Effective urban traffic management is vital for sustainable city development, relying on intelligent systems with machine learning tasks such as traffic flow prediction and travel time estimation. Traditional approaches usually focus on static road network and trajectory representation learning, and overlook the dynamic nature of traffic states and trajectories, which is crucial for downstream tasks. To address this gap, we propose TRACK, a novel framework to bridge traffic state and trajectory data for dynamic road network and trajectory representation learning. TRACK leverages graph attention networks (GAT) to encode static and spatial road segment features, and introduces a transformer-based model for trajectory representation learning. By incorporating transition probabilities from trajectory data into GAT attention weights, TRACK captures dynamic spatial features of road segments. Meanwhile, TRACK designs a traffic transformer encoder to capture the spatial-temporal dynamics of road segments from traffic state data. To further enhance dynamic representations, TRACK proposes a co-attentional transformer encoder and a trajectory-traffic state matching task. Extensive experiments on real-life urban traffic datasets demonstrate the superiority of TRACK over state-of-the-art baselines. Case studies confirm TRACK’s ability to capture spatial-temporal dynamics effectively.

Spatiotemporal (ST) data analysis is a critical area of research in data engineering. Typical dynamic ST data includes trajectory data (representing individual-level mobility) and traffic state data (representing population-level mobility). Traditional studies often treat trajectory and traffic state data as distinct, independent modalities, each tailored to specific tasks within a single modality. However, real-world applications, such as navigation apps, require joint analysis of trajectory and traffic state data. Treating these data types as two separate domains can lead to suboptimal model performance. Although recent advances in ST data pre-training and ST foundation models aim to develop universal models for ST data analysis, most existing models are “multi-task, solo-data modality” (MTSM), meaning they can handle multiple tasks within either trajectory data or traffic state data, but not both simultaneously. To address this gap, this paper introduces BIGCity, a pioneer multi-task, multi-data modality (MTMD) model for ST data analysis. The model targets two key challenges in designing an MTMD ST model: (1) unifying the representations of different ST data modalities, and (2) unifying heterogeneous ST analysis tasks. To overcome the first challenge, BIGCity introduces a novel ST-unit that represents both trajectories and traffic states in a unified format. Additionally, for the second challenge, BIGCity adopts a tunable large model with ST task-oriented prompt, enabling it to perform a range of heterogeneous tasks without the need for fine-tuning. Extensive experiments on real-world datasets demonstrate that BIGCity achieves state-of-the-art performance across 8 tasks, outperforming 17 baselines. Our code are available at https://github.com/bigscity/BIGCity.

Maps are crucial for various smart city applications as a core component of city geographic information systems (GIS). Developing effective Map Entity Representation Learning methods can extract semantic information for downstream tasks like crime rate prediction and land use classification, with significant application potential. A map comprises three entity types: land parcels, road segments, and points of interest. Most existing methods focus on a single entity type, losing inter-entity relationships and weakening representation effectiveness for real-world applications. Thus, jointly modelling and representing multiple map entity types is essential. However, designing a unified framework is challenging due to map data's unstructured, complex, and heterogeneous nature. We propose a novel method, HygMap, to represent all map entity types. We model the map as a heterogeneous hypergraph, design an encoder for map entities, and introduce a hybrid self-supervised training scheme. This architecture comprehensively captures the heterogeneous relationships among map entities at different levels. Experiments on nine downstream tasks with two real-world datasets show that our framework outperforms all baselines, with good computational efficiency and scalability.

Traffic flow data are of great value in smart city applications. However, limited by data collection costs and privacy sensitivity, it is rather difficult to obtain large-scale traffic flow data. Therefore, various data generation methods have been proposed in the literature. Nevertheless, these methods often require data from a specific city for training and are difficult to directly apply to new cities lacking data. To address this problem, this paper proposes a retrieval-augmented diffusion generation model with representation alignment. We use data from multiple source cities for training, extract consistent representations across multiple cities, and leverage retrieval-augmented generation (RAG) technology to incorporate historical data from source cities under similar conditions into the condition, aiming to improve the accuracy of data generation in the target city. Experiments on four real-world datasets demonstrate that, compared with existing deep learning methods, our method achieves better cross-city transfer performance.

Federated learning (FL) has gained widespread attention for its privacy-preserving and collaborative learning capabilities. Due to significant statistical heterogeneity, traditional FL struggles to generalize a shared model across diverse data domains. Personalized federated learning addresses this issue by dividing the model into a globally shared part and a locally private part, with the local model correcting representation biases introduced by the global model. Nevertheless, locally converged parameters more accurately capture domain-specific knowledge, and current methods overlook the potential benefits of these parameters. To address these limitations, we propose PM-MoE architecture. This architecture integrates a mixture of personalized modules and an energy-based personalized modules denoising, enabling each client to select beneficial personalized parameters from other clients. We applied the PM-MoE architecture to nine recent model-split-based personalized federated learning algorithms, achieving performance improvements with minimal additional training. Extensive experiments on six widely adopted datasets and two heterogeneity settings validate the effectiveness of our approach. The source code is available at https://github.com/dannis97500/PM-MOE.

The increasing demands of safety-critical computer vision applications have attracted extensive research on Out-of-Distribution (OOD) detection in recent years. Nevertheless, a large proportion of real-world tasks are in low-data regime, and the gap between meta-learning paradigm and OOD detection mechanism causes low performance in few-shot settings. In order to bridge the gap, we first propose an simple yet effective Episodic Projection Scheme (EPS). EPS is designed to project feature vectors to task-specific feature space for OOD detection, without sacrificing generalization of few-shot models. We then construct a multi-modal representation space for few-shot OOD detection by employing representations of the labels and their synonyms. At last, we put forward a few-shot OOD detection framework named Episodic Projection Network (EPN), which can integrate many kinds of perturbation based OOD algorithms with ease. To verify effectiveness of the proposed scheme, we implement several OOD algorithms into EPN and conduct experiments on two few-shot classification datasets, i.e., Omniglot and mini-ImageNet. Experimental results demonstrate that accuracy has been increased by 5% by integrating the OOD algorithms into the EPN framework.

An optimal representation should contain the maximum task-relevant information and minimum task-irrelevant information, as revealed from Information Bottleneck Principle. In video action recognition, CNN based approaches have obtained better spatio-temporal representation by modeling temporal context. However, these approaches still suffer low generalization. In this paper, we propose a moderate optimization based approach called Dual-view Temporal Regularization (DTR) based on Information Bottleneck Principle for an effective and generalized video representation without sacrificing any efficiency of the model. On the one hand, we design Dual-view Regularization (DR) to constrain task-irrelevant information, which can effectively compress background and irrelevant motion information. On the other hand, we design Temporal Regularization (TR) to maintain task-relevant information by finding an optimal difference between frames, which benefits extracting sufficient motion information. The experimental results demonstrate: (1) DTR is orthogonal to temporal modeling as well as data augmentation, and it achieves general improvement on both model-based and data-based approaches; (2) DTR is effective among 7 different datasets, especially on motion-centric datasets i.e. SSv1/ SSv2, in which DTR gets 6%/3.8% absolute gains in top-1 accuracy.