Traffic prediction serves as a cornerstone for systems and network services such as the Web of Vehicles (WoV), online navigation, and smart city applications. Despite the proliferation of model architectures in recent years, existing approaches often suffer from highly customized structures and weak transferability, making it difficult to cope with increasing task heterogeneity and modeling complexity. To address these challenges, we propose ST-LEGO, a modular assembly framework driven by large language models (LLMs) that supports flexible structural composition and automated code generation. ST-LEGO employs a multi-agent collaborative system comprising a Prompt Agent, Assemble Agent, and Code Agent, which are responsible for understanding task requirements, dynamically assembling structural modules, and automatically generating executable PyTorch code. By introducing a standardized module library and an intermediate structural description language (DSL), the framework enables controllable generation, reusable composition, and cross-task generalization of model architectures. Empirical results on multiple real-world traffic datasets demonstrate that models generated by ST-LEGO achieve superior accuracy, structural diversity, and convergence compared to a wide range of manually designed baselines. These results highlight the unique potential and scalability of LLMs as structural architects for traffic prediction, offering a new paradigm for integrating language models into web-interactive intelligent transportation systems.

Spatio-temporal data proliferates in numerous real-world domains, such as transportation, weather, and energy. Spatio-temporal deep learning models aims to utilize useful patterns in such data to support tasks like prediction, imputation, and anomaly detection. However, previousone-to-onedeep learning models designed for specific tasks typically require separate training for each use case, leading to increased computational and storage costs. To address this issue,one-to-manyspatio-temporal foundation models have emerged, offering a unified framework capable of solving multiple spatio-temporal tasks. These foundation models achieve remarkable success by learning general knowledge with spatio-temporal data or transferring the general capabilities of pre-trained language models. While previous surveys have explored spatio-temporal data and methodologies separately, they have ignored a comprehensive examination of how foundation models are designed, selected, pre-trained, and adapted. As a result, the overall pipeline for spatio-temporal foundation models remains unclear. To bridge this gap, we innovatively provide an up-to-date review of previous spatio-temporal foundation models from the pipeline perspective. The pipeline begins with an introduction to different types of spatio-temporal data, followed by details of data preprocessing and embedding techniques. The pipeline then presents a novel data property taxonomy to divide existing methods according to data sources and dependencies, providing efficient and effective model design and selection for researchers. On this basis, we further illustrate the training objectives of primitive models, as well as the adaptation techniques of transferred models. Overall, our survey provides a clear and structured pipeline to understand the connection between core elements of spatio-temporal foundation models while guiding researchers to get started quickly. Additionally, we introduce emerging opportunities such as multi-objective training in the field of spatio-temporal foundation models, providing valuable insights for researchers and practitioners.

Cellular Trajectory Map-Matching (CTMM) aims to align cellular location sequences to road networks, which is a necessary preprocessing in location-based services on web platforms like Google Maps, including navigation and route optimization. Current approaches mainly rely on ID-based features and region-specific data to learn correlations between cell towers and roads, limiting their adaptability to unexplored areas. To enable high-accuracy CTMM without additional training in target regions, Zero-shot CTMM requires to extract not only region-adaptive features, but also sequential and location uncertainty to alleviate positioning errors in cellular data. In this paper, we propose a pixel-based trajectory calibration assistant for zero-shot CTMM, which takes advantage of transferable geospatial knowledge to calibrate pixelated trajectory, and then guide the path-finding process at the road network level. To enhance knowledge sharing across similar regions, a Gaussian mixture model is incorporated into VAE, enabling the identification of scenario-adaptive experts through soft clustering. To mitigate high positioning errors, a spatial-temporal awareness module is designed to capture sequential features and location uncertainty, thereby facilitating the inference of approximate user positions. Finally, a constrained path-finding algorithm is employed to reconstruct the road ID sequence, ensuring topological validity within the road network. This process is guided by the calibrated trajectory while optimizing for the shortest feasible path, thus minimizing unnecessary detours. Extensive experiments demonstrate that our model outperforms existing methods in zero-shot CTMM by 16.8\%.

