Recent works on the application of Physics-Informed Neural Networks to traffic density estimation have shown to be promising for future developments due to their robustness to model errors and noisy data. In this paper, we introduce a methodology for online approximation of the traffic density using measurements from probe vehicles in two settings: one using the Greenshield model and the other considering a high-fidelity traffic simulation. The proposed method continuously estimates the real-time traffic density in space and performs model identification with each new set of measurements. The density estimate is updated in almost real-time using gradient descent and adaptive weights. In the case of full model knowledge, the resulting algorithm has similar performance to the classical open-loop one. However, in the case of model mismatch, the iterative solution behaves as a closed-loop observer and outperforms the baseline method. Similarly, in the high-fidelity setting, the proposed algorithm correctly reproduces the traffic characteristics.
Physics-informed deep operator network for traffic state estimation
The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China（交通工程重点实验室，教育部，同济大学，上海201804，中国）；College of Transportation Engineering, Tongji University, Shanghai 201804, China（交通工程学院，同济大学，上海201804，中国）
Macroscopic transport patterns of UAV traffic in 3D anisotropic wind fields: A constraint-preserving hybrid PINN-FVM approach
Nanophotonics and Biophotonics Key Laboratory of Jilin Province, School of Physics, Changchun University of Science and Technology（吉林省纳米光子学与生物光子学重点实验室，物理学院，长春理工大学）
CP-PINNs: Data-Driven Changepoints Detection in PDEs Using Online Optimized Physics-Informed Neural Networks
Stony Brook University（石溪大学）；Center of Excellence in Wireless and Information Technology（无线与信息技术卓越中心）；Institute for Advanced Computational Science（高级计算科学研究所）
A Multi-View Coupled Tensor Decomposition for Lightweight Online Adaptive Traffic Prediction
School of Mathematics and Statistics, Central China Normal University（华中师范大学数学与统计学学院）；School of Mathematics and Statistics, Beijing Institute of Technology（北京理工大学数学与统计学学院）；State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院数学科学国家重点实验室）；School of Mathematical Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）
CIWI-CKT: Chaos-Informed Wave Interference Feature Fusion and Cross-City Knowledge Transfer for Traffic Flow Forecasting
Griffith University（格里菲斯大学）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；School of Information Engineering, Chang’an University（长安大学信息工程学院）
Informed along the road: roadway capacity driven graph convolution network for network-wide traffic prediction
Tandon School of Engineering, New York University（纽约大学工程学院）；State Key Laboratory of Internet of Things for Smart City, University of Macau（澳门大学物联网智能城市国家重点实验室）
Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario
Department of Building, Civil and Environmental Engineering, Concordia University（康科迪亚大学建筑、土木和环境工程系）；Urban Artificial Intelligence Laboratory, University of Florida（佛罗里达大学城市人工智能实验室）；Department of Civil, Environmental and Construction Engineering, University of Central Florida（中央佛罗里达大学土木、环境和建设工程系）
Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow 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）；Zhongguancun Laboratory（中关村实验室）；School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）
A Spatio-Temporal Online Robust Tensor Recovery Approach for Streaming Traffic Data Imputation
Center for Intelligent Decision-making and Machine Learning, School of Management, Xi’an Jiaotong University（智能决策与机器学习中心，管理学院，西安交通大学）；Department of Marketing, School of Management, Xi’an Jiaotong University（市场营销系，管理学院，西安交通大学）
Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
University of Maryland, College Park, MD 20742, United States（马里兰大学学院公园分校）；Florida Atlantic University, Boca Raton, FL 33431, United States（佛罗里达 Atlantic 大学）
School of Statistics and Data Science, LPMC, KLMDASR and LEBPS, Nankai University（南开大学统计与数据科学学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Quality or Quantity? Error-Informed Selective Online Learning with Gaussian Processes in Multi-Agent Systems: Extended Version
Technical University of Munich (TUM)（慕尼黑技术大学）；School of Computation, Information and Technology (CIT)（计算、信息与技术学院）；College of Intelligent Systems Science and Engineering（智能系统科学与工程学院）；School of Electrical and Mechanical Engineering（电气与机械工程学院）；Research and Innovation Centre（研究与创新中心）；University of Adelaide（阿德莱德大学）；Obuda University（奥布达大学）
Adaptive Graph Pruning with Sudden-Events Evaluation for Traffic Prediction using Online Semi-Decentralized ST-GNNs
University of Zagreb, Faculty of Electrical Engineering and Computing（Zagreb大学电子工程与计算学院）；RISE Research Institutes of Sweden（瑞典RISE研究机构）；KTH Royal Institute of Technology（皇家理工学院）
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow 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）；Intelligent Transportation Thrust, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）智能交通枢纽）；School of Software, Nanjing University of Information Science and Technology（南京信息工程大学软件学院）；Zhongguancun Lab（中关村实验室）；School of Computing and Communications, Lancaster University（兰卡斯特大学计算与通信学院）；JD Intelligent Cities Research（京东智能城市研究院）；JD iCity, JD Technology（京东科技京东智城）
Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
Key Laboratory of Road and Traffic Engineering, Ministry of Education at Tongji University（交通工程教育部重点实验室，同济大学）；Urban Mobility Institute, Tongji University（交通 mobility 院，同济大学）
