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Title	Stkopt: Automated Spatio-Temporal Knowledge Optimization For Traffic Prediction
ID_Doc	53039
Authors	Hong Y.; Chen L.; Wang L.; Xie X.; Luo G.; Wang C.; Chen L.
Year	2025
Published	WWW 2025 - Proceedings of the ACM Web Conference
DOI	http://dx.doi.org/10.1145/3696410.3714598
Abstract	Ubiquitous sensors and mobile devices have spurred the growth of Web-of-Things (WoT) services in smart cities, making accurate spatio-temporal traffic predictions increasingly crucial. Leveraging advances in deep learning, recent Spatio-Temporal Graph Neural Networks (STGNNs) have achieved remarkable results. However, these methods address scenario-specific spatio-temporal heterogeneity by designing model architectures, often overlooking the importance of selecting optimal spatio-temporal knowledge (i.e., model inputs). In this paper, we propose an automated framework for spatio-temporal knowledge optimization to address this challenge. Our framework seamlessly integrates with downstream models, enhancing their performance across various prediction tasks. Specifically, we design a knowledge search space composed of parameters that represent scenario-specific spatio-temporal correlations within data. Additionally, we employ a bandit-based multi-fidelity algorithm for knowledge optimization to solve the constraint of limited resource. Furthermore, we adopt a meta-learner to extract transferable meta-knowledge about optimal knowledge, facilitating efficient exploration of the search space. Extensive experiments on five widely used real-world datasets demonstrate the effectiveness of our proposed framework. To the best of our knowledge, we are the first to automatically optimize spatio-temporal knowledge for spatio-temporal traffic prediction. © 2025 Copyright held by the owner/author(s).
Author Keywords	Automated Machine Learning; Spatio-Temporal Modeling; Traffic Prediction

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