Vertical Federated Learning (VFL) has emerged as a popular machine learning paradigm, enabling model training between the data and the task parties with different features about the same user set while preserving data privacy. In a production environment, VFL usually involves one task party and one data party. Fair and economically efficient feature trading is crucial to the commercialization of VFL, where the task party is considered the data consumer who buys the data party's features. However, current VFL feature trading practices often price the data party's data as a whole and assume transactions occur before performing VFL. Neglecting the performance gains resulting from traded features may lead to underpayment and overpayment issues. In this study, we propose a bargaining-based feature trading approach in VFL to facilitate economically efficient transactions. Our model incorporates performance gain-based pricing, taking into account the revenue-based optimization objectives of both parties. We analyze the proposed bargaining model under perfect and imperfect performance information settings, proving the existence of an equilibrium that optimizes the parties' objectives. Moreover, we develop performance gain estimation-based bargaining strategies for imperfect performance information scenarios and discuss potential security concerns and solutions. Experiments on three real-world datasets demonstrate the effectiveness of the proposed bargaining model.

Fine-grained traffic management and prediction are fundamental to key applications such as autonomous driving, lane change guidance, and traffic signal control. However, obtaining lane-level traffic data has become a critical bottleneck for data-driven models due to limitations in the types and number of sensors and issues with the accuracy of tracking algorithms. To address this, we propose the Fine-grained Road Traffic Inference (FRTI) task, which aims to generate more detailed lane-level traffic information using limited road data, providing a more energy-efficient and cost-effective solution for precise traffic management. This task is abstracted as the first scene of the spatio-temporal graph node generation problem. We designed a two-stage framework-RoadDiff-to solve the FRTI task. This framework leverages the Road-Lane Correlation Autoencoder-Decoder and the Lane Diffusion Module to fully utilize the limited spatio-temporal dependencies and distribution relationships of road data to accurately infer fine-grained lane traffic states. Based on existing research, we designed several baseline models with the potential to solve the FRTI task and conducted extensive experiments on six datasets representing different road conditions to validate the effectiveness of the RoadDiff model in addressing the FRTI task. The relevant datasets and code are available at https://github.com/ShuhaoLii/RoadDiff.

Traffic prediction has long been a focal and pivotal area in research, witnessing both significant strides from city-level to road-level predictions in recent years. With the advancement of Vehicle-to-Everything (V2X) technologies, autonomous driving, and large-scale models in the traffic domain, lane-level traffic prediction has emerged as an indispensable direction. However, further progress in this field is hindered by the absence of comprehensive and unified evaluation standards, coupled with limited public availability of data and code. In this paper, we present the first systematic classification framework for lane-level traffic prediction, offering a structured taxonomy and analysis of existing methods. We construct three representative datasets from two real-world road networks, covering both regular and irregular lane configurations, and make them publicly available to support future research. We further establishes a unified spatial topology structure and prediction task formulation, and proposes a simple yet effective baseline model, GraphMLP, based on graph structure and MLP networks. This unified framework enables consistent evaluation across datasets and modeling paradigms. We also reproduce previously unavailable code from existing studies and conduct extensive experiments to assess a range of models in terms of accuracy, efficiency, and applicability, providing the first benchmark that jointly considers predictive performance and training cost for lane-level traffic scenarios. All datasets and code are released athttps://github.com/ShuhaoLii/LaneLevel-Traffic-Benchmark.

