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.

Fire events threaten the safety of residents and the health of ecosystems in affected areas, and post-disaster recovery efforts also require a large investment of resources and time. In recent years, the rising frequency of fire events has motivated local governments to strengthen their monitoring and emergency response efforts. However, current fire event identification methods can only identify the presence of a fire, without the ability to distinguish its specific category. In addition, when a fire occurs, the lack of information about the affected areas makes it challenging for emergency management authorities to take timely and effective rescue measures. To address these issues, we propose a two-stage framework for fire event identification and assessment. Specifically, in the first stage, based on multi-band fused remote sensing images and heterogeneous environmental images, the proposed framework not only identifies various fire events but also accurately identifies the boundaries of the fire events. In the second stage, integrating the results of fire event identification with social media data and domain knowledge, we present a real-time assessment agent for fire events based on the large language model. This agent enables timely and accurate analysis of the impact of fires on the affected areas. We evaluate our method on a real-world authority dataset, and results show that our framework identifies fire events with an F1-score of 61.0$\%$and a mAP of 57.7$\%$, which outperforms state-of-the-art baseline methods. In addition, the assessment results of fire events in real cases indicate that the proposed fire event assessment agent can assist emergency responders in obtaining timely and accurate information.

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.

Urban events, such as hurricanes, floods, and epidemic outbreaks, usually have a significant impact on crowd behaviors that may dramatically change people’s mobility patterns, interaction manners, and etc. Accurately foreseeing the abnormal crowd behaviors triggered by urban events can help authorities to dynamically schedule urban resources and services, such as providing shuttle buses, temporal shelters, and psychological first aid. However, traditional crowd behavior prediction models tend to learn regular trends and patterns of crowd traffic, and usually fail to predict the abnormal traffic fluctuations triggered by urban events. In this work, we propose a context-aware framework to accurately predict event-triggered abnormal crowd traffic by explicitly modeling event contexts and their impact. We start by training a state-of-the-art spatiotemporal multi-graph model (MG) leveraging a multi-graph convolutional network (MGCN) to model city-wide venues and their connections, as well as gated recurrent unit (GRU) to capture the crowd traffic patterns within venues. In order to model the time-occasional impact of sudden influential event context (e.g., weather conditions), we propose a multi-task fusion diagram to co-train the MG model by learning a temporal impact embedding with an attention mechanism and recurrent neural network to obtain the MG-MT model. In order to model the hyper-space impact of irregularly distributed event context (e.g., epicenters and trajectories), we propose a multi-view fusion diagram to fine-tune the above-mentioned model by learning a spatial impact graph with a stimulus-response mechanism to obtain the spatiotemporal crowd traffic model (STORM). Experiments using real-world urban data collected from Xiamen, China show that our approach improves over the state-of-the-art baseline by more than 6% in predicting crowd traffic and by more than 40% in the abnormal crowd traffic part.