Spatiotemporal forecasting is a fundamental task in areas such as traffic flow prediction, environmental sensing, and urban planning. Recent advances have shown that decomposing temporal signals into multiple frequencies and modeling them jointly with spatial structures can significantly enhance forecasting performance. However, existing multifrequency forecasting models still face two critical limitations. First, the importance of different temporal frequencies evolves over time, yet most models assume fixed or static frequency contributions. Second, spatial dependencies are inherently frequency-sensitive. For instance, low-frequency components often align with global spatial patterns, while highfrequency components tend to correspond to localized interactions. However, current approaches typically use a shared spatial information across all frequencies, introducing spatiotemporal inconsistency. To address these challenges, we propose a novel Adaptive Frequency Pathways (AdaFre) for spatiotemporal forecasting, which adaptively captures both dynamic frequency relevance and frequency-aligned spatial structures. AdaFre employs a multi-frequency routing mechanism to dynamically select and aggregate the most informative temporal frequency components, while associating each with its corresponding spatial representation derived from frequency-aware embeddings. Spatiotemporal backbones are then used to model each path independently before final aggregation. Extensive experiments on several real-world datasets demonstrate that AdaFre significantly outperforms state-of-the-art baselines.

Dynamic recommendation systems aim to provide personalized suggestions by modeling temporal user-item interactions across time-series behavioral data. Recent studies have leveraged pre-trained dynamic graph neural networks (GNNs) to learn user-item representations over temporal snapshot graphs. However, fine-tuning GNNs on these graphs often results in generalization issues due to temporal discrepancies between pre-training and fine-tuning stages, limiting the model’s ability to capture evolving user preferences. To address this, we propose TarDGR, a task-aware retrieval-augmented framework designed to enhance generalization capability by incorporating task-aware model and retrieval-augmentation. Specifically, TarDGR introduces a Task-Aware Evaluation Mechanism to identify semantically relevant historical subgraphs, enabling the construction of task-specific datasets without manual labeling. It also presents a Graph Transformer-based Task-Aware Model that integrates semantic and structural encodings to assess subgraph relevance. During inference, TarDGR retrieves and fuses task-aware subgraphs with the query subgraph, enriching its representation and mitigating temporal generalization issues. Experiments on multiple large-scale dynamic graph datasets demonstrate that TarDGR consistently outperforms state-of-the-art methods, with extensive empirical evidence underscoring its superior accuracy and generalization capabilities.

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.

As a typical problem of Spatiotemporal Resource Management, Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy the trade-off between demands and maximum supply. The Black-Litterman (BL) model, which comes from financial portfolio management, offers a new perspective for the TSSA by balancing expected returns against insufficient supply risks. However, the BL model is not only constrained by manually constructed perspective matrices and spatio-temporal market dynamics but also restricted by the absence of supervisory signals and unreliable supplier data. To solve these limitations, we introduce the pioneering Deep Black-Litterman Model for TSSA, which innovatively adapts the BL model from financial domain to supply chain context. Specifically, DBLM leverages Spatio-Temporal Graph Neural Networks (STGNNs) to capture spatio-temporal dependencies for automatically generating future perspective matrices. Moreover, a novel Spearman rank correlation is designed as our DBLM supervise signal to navigate complex risks and interactions of the supplier. Finally, DBLM further uses a masking mechanism to counteract the bias of unreliable data, thus improving precision and reliability. Extensive experiments on two datasets demonstrate significant improvements of DBLM on TSSA.

Xinke Jiang, Yue Fang, Rihong Qiu, Haoyu Zhang, Yongxin Xu, Hao Chen, Wentao Zhang, Ruizhe Zhang, Yuchen Fang, Xinyu Ma, Xu Chu, Junfeng Zhao, Yasha Wang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Locality sensitive hashing (LSH) is a representative approach for nearest neighbor (NN) search in high-dimensional spaces, which is able to answer c-approximate NN (c-ANN) queries in sublinear time with constant probability. Existing advanced LSH methods leverage a plurality of novel techniques such as query-aware dynamic bucketing, virtual rehashing, and efficient indexing to achieve state-of-the-art performance. However, they rely on similar random LSH functions, which provides distance estimations that are irrelevant to the given data distribution. Therefore, the quality of the searched candi-dates is suboptimal. In this study, we reformulate the c-ANN query from the perspective of data distribution. Specifically, we propose a novel distribution-aware c-ANN query, which can guarantee the quality of searched results from the query distribution perspective. We introduce an accurately unbiased distance estimator into LSH methods, which can provide more precise distance estimations by modeling the data distribution. We also conduct rigorous theoretical analysis to prove that our methods can correctly answer the distribution-aware c-ANN query with at least a constant probability. Experiments on seven real datasets with different sizes and dimensionalities indicate that the proposed method can achieve better performance than existing LSH methods in terms of efficiency and effectiveness.