Lane-level traffic prediction is crucial for refined smart city applications, yet the scarcity and quality issues of datasets hinder its development. To overcome these challenges, this study introduces a novel M ulti- c hannel g raph-structured V ariational A uto E ncoder model, McgVAE. This model integrates road-level information to provide a global perspective for lane prediction and performs integrated tasks through three interconnected channels: the road-level channel ensures accurate prediction of road traffic states and communicates closely with the data quality channel to share historical and predicted road information; the data quality channel leverages road-level information to identify and correct missing and noisy data; and finally, the lane channel uses the aforementioned information for lane-level traffic prediction. After extensive experimental comparisons with multiple baseline models across three datasets, the McgVAE model demonstrated outstanding predictive performance and the ability to handle data missingness and noise

The popularity of mobile devices has led to the increased attention of Spatial Crowdsourcing (SC), a framework that assigns location-sensitive tasks to mobile workers. Task recommendation is crucial in helping workers discover attractive tasks. Existing studies have focused on modeling workers' preferences from past task-performing patterns, but their performance is sub-optimal due to the strong coupling of sequentiality, spatiality, and temporality. Moreover, achieving the highest preference-based utility of workers in most of the existing task recommendation studies is inferior to the benefits of the SC platform and the satisfaction of workers in a long range, due to the lower task coverage rate and the poor diversity in a worker's recommended list. To address these problems, we propose a Diversity-Coverage Balanced Task Recommendation (DCBTaskRec) framework. Specifically, we first introduce a decoupled worker preference learning model that adopts self-attention networks as the backbone and decouples the modeling of multiple factors in attention scores. Additionally, we provide an optimal diveristy-aware approach to maximize the recommendation diversity while keeping high preference-based utility of workers to satisfy the multiple tastes of workers. From the side of the SC platform, we also provide two approaches (i.e., greedy coverage-aware approach and diversity-coverage balanced approach) to achieve high coverage and provide a trade-off between diversity and coverage, respectively. Extensive experiments offer insight into the effectiveness of the proposed framework.

The constantly increasing scale of graphs leads to higher costs in terms of data storage and computation. Consequently, there is a growing trend of outsourcing and analyzing graphs in clouds. As there is a concern that cloud servers may extract sensitive information from these graphs, the graphs being outsourced must be pre-anonymized, leading to increased space consumption and degraded graph query processing efficiency. Previous work has attempted to address this issue by outsourcing a compacted anonymized graph to the cloud. However, the solution typically focuses on a specific type of query, such as a subgraph query, and cannot adequately accommodate real-life scenarios where multiple applications often work concurrently on the same graph. In this paper, we propose a generic framework called FRESH to handle various graph queries efficiently within a single outsourced graph. To reduce the size of the outsourced graph, we developed a novel graph contraction scheme that transforms a big graph into a compact one while preserving graph privacy. To showcase the adaptability of classical graph query algorithms (e.g., subgraph query, triangle counting, and shortest distance query), we demonstrate their successful execution on the same compact graph created through our contraction scheme. We further extend our framework by incorporating optimizations that significantly improve query processing efficiency. Extensive experimental results demonstrate the superiority of FRESH over traditional techniques.

With the widespread application of intelligent transportation systems and navigation software, traffic prediction should be modeled in finer granularity to facilitate lane-changing guidance and congestion mitigation. However, existing studies divide the road network into continuous segments which assumes different lanes share the same spatio-temporal patterns. This paper proposes a novel lightweight, attention-based, fully convolutional model, named the Spatio-Temporal Attention- Based Convolutional network (ST-ABC), where lane segments are treated as graph nodes and dynamically models the adjacent spatial dependencies using local attention graph convolution. The attention-based dilated convolutions can process longer sequence periods in parallel, and a global attention layer allows individual nodes to be associated with the global context. By setting a target window, it can further reduce unnecessary computations and improve the prediction effect for the targeted area. Further-more, the ST-ABC model facilitates the simultaneous integration of spatio-temporal information and relational distance metrics among lane segments, enriching the granularity of multi-scaled spatial prediction. Empirical evaluations conducted on two real-world datasets substantiate the augmented efficacy of the STABC model in comparison to established models, with a marked prominence in long-term prediction scenarios.

