Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation. Accurate forecasting not only depends on the historical traffic flow information but also needs to consider the influence of a variety of external factors, such as weather conditions and surrounding POI distribution. Recently, spatiotemporal models integrating graph convolutional networks and recurrent neural networks have become traffic forecasting research hotspots and have made significant progress. However, few works integrate external factors. Therefore, based on the assumption that introducing external factors can enhance the spatiotemporal accuracy in predicting traffic and improving interpretability, we propose an attribute-augmented spatiotemporal graph convolutional network (AST-GCN). We model the external factors as dynamic attributes and static attributes and design an attribute-augmented unit to encode and integrate those factors into the spatiotemporal graph convolution model. Experiments on real datasets show the effectiveness of considering external information on traffic forecasting tasks when compared to traditional traffic prediction methods. Moreover, under different attribute-augmented schemes and prediction horizon settings, the forecasting accuracy of the AST-GCN is higher than that of the baselines.
Traffic speed forecasting is one of the core problems in transportation systems. For a more accurate prediction, recent studies started using not only the temporal speed patterns but also the spatial information on the road network through the graph convolutional networks. Even though the road network is highly complex due to its non-Euclidean and directional characteristics, previous approaches mainly focused on modeling the spatial dependencies using the distance only. In this paper, we identify two essential spatial dependencies in traffic forecasting in addition to distance, direction and …
Devising and analyzing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among others. Graph Convolutional Neural Networks (GCNNs) are an established approach to learn from time-invariant network data. The graph convolution operation offers a principled approach to aggregate multiresolution information. However, extending the convolution principled learning and respective analysis to the spatiotemporal domain is challenging because spatiotemporal data have more intrinsic dependencies. Hence, a high…
Simple and Robust Forecasting of Spatiotemporally Correlated Small Earth Data with A Tabular Foundation Model
School of Engineering and Technology, China University of Geosciences (Beijing)（工程与技术学院，中国地质大学（北京））；School of Geological Engineering and Geomatics, Chang'an University（地质工程与测绘学院，长安大学）
P-K-GCN: Physics-augmented Koopman-enhanced Graph Convolutional Network for Deep Spatiotemporal Super-resolution
Department of Industrial & Systems Engineering, The University of Tennessee, Knoxville, TN, USA（工业与系统工程系，田纳西大学， Knoxville，TN，美国）；Charles F. Dolan School of Business, Fairfield University, Fairfield, USA（查尔斯·F·多兰商学院，费尔菲尔德大学， Fairfield，美国）；Department of Electrical Engineering & Computer Science, The University of Tennessee, Knoxville, TN, USA（电气工程与计算机科学系，田纳西大学， Knoxville，TN，美国）
Transformer with Koopman-Enhanced Graph Convolutional Network for Spatiotemporal Dynamics Forecasting
Charles F. Dolan School of Business, Fairfield University（Fairfield大学商学院）；Department of Industrial & Systems Engineering, The University of Tennessee（田纳西大学工业与系统工程系）
The construction of spatiotemporal networks using graph convolution networks (GCNs) has become one of the most popular methods for predicting traffic signals. However, when using a GCN for traffic speed prediction, the conventional approach generally assumes the relationship between the sensors as a homogeneous graph and learns an adjacency matrix using the data accumulated by the sensors. However, the spatial correlation between sensors is not specified as one but defined differently from various viewpoints. To this end, we aim to study the heterogeneous characteristics inherent in traffic si…
Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing
School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算与人工智能学院）；SWJTU-Leeds Joint School, Southwest Jiaotong University（西南交通大学-利兹学院）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）
Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
Shandong University（山东大学）；Hong Kong University of Science and Technology（香港科技大学）；Didi Chuxing（滴滴出行）；Southern University of Science and Technology（南方科技大学）
University of Science and Technology of China（中国科学技术大学）；Shanghai AI Laboratory（上海人工智能实验室）
Spatiotemporal learning, which aims at extracting spatiotemporal correlations from the collected spatiotemporal data, is a research hotspot in recent years. And considering the inherent graph structure of spatiotemporal data, recent works focus on capturing spatial dependencies by utilizing Graph Convolutional Networks (GCNs) to aggregate vertex features with the guidance of adjacency matrices. In this paper, with extensive and deep-going experiments, we comprehensively analyze existing spatiotemporal graph learning models and reveal that extracting adjacency matrices with carefully design str…
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR)（新加坡科技研究局资讯通信研究院（I2R））
Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With the accurate EV station situation prediction, suitable charging behaviors could be scheduled in advance to relieve range anxiety. Many existing deep learning methods are proposed to address this issue, however, due to the complex road network structure and comprehensive external factors, such as point of interests (POIs) and weather effects, many commonly used algorithms could just extract the historical usage information without considerin…
STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
SUSTech-UTokyo Joint Research Center on Super Smart City（南方科技大学-东京大学超级智慧城市联合研究中心）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）；Southern University of Science and Technology (SUSTech)（南方科技大学）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；University of Tokyo（东京大学）
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
Beijing Institute of Technology（北京理工大学）；Tongji University（同济大学）；University of Oxford（牛津大学）；University of Macau（澳门大学）；HeFei University of Technology（合肥工业大学）；Macquarie University（麦考瑞大学）
Existing approaches to the crime prediction problem are unsuccessful in expressing the details since they assign the probability values to large regions. This paper introduces a new architecture with the graph convolutional networks (GCN) and multivariate Gaussian distributions to perform high-resolution forecasting that applies to any spatiotemporal data. We tackle the sparsity problem in high resolution by leveraging the flexible structure of GCNs and providing a subdivision algorithm. We build our model with Graph Convolutional Gated Recurrent Units (Graph-ConvGRU) to learn spatial, tempora…
