School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；School of Geosciences and Info-Physics, Central South University（中南大学地球科学与信息物理学院）
Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among which GCRN is the state of art work, to characterize the temporal and spatial correlation of traffic flows. However, it is hard to apply GCRN to the large scale road networks due to high computational complexity. To address this problem, we propose to abstract the road network into a geometric graph and build a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies of traffic flow. Specifically, We use FastGCN unit to efficiently capture the topological relationship between the roads and the surrounding roads in the graph with reducing the computational complexity through importance sampling, combine GRU unit to capture the temporal dependency of traffic flow, and embed the spatiotemporal features into Seq2Seq based on the Encoder-Decoder framework. Experiments on large-scale traffic data sets illustrate that the proposed method can greatly reduce computational complexity and memory consumption while maintaining relatively high accuracy.
Improving short-term bike sharing demand forecast through an irregular convolutional neural network
The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；The University of Hong Kong（香港大学）；Smart Cities Research Institute, The Hong Kong Polytechnic University（香港理工大学智慧城市研究院）；Shenzhen University（深圳大学）
DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems
Southeast University（东南大学）；Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies（江苏省现代城市交通技术协同创新中心）；City University of Hong Kong（香港城市大学）；School of Mathematics, Southeast University（东南大学数学学院）；Systems Research Institute of the Polish Academy of Sciences（波兰科学院系统研究所）；Luoyang Normal University（洛阳师范学院）；Purple Mountain Laboratories（紫金山实验室）；AGH University of Krakow（AGH科技大学）
STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting
Zhejiang University（浙江大学）；South China University of Technology（华南理工大学）；The University of Sydney（悉尼大学）；Chongqing University（重庆大学）；Zhejiang Institute of Industry and Information Technology（浙江工业信息技术研究院）
UrbanWell: Benchmarking Multimodal Large Language Models for Spatio-Temporal Urban Wellbeing Analytics
University of Helsinki（赫尔辛基大学）；Zhongguancun Academy（中关村学院）；University of Oxford（牛津大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience
Berlin University of Applied Sciences and Technology（柏林应用科学与技术大学）；Intelligent Embedded Systems Department, University of Duisburg-Essen（智能嵌入式系统系，杜伊斯堡- Essen大学）；Einstein Center Digital Future（爱因斯坦数字未来研究中心）
School of Computing and Data Science（计算与数据科学学院）；The University of Hong Kong（香港大学）；School of Electrical Engineering and Computer Science（电气工程与计算机科学学院）；The University of Queensland（昆士兰大学）；Department of Computing（计算学院）
Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration
National Science Foundation（国家科学基金会）；U.S. Department of Transportation（美国交通部）；University Transportation Center (UTC)（大学交通中心）；Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE)（交通网络安全高级研究与教育中心）；NVIDIA Academic Grant Program（NVIDIA学术资助计划）
Unveiling the Inflexibility of Adaptive Embedding in 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, Southern University of Science and Technology (SUSTech)（南方科技大学可信自主系统研究院）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Center for Spatial Information Science, University of Tokyo（东京大学空间信息科学中心）
Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University（香港理工大学土木与环境工程系）；Department of Traffic Engineering and Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University（同济大学交通工程系和道路与交通工程重点实验室）
Data re-uploading in Quantum Machine Learning for time series: application to traffic forecasting
School of Production Engineering and Management, Technical University of Crete（克里特技术大学生产工程与管理学院）；Quantum Innovation Pc（量子创新公司）；Alma-Sistemi Srl（阿尔玛系统有限公司）；Univ Gustave Eiffel（古斯塔夫·埃菲尔大学）；School of Electrical and Computer Engineering, Technical University of Crete（克里特技术大学电气与计算机工程学院）；QUBITECH Pc（QUBITECH公司）；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究所）；School of Civil Engineering, National Technical University of Athens（雅典国家技术大学土木工程学院）