With an exponentially growing number of graphs from disparate repositories, there is a strong need to analyze a graph database containing an extensive collection of small- or medium-sized data graphs (eg chemical compounds). Although subgraph enumeration and subgraph mining have been proposed to bring insights into a graph database by a set of subgraph structures, they often end up with similar or homogenous topologies, which is undesirable in many graph applications. To address this limitation, we propose theTop-k Edge-Diversified Patterns Discovery problemto retrieve a set of subgraphs that cover the maximum number of edges in a database. To efficiently process such query, we present a generic and extensible framework called$\textsc {Ted}^+$which achieves a guaranteed approximation ratio to the optimal result. Three optimization strategies are further developed to improve the performance, and a lightweight version calledTedLiteis designed for even larger graph databases. Experimental studies on real-world datasets demonstrate the superiority of$\textsc {Ted}^+$to traditional techniques.

The pervasiveness of GPS-enabled devices and wireless communication technologies results in a proliferation of traffic data in intelligent transportation systems, where traffic prediction is often essential to enable reliability and safety. Many recent studies target traffic prediction using deep learning techniques. They model spatio-temporal dependencies among traffic states by deep learning and achieve good overall performance. However, existing studies ignore the bias on traffic prediction models, which refers to non-uniformed performance distribution across road segments, especially the significantly poor prediction results on certain road segments. To solve this issue, we propose a framework named spatio-temporal mixture-of-experts (ST-MoE) that aims to eliminate the bias on traffic prediction. In general, we refer to any traffic prediction model as the based model, and adopt the proposed ST-MoE framework as a plug-in to debias. ST-MoE uses stacked convolution-based networks to learn spatio-temporal representations of individual patterns of road segments and then adaptively assigns appropriate expert layers (sub-networks) to different patterns through a spatio-temporal gating network. To this end, the patterns can be distinguished, and biased performance among road segments can be eliminated by experts tailored for specific patterns, which also further improves the overall prediction accuracy of the base model. Extensive experimental results on various base models and real-world datasets prove the effectiveness of ST-MoE.

Traffic prediction has drawn increasing attention due to its essential role in smart city applications. To achieve precise predictions, a large number of approaches have been proposed to model spatial dependencies and temporal dynamics. Despite their superior performance, most existing studies focus datasets that are usually in large geographic scales, e.g., citywide, while ignoring the results on specific regions. However, in many scenarios, for example, route planning on time-dependent road networks, only small regions are of interest. We name the task of answering forecasting requests from any query region of interest (ROI) as ROI-demand traffic prediction (RTP). In this paper, we make a primary observation that existing methods fail to jointly achieve effectiveness and efficiency for RTP. To address this issue, a novel model-agnostic framework based on pre-Training, Querying and fine-Tuning, named TQT, is proposed, which first customizes input data given an ROI, and then makes fast adaptation from pre-trained traffic prediction backbone models by fine-tuning. We evaluate TQT on two real-world traffic datasets, performing both flow and speed prediction tasks. Extensive experiment results demonstrate the effectiveness and efficiency of the proposed method.

Learning fair and transferable representations of users that can be used for a wide spectrum of downstream tasks (specifically, machine learning models) has great potential in fairness-aware Web services. Existing studies focus on debiasing w.r.t. a small scale of (one or a handful of) fixed pre-defined sensitive attributes. However, in real practice, downstream data users can be interested in various protected groups and these are usually not known as prior. This requires the learned representations to be fair w.r.t. all possible sensitive attributes. We name this task universal fair representation learning, in which an exponential number of sensitive attributes need to be dealt with, bringing the challenges of unreasonable computational cost and un-guaranteed fairness constraints. To address these problems, we propose a controllable universal fair representation learning (CUFRL) method. An effective bound is first derived via the lens of mutual information to guarantee parity of the universal set of sensitive attributes while maintaining the accuracy of downstream tasks. We also theoretically establish that the number of sensitive attributes that need to be processed can be reduced from exponential to linear. Experiments on two public real-world datasets demonstrate CUFRL can achieve significantly better accuracy-fairness trade-off compared with baseline approaches.