The rapid development of smart devices has fostered the growth of Spatial Crowdsourcing (SC), where workers complete spatial tasks by traveling to specific locations. Task assignment is a key issue in SC due to the inherent complexity of matching workers with these spatial tasks efficiently. Previous studies on task assignment have primarily focused on optimizing worker-task matching within a single, centralized area, often ignoring scenarios that involve multiple independent service centers across an area. To address this gap, we introduce a collaborative multi-center task assignment problem, which focuses on scenarios where an SC platform manages multiple independent service centers within an area, shifting the focus from worker-level cooperation to exploring the solutions specific to multi-center coordination. We target the imbalances between available workers and unassigned tasks among different centers, aiming to maximize the total number of assigned tasks and minimize unfairness in inter-center collaboration. In particular, we propose an Iterative Multi-center Task Assignment and Optimization (IMTAO) framework. IMTAO operates in two phases: (1) center-independent task assignment based on an efficient sequential task assignment algorithm, and (2) inter-center workforce transfer based on a game-theoretic multi-center collaboration algorithm that ensures fair collaboration through bi-directional optimization. Extensive experiments demonstrate the efficiency and effectiveness of IMTAO in enhancing task assignment and improving collaboration fairness compared to baseline methods.

Graph Neural Networks (GNNs) have emerged as a fundamental tool for modeling complex graph structures across diverse applications. However, directly applying pretrained GNNs to varied downstream tasks without fine-tuning-based continual learning remains challenging, as this approach incurs high computational costs and hinders the development of Large Graph Models (LGMs). In this paper, we investigate an efficient and generalizable dataset distillation framework for Graph Continual Learning (GCL) across multiple downstream tasks, implemented through a novel Lightweight Graph Neural Tangent Kernel (LIGHTGNTK). Specifically, LIGHTGNTK employs a low-rank approximation of the Laplacian matrix via Bernoulli sampling and linear association within the GNTK. This design enables efficient capture of both structural and feature relationships while supporting gradient-based dataset distillation. Additionally, LIGHTGNTK incorporates a unified subgraph anchoring strategy, allowing it to handle graph-level, node-level, and edge-level tasks under diverse input structures. Comprehensive experiments on several datasets show that LIGHTGNTK achieves state-of-the-art performance in GCL scenarios, promoting the development of adaptive and scalable LGMs.

Road traffic forecasting is crucial in real-world intelligent transportation scenarios like traffic dispatching and path planning in city management and personal traveling. Spatio-temporal graph neural networks (STGNNs) stand out as the mainstream solution in this task. Nevertheless, the quadratic complexity of remarkable dynamic spatial modeling-based STGNNs has become the bottleneck over large-scale traffic data. From the spatial data management perspective, we present a novel Transformer framework called PatchSTG to efficiently and dynamically model spatial dependencies for large-scale traffic forecasting with interpretability and fidelity. Specifically, we design a novel irregular spatial patching to reduce the number of points involved in the dynamic calculation of Transformer. The irregular spatial patching first utilizes the leaf K-dimensional tree (KDTree) to recursively partition irregularly distributed traffic points into leaf nodes with a small capacity, and then merges leaf nodes belonging to the same subtree into occupancy-equaled and non-overlapped patches through padding and backtracking. Based on the patched data, depth and breadth attention are used interchangeably in the encoder to dynamically learn local and global spatial knowledge from points in a patch and points with the same index of patches. Experimental results on four real world large-scale traffic datasets show that our PatchSTG achieves train speed and memory utilization improvements up to 10x and 4x with the state-of-the-art performance.

