In this paper, we consider the temporal pattern in traffic flow time series, and implement a deep learning model for traffic flow prediction. Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction. Inspired by the detrending method, we propose DeepTrend, a deep hierarchical neural network used for traffic flow prediction which considers and extracts the time-variant trend. DeepTrend has two stacked layers: extraction layer and prediction layer. Extraction layer, a fully connected layer, is used to extract the time-variant trend in traffic flow by feeding the original flow series concatenated with corresponding simple average trend series. Prediction layer, an LSTM layer, is used to make flow prediction by feeding the obtained trend from the output of extraction layer and calculated residual series. To make the model more effective, DeepTrend needs first pre-trained layer-by-layer and then fine-tuned in the entire network. Experiments show that DeepTrend can noticeably boost the prediction performance compared with some traditional prediction models and LSTM with detrending based methods.
HieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecasting
State Key Laboratory of Complex & Critical Software Environment (SKLCCSE)（复杂关键软件环境国家重点实验室）；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）；MOE Engineering Research Center of Advanced Computer Application Technology, Beihang University（教育部先进计算机应用技术工程研究中心，北京航空航天大学）
HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome（罗马大学建筑规划、设计与技术系）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动性学院）
Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control
Department of Civil Engineering and Engineering Mechanics, Columbia University（哥伦比亚大学土木工程与工程力学系）；Department of Civil and Environmental Engineering, University of Michigan（密歇根大学土木与环境工程系）；Center for Smart Cities, Data Science Institute, Columbia University（哥伦比亚大学智能城市中心、数据科学研究所）；Department of Electrical Engineering, Columbia University（哥伦比亚大学电气工程系）
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS
Fraunhofer Institute for Transportation and Infrastructure Systems IVI（弗劳恩霍夫交通与基础设施系统研究所IVI）；Technical University of Munich（慕尼黑工业大学）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）
Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection
Zhejiang University of Technology（浙江工业大学）；Binjiang Institute of Artificial Intelligence, ZJUT（浙江工业大学滨江人工智能学院）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；Institute of Cyberspace Security（网络空间安全研究院）
HALO: Hierarchical Reinforcement Learning for Large-Scale Adaptive Traffic Signal Control
University of Exeter Department of Computer Science Exeter UK（埃克塞特大学计算机科学系埃克塞特英国）；University of Warwick Department of Computer Science Coventry UK（沃里克大学计算机科学系科文特里英国）；University of Exeter（埃克塞特大学）；University of Warwick（沃里克大学）
HONEST-CAV: Hierarchical Optimization of Network Signals and Trajectories for Connected and Automated Vehicles with Multi-Agent Reinforcement Learning
College of Engineering, Center for Environmental Research and Technology, University of California at Riverside（加州大学河滨分校工程学院、环境研究与技术中心）；InfoTech Labs, Toyota Motor North America（丰田北美信息科技实验室）
COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep reinforcement learning
STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting
Beijing University of Posts and Telecommunications（北京邮电大学）；School of Software, Shandong University（山东大学软件学院）；Nanyang Technological University（南洋理工大学）；Digital Research Institute of ENN Group（ENN集团数字研究院）
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）
Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tongji University（同济大学）；The Hong Kong University of Science and Technology（香港科技大学）
An Enhanced Analysis of Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function
Al-Kitab University（阿尔基塔布大学）；Annai Velankanni College of Engineering（安奈维兰卡尼工程学院）；Saveetha School of Engineering（萨维塔工程学院）；Sri Eshwar College of Engineering（斯里埃什瓦尔工程学院）；Northern Technical University（北方技术大学）；University of Anbar（安巴尔大学）
ReGAIN: Retrieval-Grounded AI Framework for Network Traffic Analysis
Computer Science（计算机科学）；North Carolina A\&T State University（北卡罗来纳A&T州立大学）；Institute for Cyber Security（网络安全研究所）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
