Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook
Lyles School of Civil and Construction Engineering, Purdue University（普渡大学莱尔土木与建筑工程学院）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）
Jiawei Xue, Ruichen Tan, Jianzhu Ma, Satish V. Ukkusuri
Data mining in transportation networks (DMTNs) refers to using diverse types of spatio-temporal data for various transportation tasks, including pattern analysis, traffic prediction, and traffic controls. Graph neural networks (GNNs) are essential in many DMTN problems due to their capability to represent spatial correlations between entities. Between 2016 and 2024, the notable applications of GNNs in DMTNs have extended to multiple fields such as traffic prediction and operation. However, existing reviews have primarily focused on traffic prediction tasks. To fill this gap, this study provides a timely and insightful summary of GNNs in DMTNs, highlighting new progress in prediction and operation from academic and industry perspectives since 2023. First, we present and analyze various DMTN problems, followed by classical and recent GNN models. Second, we delve into key works in three areas: (1) traffic prediction, (2) traffic operation, and (3) industry involvement, such as Google Maps, Amap, and Baidu Maps. Along these directions, we discuss new research opportunities based on the significance of transportation problems and data availability. Finally, we compile resources such as data, code, and other learning materials to foster interdisciplinary communication. This review, driven by recent trends in GNNs in DMTN studies since 2023, could democratize abundant datasets and efficient GNN methods for various transportation problems including prediction and operation.
Deep Learning for Spatio-Temporal Data Mining: A Survey
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Tsinghua University（清华大学）
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
Griffith University（格里菲斯大学）；University of Oxford（牛津大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Zhejiang Normal University（浙江师范大学）；Ant Group（蚂蚁集团）；Alibaba Group（阿里巴巴集团）；Deloitte Service LLP（德勤服务有限责任公司）；The University of Hong Kong（香港大学）；NEC Laboratories America（NEC美国实验室）；A*STAR；National Yang Ming Chiao Tung University（阳明交通大学）；JD Technology（京东科技）；Squirrel Ai Learning
Optimizing Bus Travel: A Novel Approach to Feature Mining with P-KMEANS and P-LDA Algorithms
Xi’an Jiaotong University（西安交通大学）；School of Computer Science and Technology（计算机科学与技术学院）；Guangdong Planning and Designing Institute of Telecommunications Co., Ltd（广东省电信规划设计院有限公司）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；College of Transportation Engineering, Tongji University（同济大学交通运输工程学院）；SILC Business School, Shanghai University（上海大学悉尼工商学院）；College of Engineering, Zhejiang Normal University（浙江师范大学工学院）
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；JD Technology（京东科技）；JD Intelligent Cities Research（京东智能城市研究院）；Tsinghua University（清华大学）；Southwest Jiaotong University（西南交通大学）
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings
Identifying roadway departure crash patterns on rural two-lane highways under different lighting conditions: association knowledge using data mining approach
University of Louisiana at Lafayette（路易斯安那大学拉法叶分校）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Rajshahi Science & Technology University（拉杰沙希科技大学）；Auburn University（奥本大学）
Enabling CMF Estimation in Data-Constrained Scenarios: A Semantic-Encoding Knowledge Mining Model
Institute of Transportation Studies, University of California, Davis（加州大学戴维斯分校交通研究所）；Department of Civil and Environmental Engineering, Washington State University（华盛顿州立大学土木与环境工程系）
Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Carnegie Mellon University（卡内基梅隆大学）；National University of Singapore（新加坡国立大学）；Griffith University（格里菲斯大学）；East China Normal University（华东师范大学）；University of New South Wales（新南威尔士大学）；Squirrel Ai Learning, USA（美国松鼠人工智能学习公司）；Nanyang Technology University（南洋理工大学）
FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention
Tsinghua University（清华大学）；DiDi AI Labs（滴滴人工智能实验室）
预计到达时间（Estimated Time of Arrival，ETA）是智能交通系统中最重要的服务之一，也是近年来时空数据采掘中一个具有挑战性的课题。如今，基于深度学习的方法，特别是基于递归神经网络（RNN）的方法，适用于从大量数据中为ETA建模ST模式，并成为最先进的方法。然而，RNN的训练和推理速度缓慢，因为其结构对并行计算不友好。为了解决这个问题，我们提出了一种新的，简要的和有效的框架主要基于前馈网络（FFN）的ETA，FFN与多因素自我注意（FMA-ETA）。提出了一种新的多因素自注意机制，以处理不同的类别特征，并有目的地聚集信息。在真实车辆行驶数据集上的大量实验结果表明，FMA-ETA在预测精度方面与最先进的方法具有竞争力，推理速度明显更快。
SparseST: Exploiting Data Sparsity in Spatiotemporal Modeling and Prediction
Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute（工业与系统工程系，罗切斯特理工学院）；Department of Electrical, Computer, and Systems Engineering and the Department of Computer Science, Rensselaer Polytechnic Institute（电气、计算机与系统工程系和计算机科学系，罗切斯特理工学院）；IBM Research（IBM研究院）
Dynamic Frequency Domain Graph Convolutional Network for Traffic Forecasting
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Academy of Cyber（网络研究院）
TEAM: Topological Evolution-aware Framework for Traffic Forecasting--Extended Version
University of Science（科学大学）；Vietnam National University（越南国家大学）；Aalborg University（奥尔堡大学）；University of Electronic Science and Technology of China（电子科技大学）；East China Normal University（华东师范大学）
Multi-Factor Spatio-Temporal Prediction based on Graph Decomposition Learning
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Economics and Management, Beihang University（北京航空航天大学经济管理学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