Spatio-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool for modeling dynamic graph-structured data across diverse domains. However, they often fail to generalize in Spatio-Temporal Out-of-Distribution (STOOD) scenarios, where both temporal dynamics and spatial structures evolve beyond the training distribution. To address this problem, we propose an innovative Spatio-Temporal Retrieval-Augmented Pattern Learning framework, STRAP, which enhances model generalization by integrating retrieval-augmented learning into the STGNN continue learning pipeline. The core of STRAP is a compact and expressive pattern library that stores representative spatio-temporal patterns enriched with historical, structural, and semantic information, which is obtained and optimized during the training phase. During inference, STRAP retrieves relevant patterns from this library based on similarity to the current input and injects them into the model via a plug-and-play prompting mechanism. This not only strengthens spatio-temporal representations but also mitigates catastrophic forgetting. Moreover, STRAP introduces a knowledge-balancing objective to harmonize new information with retrieved knowledge. Extensive experiments across multiple real-world streaming graph datasets show that STRAP consistently outperforms state-of-the-art STGNN baselines on STOOD tasks, demonstrating its robustness, adaptability, and strong generalization capability without task-specific fine-tuning.

Transportation mode-aware trajectory recovery is the fundamental for individual oriented downstream tasks in intelligent transportation systems. Different from vehicle based trajectory recovery, it suffers from the heterogeneity and sparsity issues arising from the insufficient data labelled for distinct transportation modes (e.g., obtaining limited individual trajectories from modes like walking or cycling due to privacy concerns while obtaining rich vehicle trajectories from the mode like driving). To alleviate this, we develop a novel Cross-trAnsportation-mode Knowledge transfEr method with meta learning, coined as Cake, to first learn generalized parameters from source modes (i.e., the relatively dense modes) and then share the meta knowledge with the targets (i.e., more sparse modes), which significantly improve the recovery performance for the sparse. To achieve this, we first develop an efficient fine-GRAined Personalized trajectory rEcovery model called Grape, to incorporate the cross-granularity features with coarse-centered and fine-centered subgraph learning and learn the intrinsic characteristics of transportation modes with auto-correlation efficiently. Then we design a personalized memory to store distinct parameters for diverse interests of individual groups and read the memory for predictor’s input features according to the previous learnt features. At last, we employ the feature reuse strategy based on meta learning to iteratively make adaptions from the source to the target. Extensive experimental results on real-world dataset demonstrate that our proposed method significantly outperforms the state-of-arts for the sparse modes.

We focus on the transportation-aware trajectory recovery problem, which is distinct from the conventional vehicle-based trajectory recovery, facing three major challenges: heterogeneity, personalization and efficiency. For the heterogeneity, the velocity of the mobile object is intrinsically correlated with the specific transportation mode, containing inherent heterogeneity. For the personalization, the trajectory data is complicated by substantial variations in users, which are different in personalized behaviors. For the efficiency, previous works mostly employ sequence-to-sequence framework which limits their efficiency due to the auto-regressive inference pattern. To address these challenges, we design a novel efficient and effective multi-modal deep model, coined as PTrajRec, for transportation-aware trajectory recovery. Specifically, we initially embed location, behavior, and transportation mode modalities in distinct channels, which not only reflect spatio-temporal information encapsulated in location sequences but also introduce the heterogeneity and personalization characteristics associated with mode and behavior sequences. For further modeling these modalities, we employ the auto-correlation mechanism to learn periodic dependencies on the temporal dimension and the graph attention mechanism to learn road network dependencies on the spatial dimension. At last, we propose a dual-view constraint mechanism to assist the efficient trajectory recovery framework and design three auxiliary tasks to address the inherent heterogeneity and efficiency design. Extensive experimental results on two real-world datasets demonstrate the superiority of our proposed method compared to state-of-the-art baselines with reduced computation cost and excellent performance.

Explaining black-box models is fundamental to gaining trust and deploying these models in real applications. As existing explanation methods have been shown to lack robustness against adversarial perturbations, there has been a growing interest in generating robust explanations. However, existing works resort to empirical defense strategies and these heuristic methods fail against powerful adversaries. In this paper, we certify the robustness of explanations motivated by the success of randomized smoothing. Specifically, we compute a tight radius in which the robustness of the explanation is certified. While a challenge is how to formulate the robustness of the explanation mathematically, we quantize the explanation into discrete spaces to mimic classification in randomized smoothing. To address the high computational cost of randomized smoothing, we introduce randomized gradient smoothing. Also, we explore the robustness of the semantic explanation by certifying the robustness of capsules. In the experiment, we demonstrate the effectiveness of our method on benchmark datasets from the perspectives of post-hoc explanation and semantic explanation respectively. Our work is a promising step towards filling the gap between the theoretical robustness bound and empirical explanations. Our code has been released at https://github.com/NKUShaw/CertifiedExplanation.

