Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications

6 February 2020

Papers citing "Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications"

7 / 7 papers shown

Title
Closed-Loop Neural Operator-Based Observer of Traffic Density Alice Harting Karl H. Johansson Matthieu Barreau 30 0 0 07 Apr 2025
MoGERNN: An Inductive Traffic Predictor for Unobserved Locations in Dynamic Sensing Networks Qishen Zhou Yifan Zhang Michail A. Makridis Anastasios Kouvelas Yibing Wang Simon Hu AI4TS 75 1 0 21 Jan 2025
Physics-informed Machine Learning for Calibrating Macroscopic Traffic Flow Models Yu Tang Li Jin K. Ozbay AI4CE 13 1 0 12 Jul 2023
Inverting the Fundamental Diagram and Forecasting Boundary Conditions: How Machine Learning Can Improve Macroscopic Models for Traffic Flow Maya Briani E. Cristiani Elia Onofri 21 2 0 21 Mar 2023
Traffic State Estimation from Vehicle Trajectories with Anisotropic Gaussian Processes Fan Wu Zhanhong Cheng Huiyu Chen T. Qiu Lijun Sun 18 3 0 04 Mar 2023
Short-term traffic prediction using physics-aware neural networks M. Pereira Annika Lang Balázs Kulcsár 13 19 0 21 Sep 2021
Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs Jun Zhu Ning Chen Eric P. Xing BDL 52 156 0 05 Oct 2012