ASTEROID: A Spatiotemporal Information Transformer for Forecasting Multi-Step Time Series of Molecular Dynamics
Department of Medicinal Chemistry, School of Pharmaceutical Sciences, Fudan University（药学院药物化学系，复旦大学）；School of Mathematical Sciences and School of AI, Shanghai Jiao Tong University（数学科学学院和人工智能学院，上海交通大学）
MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition
School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）；College of Communication Engineering, Jilin University（吉林大学通信工程学院）；School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University（中山大学药学院（深圳））；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation
Carnegie Mellon University（卡内基梅隆大学）；University of Southern California（南加州大学）；Brown University（布朗大学）；Northeastern University（东北大学）；Rochester Institute of Technology（罗切斯特理工学院）
This paper presents a new approach for predicting team performance from the behavioral traces of a set of agents. This spatiotemporal forecasting problem is very relevant to sports analytics challenges such as coaching and opponent modeling. We demonstrate that our proposed model, Spatial Temporal Graph Convolutional Networks (ST-GCN), outperforms other classification techniques at predicting game score from a short segment of player movement and game features. Our proposed architecture uses a graph convolutional network to capture the spatial relationships between team members and Gated Recur…
Mobility-GCN: a human mobility-based graph convolutional network for tracking and analyzing the spatial dynamics of the synthetic opioid crisis in the USA, 2013-2020
Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（中国科学院重庆绿色智能技术研究院）；Southwest University（西南大学）
The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of graph size and the number of model parameters. Although some work has explored training on large-scale graphs (e.g., GraphSAGE, ClusterGCN, etc.), we pioneer efficient training of large-scale GCN models (i.e., ultra-wide, overparameterized models) with the proposal of a novel, distributed training framework. Our proposed training methodology, called GIST, disjointly partitions the parameters of a GCN model into several, smaller sub-GCNs t…
Accurately forecasting transportation demand is crucial for efficient urban traffic guidance, control and management. One solution to enhance the level of prediction accuracy is to leverage graph convolutional networks (GCN), a neural network based modelling approach with the ability to process data contained in graph based structures. As a powerful extension of GCN, a spatial-temporal graph convolutional network (ST-GCN) aims to capture the relationship of data contained in the graphical nodes across both spatial and temporal dimensions, which presents a novel deep learning paradigm for the a…
A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network
The technology of traffic flow forecasting plays an important role in intelligent transportation systems. Based on graph neural networks and attention mechanisms, most previous works utilize the transformer architecture to discover spatiotemporal dependencies and dynamic relationships. However, they have not considered correlation information among spatiotemporal sequences thoroughly. In this paper, based on the maximal information coefficient, we present two elaborate spatiotemporal representations, spatial correlation information (SCorr) and temporal correlation information (TCorr). Using SC…
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangzhou HKUST Fok Ying Tung Research Institute（广州香港科技大学霍英东研究院）；The Hong Kong University of Science and Technology（香港科技大学）
Machine learning based traffic forecasting models leverage sophisticated spatiotemporal auto-correlations to provide accurate predictions of city-wide traffic states. However, existing methods assume a reliable and unbiased forecasting environment, which is not always available in the wild. In this work, we investigate the vulnerability of spatiotemporal traffic forecasting models and propose a practical adversarial spatiotemporal attack framework. Specifically, instead of simultaneously attacking all geo-distributed data sources, an iterative gradient-guided node saliency method is proposed t…
Spatiotemporal Heterogeneity of AI-Driven Traffic Flow Patterns and Land Use Interaction: A GeoAI-Based Analysis of Multimodal Urban Mobility
Department of Applied Mathematics（应用数学系）；Computer Science (DTU Compute), Technical University of Denmark, Kongens Lyngby, Denmark（计算机科学（DTU Compute），丹麦技术大学，Kongens Lyngby）
Short-term future population prediction is a crucial problem in urban computing. Accurate future population prediction can provide rich insights for urban planners or developers. However, predicting the future population is a challenging task due to its complex spatiotemporal dependencies. Many existing works have attempted to capture spatial correlations by partitioning a city into grids and using Convolutional Neural Networks (CNN). However, CNN merely captures spatial correlations by using a rectangle filter; it ignores urban environmental information such as distribution of railroads and l…
An Interpretable Implicit-Based Approach for Modeling Local Spatial Effects: A Case Study of Global Gross Primary Productivity
College of Urban and Environmental Sciences, Peking University（北京大学城市与环境学院）；The Hong Kong Polytechnic University（香港理工大学）；School of Safety Science, Tsinghua University（清华大学安全科学学院）；College of Surveying and Geo-Informatics, Tongji University（同济大学测绘与地理信息学院）；School of Architecture and Urban Planning, Shenzhen University（深圳大学建筑与城市规划学院）
Auto-Configured Explainable Graph Neural Networks for Multi-Site Pollution Prediction
University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB)（奥兰科技大学-穆罕默德·布迪亚夫）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
China University of Geosciences (Beijing)（中国地质大学（北京））；University of Naples Federico II（那不勒斯费德里科二世大学）
Heterogeneous data are commonly adopted as the inputs for some models that predict the future trends of some observations. Existing predictive models typically ignore the inconsistencies and imperfections in heterogeneous data while also failing to consider the (1) spatial correlations among monitoring points or (2) predictions for the entire study area. To address the above problems, this paper proposes a deep learning method for fusing heterogeneous data collected from multiple monitoring points using graph convolutional networks (GCNs) to predict the future trends of some observations and e…