In the era of observability, massive amounts of time series data have been collected to monitor the running status of the target system, where anomaly detection serves to identify observations that differ significantly from the remaining ones and is of utmost importance to enable value extraction from such data. While existing reconstruction-based methods have demonstrated favorable detection capabilities in the absence of labeled data, they still encounter issues of training bias on abnormal times and distribution shifts within time series. To address these issues, we propose a simple yet effective Temporal-Frequency Masked AutoEncoder (TFMAE) to detect anomalies in time series through a contrastive criterion. Specifically, TFMAE uses two Transformer-based autoencoders that respectively incorporate a window-based temporal masking strategy and an amplitude-based frequency masking strategy to learn knowledge without abnormal bias and reconstruct anomalies by the extracted normal information. Moreover, the dual autoencoder undergoes training through a contrastive objective function, which minimizes the discrepancy of representations from temporal-frequency masked autoencoders to highlight anomalies, as it helps alleviate the negative impact of distribution shifts. Finally, to prevent over-fitting, TFMAE adopts adversarial training during the training phase. Extensive experiments conducted on seven datasets provide evidence that our model is able to surpass the state-of-the-art in terms of anomaly detection accuracy.

Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGraph), which brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. On the top of our framework is a toy graph vector library that we established, which captures key attributes, such as features and task-specific label information. During inference, the RAGraph adeptly retrieves similar toy graphs based on key similarities in downstream tasks, integrating the retrieved data to enrich the learning context via the message-passing prompting mechanism. Our extensive experimental evaluations demonstrate that RAGraph significantly outperforms state-of-the-art graph learning methods in multiple tasks such as node classification, link prediction, and graph classification across both dynamic and static datasets. Furthermore, extensive testing confirms that RAGraph consistently maintains high performance without the need for task-specific fine-tuning, highlighting its adaptability, robustness, and broad applicability.

Traffic forecasting belongs to intelligent transportation systems and is helpful for public property and life safety. Therefore, to forecast traffic accurately, researchers pay great attention to dealing with complex problems by mining intricate spatial and temporal dependencies of the traffic. However, some challenges still hold back traffic forecasting: 1) Most studies mainly focus on modeling correlations of traffic time series of close distances on the road network and ignore correlations of remote but similar traffic time series; 2) Previous static graph-based methods failed to reflect the dynamic changed spatial relations of multiple time series in the evolving traffic system. To tackle the above issues, we design a new dynamic multi-graph spatio-temporal convolution network (DMGSTCN) in this paper, which utilizes the gated causal convolution with the dynamic multi-graph convolution network (DMGCN) to simultaneously extract spatial and temporal information. Specifically, DMGCN uses not only distance-based graphs but also structure-based graphs to obtain spatial information from nearby and remote but similar traffic time series, respectively. Moreover, to dynamically model spatial correlations, DMGCN first splits neighbors of each traffic time series into different regions according to relative position relationships. Then DMGCN assigns different weights to different regions at different time slices. Empirical evaluations on four traffic forecasting benchmarks reveal that DMGSTCN outperforms existing methods.

Travel time estimation(TTE) is a critical component of intelligent transportation systems. To achieve efficient and accurate trajectory-based travel time estimation, it is essential to design a streamlined model that reduces computation and memory costs. However, this is challenging as traditional deep neural networks are limited in their calculation capabilities and can be cumbersome due to the high number of model parameters. To overcome these challenges, we propose a novel approach to travel time estimation, utilizing a well-designed deep neural network model called Knowledge Distillation for Travel Time Estimation (KDTTE). By implementing knowledge distillation techniques, the model’s computational and memory requirements are reduced, while simultaneously improving its accuracy. The student model leverages the knowledge of the Teacher model to learn features it would not have been able to on its own, thereby enhancing the overall accuracy of the model. Our approach, referred to as KDTTE, was tested on two real-world datasets and showed improved accuracy compared to nine state-of-the-art baselines, demonstrating a 34.0% and 86.8% increase in accuracy on the Chengdu and Porto datasets, respectively.

