Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to a lack of mining road topology. To address the effect attenuation problem, we suggest taking into account the traffic of surrounding locations(wider than the adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, an attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with a 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in the temporal and spatial domains. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method.
ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset
Bitdefender；University of Bucharest（布加勒斯特大学）；University of Stuttgart（斯图加特大学）；International Max Planck Research School for Intelligent Systems（智能系统国际马克斯·普朗克研究学校）
Capturing Complex Spatial-Temporal Dependencies in Traffic Forecasting: A Self-Attention Approach
Guangdong University of Technology（广东工业大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd.（广州市城市规划勘测设计研究院有限公司）
A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting
The Hong Kong University of Science and Technology（香港科技大学）；Chongqing University（重庆大学）；National Chung Hsing University（国立中兴大学）；National Yang Ming Chiao Tung University（国立阳明交通大学）
PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting
Shenzhen Ubiquitous Data Enabling Key Lab（深圳通用数据赋能重点实验室）；Shenzhen International Graduate School, Tsinghua University（深圳国际研究生学院，清华大学）；School of Computer Science and Engineering（计算机科学与工程学院）；University of Electronic Science and Technology of China（电子科技大学）
Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention
RMIT University（皇家墨尔本理工大学）；Xidian University（西安电子科技大学）；Qatar Mobility Innovations Center（卡塔尔移动创新中心）；Qatar University（卡塔尔大学）；Arizona State University（亚利桑那州立大学）；University of California Davis（加州大学戴维斯分校）；University of New South Wales（新南威尔士大学）；ETH Zurich（苏黎世联邦理工学院）
RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间安全学院）；University of Science and Technology of China（中国科学技术大学）；Department of Computer Science, University of Illinois Chicago（伊利诺伊大学芝加哥分校计算机科学系）
KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting
National University of Science and Technology（国立科技大学）；Technical Universität Kaiserslautern（凯泽斯劳滕工业大学）；Deutsches Forschungszentrum für Künstliche Intelligenz（德国人工智能研究中心）
Learning Local Heterogeneity and Cross-Region Context for Large-Scale Traffic Forecasting
Northwestern Polytechnical University（西北工业大学）；City University of Hong Kong（香港城市大学）；Shenzhen Research Institute, City University of Hong Kong（香港城市大学深圳研究院）；Northwest University（西北大学）
STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting
Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算机学院）；School of Transportation Science and Engineering, Harbin Institute of Technology（哈尔滨工业大学交通科学与工程学院）；Company of China Software Testing Center (MIIT Software and Integrated Circuit Promotion Center)（中国软件测试中心（工信部软件与集成电路推广中心））；Transportation Research Institute (IMOB), Hasselt University（哈塞尔特大学交通研究所）；Faculty of Engineering, University of Kragujevac（克拉耶夫大学工程学院）
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
South China University of Technology（华南理工大学）；JD Finance America Corporation（京东金融美国公司）；Communication and Computer Network Laboratory of Guangdong（广东省通信与计算机网络实验室）；Peng Cheng Laboratory（鹏城实验室）；JD Intelligent Cities Research（京东智能城市研究院）；JD Intelligent Cities Business Unit, JD Digits（京东数字科技智能城市业务部）
GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
University of Chinese Academy of Sciences（中国科学院大学）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）
UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
School of Computing and Data Science, The University of Hong Kong, Hong Kong, China（计算与数据科学学院，香港大学，香港，中国）；School of Information Engineering, Beijing Institute of Graphic Communication, Beijing, China（信息工程学院，北京印刷学院，北京，中国）；Innovation Engineering College, Macau University of Science（创新工程学院，澳门科学技术大学）；Department of Computer Science and Technology, University of Cambridge, Cambridge, UK（计算机科学与技术系，剑桥大学，剑桥，英国）
EMAGN: Efficient Multi-Attention Graph Network via Learned Clustering for Scalable Traffic Forecasting
Shanghai Jiao Tong University（上海交通大学）；University of Michigan, Ann Arbor（密歇根大学安娜堡分校）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Cornell University（康奈尔大学）；Technische Universität Dresden（德累斯顿工业大学）；Carnegie Mellon University（卡内基梅隆大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
