynamics arising from diverse human activities and interactions. Effectively modeling these dynamics is essential for understanding and optimizing urban systems In this work, we introduce UrbanDiT, a foundation model for open-world urban spatio-temporal learning that successfully scale up diffusion transformers in this field. UrbanDiT pioneers a unified model that integrates diverse spatio-temporal data sources and types while learning universal spatio-temporal patterns across different cities and scenarios. This allows the model to unify both multi-data and multi-task learning, and effectively support a wide range of spatio-temporal applications. Its key innovation lies in the elaborated prompt learning framework, which adaptively generates both data-driven and task-specific prompts, guiding the model to deliver superior performance across various urban applications. UrbanDiT offers three primary advantages: 1) It unifies diverse data types, such as grid-based and graph-based data, into a sequential format, allowing to capture spatio-temporal dynamics across diverse scenarios of different cities; 2) With masking strategies and task-specific prompts, it supports a wide range of tasks, including bi-directional spatio-temporal prediction, temporal interpolation, spatial extrapolation, and spatio-temporal imputation; and 3) It generalizes effectively to open-world scenarios, with its powerful zero-shot capabilities outperforming nearly all baselines with training data. These features allow UrbanDiT to achieves state-of-the-art performance in different domains such as transportation traffic, crowd flows, taxi demand, bike usage, and cellular traffic, across multiple cities and tasks. UrbanDiT sets up a new benchmark for foundation models in the urban spatio-temporal domain.

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