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to the temporal changes and the dynamic spatial correlations. To capture these intricate dependencies, spatio-temporal networks, such as recurrent neural networks with graph convolution networks, are applied. However, traffic forecasting is still a non-trivial task because of three major challenges: 1) Previous spatio-temporal networks are based on end-to-end training and thus fail to handle the distribution shift in the non-stationary traffic time series. 2) Existing methods always utilize the one-hour input to forecast future traffic and the long-term historical trend knowledge is ignored. 3) The efficient and effective algorithm for modeling multi-scale spatial correlations is still lacking in prior networks. Therefore, in this paper, rather than proposing yet another end-to-end model, we provide a novel disentangle-fusion framework STWave+to mitigate the distribution shift issue. The framework first decouples the complex one-hour traffic data into stable trends and fluctuating events, followed by a dual-channel spatio-temporal network to model trends and events, respectively. Moreover, long-term trends are used as a self-supervised signal in STWave+to teach overall temporal information into one-hour trends through a contrastive loss. Finally, reasonable future traffic can be predicted through the adaptive fusion of one-hour trends and events. Additionally, we incorporate a novel query sampling strategy and multi-scale graph wavelet positional encoding into the full graph attention network to efficiently and effectively model dynamic hierarchical spatial correlations. Extensive experiments on four traffic datasets show the superiority of our approach,i.e., the higher forecasting accuracy with lower computational cost.

With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal data is an important yet demanding aspect of urban computing, which can enhance intelligent management decisions in various fields, including transportation, environment, climate, public safety, healthcare, and others. Traditional statistical and deep learning methods struggle to capture complex correlations in urban spatio-temporal data. To this end, Spatio-Temporal Graph Neural Networks (STGNN) have been proposed, achieving great promise in recent years. STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods. In this manuscript, we provide a comprehensive survey on recent progress on STGNN technologies for predictive learning in urban computing. Firstly, we provide a brief introduction to the construction methods of spatio-temporal graph data and the prevalent deep-learning architectures used in STGNNs. We then sort out the primary application domains and specific predictive learning tasks based on existing literature. Afterward, we scrutinize the design of STGNNs and their combination with some advanced technologies in recent years. Finally, we conclude the limitations of existing research and suggest potential directions for future work.

With the rapid development of the airline industry, maximizing the market share with a constrained budget is an urgent econometric problem for an airline. We investigate the problem by adjusting flight frequencies on different flight routes. Owing to the large search space of solutions and the difficulty of predicting the market, this problem is in general daunting to solve. This paper proposes a novel two-stage optimization method to address the challenges. On the higher level, we use a signal to guide the optimization process toward a constrained satisfying solution. On the lower level, we consider the consecutive itineraries in real scenarios and model the unseen correlations between routes in itineraries for market share prediction. In theory, we prove the convergence of our optimization approach. In the experiment, we empirically verify the superiority of both our prediction model and optimization approach over existing works with large-scale real-world data. Our code has been released at: https://github.com/codingAndBS/AirlineMarket.

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to the temporal changes and the dynamic spatial correlations of the traffic data. To capture these intricate dependencies, spatio-temporal networks, such as recurrent neural networks with graph convolution networks, graph convolution networks with temporal convolution networks, and temporal attention networks with full graph attention networks, are applied. However, previous spatio-temporal networks are based on end-to-end training and thus fail to handle the distribution shift in the non-stationary traffic time series. On the other hand, the efficient and effective algorithm for modeling spatial correlations is still lacking in prior networks.In this paper, rather than proposing yet another end-to-end model, we aim to provide a novel disentangle-fusion framework STWave to mitigate the distribution shift issue. The framework first decouples the complex traffic data into stable trends and fluctuating events, followed by a dual-channel spatio-temporal network to model trends and events, respectively. Finally, reasonable future traffic can be predicted through the fusion of trends and events. Besides, we incorporate a novel query sampling strategy and graph wavelet-based graph positional encoding into the full graph attention network to efficiently and effectively model dynamic spatial correlations. Extensive experiments on six traffic datasets show the superiority of our approach, i.e., the higher forecasting accuracy with lower computational cost.

