Toward Generalized Urban Computing: Pretraining a Spatial-Temporal Model for Diverse Urban Tasks

Urban computing leverages data analysis to improve urban areas' efficiency and sustainability, tackling tasks like traffic management, crime forecasting, and air quality predictions. Current models, while efficient, often struggle with tasks beyond their initial training due to limited flexibility. Typically, new tasks require developing specialized models, which may not perform optimally with limited data. To overcome these challenges, we propose the development of a universal pretrained model that understands a city's various aspects comprehensively. This model serves as a robust foundation, ready to be quickly adjusted for different urban tasks as they arise, even if they occur in different cities. Unlike language models, urban computing models must handle unique spatial-temporal dynamics, making standard pretraining techniques inadequate. Our approach includes a spatial-temporal module with multi-graph convolution and temporal attention mechanisms, capturing the necessary spatial-temporal patterns during pretraining. We also integrate a prompt-tuning module within this framework, which can be adapted for new predictive tasks. The results of extensive experiments on four urban predictive tasks across two cities demonstrate the effectiveness of our model