Visual Graph Query Interfaces (VQIs) empower non-programmers to query graph data by constructing visual queries intuitively. Devising efficient technologies in Graph Query Engines (GQEs) for interactive search and exploration has also been studied for years. However, these two vibrant scientific fields are traditionally independent of each other, causing a vast barrier for users who wish to explore the full-stack operations of graph querying. In this demonstration, we propose a novel VQI system built upon Neo4j called VisualNeo that facilities an efficient subgraph query in large graph databases. VisualNeo inherits several advanced features from recent advanced VQIs, which include the data-driven gui design and canned pattern generation. Additionally, it embodies a database manager module in order that users can connect to generic Neo4j databases. It performs query processing through the Neo4j driver and provides an aesthetic query result exploration.

The proliferation of real-time monitoring applications such as Artificial Intelligence for IT Operations (AIOps) and the Internet of Things (IoT) has led to the generation of a vast amount of time-series data. To extract the underlying value of the data, both the industry and the academia are in dire need of efficient and effective methods for time-series analysis. To this end, in this paper, we propose a Multi-layer perceptron (MLP)-attention based multivariate time-series analysis model MARINA. MARINA is designed to simultaneously learn the temporal and spatial correlations among multivariate time-series. Also, the model is versatile in that it is suitable for major time-series analysis tasks such as forecasting and anomaly detection. Through extensive comparisons with the representative multivariate time-series forecasting and anomaly detection algorithms, MARINA is shown to achieve state-of-the-art (SOTA) performance in both forecasting and anomaly detection tasks.

Accurately recommending the next point of interest (POI) has become a fundamental problem with the rapid growth of location-based social networks. However, sparse, imbalanced check-in data and diverse user check-in patterns pose severe challenges for POI recommendation tasks. Knowledge-aware models are known to be primary in leveraging these problems. However, as most knowledge graphs are constructed statically, sequential information is yet integrated. In this work, we propose a meta-learned sequential-knowledge-aware recommender (Meta-SKR), which utilizes sequential, spatio-temporal, and social knowledge to recommend the next POI for a location-based social network user. The framework mainly contains four modules. First, in the graph construction module, a novel type of knowledge graph—the sequential knowledge graph, which is sensitive to the check-in order of POIs—is built to model users’ check-in patterns. To deal with the problem of data sparsity, a meta-learning module based on latent embedding optimization is then introduced to generate user-conditioned parameters of the subsequent sequential-knowledge-aware embedding module, where representation vectors of entities (nodes) and relations (edges) are learned. In this embedding module, gated recurrent units are adapted to distill intra- and inter-sequential knowledge graph information. We also design a novel knowledge-aware attention mechanism to capture information surrounding a given node. Finally, POI recommendation is provided by inferring potential links of knowledge graphs in the prediction module. Evaluations on three real-world check-in datasets show that Meta-SKR can achieve high recommendation accuracy even with sparse data.

Long sequence time-series forecasting (LSTF) has become increasingly popular for its wide range of applications. Though superior models have been proposed to enhance the prediction effectiveness and efficiency, it is reckless to neglect or underestimate one of the most natural and basic temporal properties of time series: history has inertia. In this paper, we introduce a new baseline for LSTF, named historical inertia (HI). In HI, the most recent historical data points in the input time series are adopted as the prediction results. We experimentally evaluate HI on 4 public real-world datasets and 2 LSTF tasks. The results demonstrate that up to 82% relative improvement over state-of-the-art works can be achieved. We further discuss why HI works and potential ways of benefiting from it.