Next Point-of-Interest (POI) recommendation is a pivotal issue for researchers in the field of location-based social networks. While many recent efforts show the effectiveness of recurrent neural network-based next POI recommendation algorithms, several important challenges have not been well addressed yet: (i) The majority of previous models only consider the dependence of consecutive visits, while ignoring the intricate dependencies of POIs in traces; (ii) The nature of hierarchical and the matching of sub-sequence in POI sequences are hardly model in prior methods; (iii) Most of the existing solutions neglect the interactions between two modals of POI and the density category. To tackle the above challenges, we propose an auto-correlation enhanced multi-modal Transformer network (AutoMTN) for the next POI recommendation. Particularly, AutoMTN uses the Transformer network to explicitly exploits connections of all the POIs along the trace. Besides, to discover the dependencies at the sub-sequence level and attend to cross-modal interactions between POI and category sequences, we replace self-attention in Transformer with the auto-correlation mechanism and design a multi-modal network. Experiments results on two real-world datasets demonstrate the ascendancy of AutoMTN contra state-of-the-art methods in the next POI recommendation.

In recent years, traffic forecasting has gradually attracted attention in data mining because of the increasing availability of large-scale traffic data. However, it faces substantial challenges of complex temporal-spatial correlations in traffic. Recent studies mainly focus on modeling the local spatial correlations by utilizing graph neural networks and neglect the influence of long-distance spatial correlations. Besides, most existing works utilize recurrent neural networks-based encoder–decoder architecture to forecast multistep traffic volume and suffer from accumulative errors in recurrent neural networks. To deal with these issues, we propose the memory attention (MA) enhanced graph convolution long short-term memory network (MAEGCLSTM), a novel deep learning model for traffic forecasting. Specifically, MAEGCLSTM combines the MA and the vanilla graph convolution long short-term memory to capture global and local spatio-temporal dependencies, respectively. Then MAEGCLSTM utilizes a simplified GCLSTM to effectively fuse the global and local information. Moreover, we integrate the MAEGCLSTM into an encoder–decoder architecture to forecast multistep traffic volume. Besides MAEGCLSTM, we add the convolution neural network and encoder–decoder attention into the decoder to ease accumulative errors caused by iterative prediction and gain whole historical information from the encoder. Experiments on four real-world traffic data sets show that our model significantly outperforms by up to 6.07 % $6.07 \% $ improvement in L 1 $L1$ measure over 14 baselines.

Forecasting traffic flow and speed in the urban is important for many applications, ranging from the intelligent navigation of map applications to congestion relief of city management systems. Therefore, mining the complex spatio-temporal correlations in the traffic data to accurately predict traffic is essential for the community. However, previous studies that combined the graph convolution network or self-attention mechanism with deep time series models (e.g., the recurrent neural network) can only capture spatial dependencies in each time slot and temporal dependencies in each sensor, ignoring the spatial and temporal correlations across different time slots and sensors. Besides, the state-of-the-art Transformer architecture used in previous methods is insensitive to local spatio-temporal contexts, which is hard to suit with traffic forecasting. To solve the above two issues, we propose a novel deep learning model for traffic forecasting, named Locality-aware spatio-temporal joint Transformer (Lastjormer), which elaborately designs a spatio-temporal joint attention in the Transformer architecture to capture all dynamic dependencies in the traffic data. Specifically, our model utilizes the dot-product self-attention on sensors across many time slots to extract correlations among them and introduces the linear and convolution self-attention mechanism to reduce the computation needs and incorporate local spatio-temporal information. Experiments on three real-world traffic datasets, England, METR-LA, and PEMS-BAY, demonstrate that our Lastjormer achieves state-of-the-art performances on a variety of challenging traffic forecasting benchmarks.

Travel Time Estimation (TTE) is indispensable in intelligent transportation system (ITS). It is significant to achieve the fine-grained Trajectory-based Travel Time Estimation (TTTE) for multi-city scenarios, namely to accurately estimate travel time of the given trajectory for multiple city scenarios. However, it faces great challenges due to complex factors including dynamic temporal dependencies and fine-grained spatial dependencies. To tackle these challenges, we propose a meta learning based framework, MetaTTE, to continuously provide accurate travel time estimation over time by leveraging well-designed deep neural network model called DED, which consists of Data preprocessing module and Encoder-Decoder network module. By introducing meta learning techniques, the generalization ability of MetaTTE is enhanced using small amount of examples, which opens up new opportunities to increase the potential of achieving consistent performance on TTTE when traffic conditions and road networks change over time in the future. The DED model adopts an encoder-decoder network to capture fine-grained spatial and temporal representations. Extensive experiments on two real-world datasets are conducted to confirm that our MetaTTE outperforms nine state-of-art baselines, and improve 29.35% and 25.93% accuracy than the best baseline on Chengdu and Porto datasets, respectively.