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.

Spatiotemporal prediction (STP) service is one of the key infrastructure applications in smart cities. Currently, most of the existing STP services are constructed following the workflow of building deep learning (DL) applications while neglecting the importance of domain knowledge and region partition. However, the performance and interpretability of STP are highly related to them. As a result, there is an urgent requirement to develop a thorough and tailored workflow for STP services. To address this gap, we propose a novel workflow including two factors above as intermediate procedures. Based on the workflow, we design and implement an STP toolbox called UCTB (Urban Computing Tool Box) assisting practitioners in the rapid construction of STP services, which can manage multiple spatiotemporal do-main knowledge, support various region partition algorithms, and possess state-of-the-art models simultaneously. The relevant code and supporting documents have been open-sourced at https://github.com/uctb/UCIB.

Spatio-temporal traffic prediction (STTP) plays a crucial role in the development of smart cities. Deep learning models have shown superior performance in traffic prediction, but their opacity and complexity of traffic data present challenges for researchers in tuning models. To tune models effectively, we propose a generalized error analysis pipeline and design a corresponding visualization system, STError-Copilot (Spatio-temporal Error Copilot). The pipeline analyzes multi-perspective spatio-temporal features to determine whether prediction errors originate from semantic or modeling levels, and subsequently tunes the model. STErrorCopilot provides a comprehensive data analysis solution, covering the entire workflow from data loading, processing and visualization to final tuning, delivering end-to-end services. We perform error analysis and tuning on two classic models using two real datasets, demonstrating that our method accurately identifies errors and provides appropriate tuning recommendations.