The ongoing COVID-19 pandemic has dramatically changed people's daily lives. A robust forecasting model for COVID-19 infections is essential for governments and institutions to plan timely and perform accurate interventions. Mainstream solutions for COVID-19 prediction fit reported data only by considering observed cases. However, the neglected facts that positive samples are incomplete and many facts of the novel disease are unknown may be prone to cause severe error accumulation, especially in long-term predictions. To fully understand the spreading patterns of the virus, we propose an encoder-decoder framework: (i) in the encoder we embed historical case data into multiple expose-infection ranges and learn message passing between time slices and across ranges with coarse-grained human mobility data incorporated; (ii) in the decoder, we decode the embedded features based on reported cases as well as deaths to jointly consider the effect of both observed and hidden data. We model the spreading of disease in over 60 counties of California and New York, which are two of the most metropolitan areas in the US. The proposed framework significantly outperforms state-of-the-art baselines on JHU COVID-19 dataset on both weekly prediction and daily prediction tasks. We design detailed ablation studies to verify the effectiveness of each key module and find the model not only works with the assistance of mobility data but also with purely cases and deaths, which implies its broad application scenarios.

Multivariate time series forecasting has been drawing increasing attention due to its prevalent applications. It has been commonly assumed that leveraging latent dependencies between pairs of variables can enhance prediction accuracy. However, most existing methods suffer from static variable relevance modeling and ignorance of correlation between temporal scales, thereby failing to fully retain the dynamic and periodic interdependencies among variables, which are vital for long- and short-term forecasting. In this paper, we propose METRO, a generic framework with multi-scale temporal graphs neural networks, which models the dynamic and cross-scale variable correlations simultaneously. By representing the multivariate time series as a series of temporal graphs, both intra- and inter-step correlations can be well preserved via message-passing and node embedding update. To enable information propagation across temporal scales, we design a novel sampling strategy to align specific steps between higher and lower scales and fuse the cross-scale information efficiently. Moreover, we provide a modular interpretation of existing GNN-based time series forecasting works as specific instances under our framework. Extensive experiments conducted on four benchmark datasets demonstrate the effectiveness and efficiency of our approach. METRO has been successfully deployed onto the time series analytics platform of Huawei Cloud, where a one-month online test demonstrated that up to 20% relative improvement over state-of-the-art models w.r.t. RSE can be achieved.

With the rapid development of mobile networks and the widespread usage of mobile devices, spatial crowdsourcing, which refers to assigning location-based tasks to moving workers, has drawn increasing attention. One of the major issues in spatial crowdsourcing is task assignment, which allocates tasks to appropriate workers. However, existing works generally assume the static offline scenarios, where the spatio-temporal information of all the workers and tasks is determined and known a priori. Ignorance of the dynamic spatio-temporal distributions of workers and tasks can often lead to poor assignment results. In this work we study a novel spatial crowdsourcing problem, namely Predictive Task Assignment (PTA), which aims to maximize the number of assigned tasks by taking into account both current and future workers/tasks that enter the system dynamically with location unknown in advance. We propose a two-phase data-driven framework. The prediction phase hybrids different learning models to predict the locations and routes of future workers and designs a graph embedding approach to estimate the distribution of future tasks. In the assignment component, we propose both greedy algorithm for large-scale applications and optimal algorithm with graph partition based decomposition. Extensive experiments on two real datasets demonstrate the effectiveness of our framework.

Exploring Hidden Points of Interest (H-POIs), which are rarely referred in online search and recommendation systems due to insufficient check-in records, benefits business and individuals. In this work, we investigate how to eliminate the hidden feature of H-POIs by enhancing conventional crowdsourced ranking aggregation framework with heterogeneous (i.e., H-POI and Popular Point of Interest (P-POI)) pairwise tasks. We propose a two-phase solution focusing on both effectiveness and efficiency. In offline phase, we substantially narrow down the search space by retrieving a set of geo-textual valid heterogeneous pairs as the initial candidates and develop two practical data-driven strategies to compute worker qualities. In the online phase, we minimize the cost of assessment by introducing an active learning algorithm to jointly select pairs and workers with worker quality, uncertainty of P-POI rankings and uncertainty of the model taken into account. In addition, a (Minimum Spanning) Tree-constrained Skip search strategy is proposed for the purpose of reducing search time cost. Empirical experiments based on real POI datasets verify that the ranking accuracy of H-POIs can be greatly improved with small number of query iterations.