首页|A Universal Pre-Training and Prompting Framework for General Urban Spatio-Temporal Prediction
A Universal Pre-Training and Prompting Framework for General Urban Spatio-Temporal Prediction
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NETL
NSTL
IEEE
Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic management, resource optimization, and emergency response. Despite remarkable breakthroughs in pretrained natural language models that enable one model to handle diverse tasks, a universal solution for spatio-temporal prediction remains challenging. Existing prediction approaches are typically tailored for specific spatio-temporal scenarios, requiring task-specific model designs and extensive domain-specific training data. In this study, we introduce UniST, a universal model designed for general urban spatio-temporal prediction across a wide range of scenarios. Inspired by large language models, UniST achieves success through: (i) utilizing diverse spatio-temporal data from different scenarios, (ii) effective pre-training to capture complex spatio-temporal dynamics, (iii) knowledge-guided prompts to enhance generalization capabilities. These designs together unlock the potential of building a universal model for various scenarios. Extensive experiments on more than 20 spatio-temporal scenarios, including grid-based data and graph-based data, demonstrate UniST’s efficacy in advancing state-of-the-art performance, especially in few-shot and zero-shot prediction.
Data modelsPredictive modelsUrban areasTrainingAdaptation modelsFoundation modelsTraining dataTransformersThree-dimensional displaysTensors
Yuan Yuan、Jingtao Ding、Jie Feng、Depeng Jin、Yong Li
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Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
NETL
NSTL
IEEE
