☆ 4.7 Article

A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Economics

Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation

Yun Yuan et al.

Summary: This study introduces a physics regularized machine learning (PRML) framework to enhance traffic flow modeling accuracy, especially in cases with small or noisy training datasets. By leveraging a stochastic physics regularized Gaussian process (PRGP) model and a physics regularizer based on macroscopic traffic flow models, the new approach shows superior estimation precision and robustness to noisy training datasets, surpassing previous methods.

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2021)

添加到收藏夹

Article Transportation Science & Technology

A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning

Xuan Di et al.

Summary: This paper comprehensively reviews literature in both transportation engineering and AI for mixed traffic modeling, focusing on the state-of-the-art applications of AI methods for controlling autonomous vehicles in mixed traffic environments. It also raises open questions regarding scalable driving policies, modeling human driver behaviors, and characterizing interactions between human drivers and autonomous vehicles.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2021)

添加到收藏夹

Article Transportation Science & Technology

A physics-informed deep learning paradigm for car-following models

Zhaobin Mo et al.

Summary: This paper develops neural network based car-following models informed by physics-based models, leveraging the advantages of both types of models. The results demonstrate the superior performance of physics-informed neural networks over those without physics-based guidance.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2021)

添加到收藏夹

Proceedings Paper Automation & Control Systems

Physics-informed Learning for Identification and State Reconstruction of Traffic Density

Matthieu Barreau et al.

Summary: The study focuses on traffic density reconstruction using measurements from probe vehicles with low penetration rate, applying machine learning to address noisy measurements and partially unknown first-order model. The proposed approach merges identification and reconstruction processes, with pre-training to aid hyperparameter tuning and avoid getting stuck at saddle points. Numerical simulations and SUMO traffic simulator examples demonstrate close reconstructions to real density in all cases.

2021 60TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC) (2021)

添加到收藏夹

Article Engineering, Civil

Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics

Zibin Zheng et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Hidden physics models: Machine learning of nonlinear partial differential equations

Maziar Raissi et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2018)

添加到收藏夹

Article Transportation Science & Technology

Deep learning for short-term traffic flow prediction

Nicholas G. Polson et al.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2017)

添加到收藏夹

Review Automation & Control Systems

Traffic state estimation on highway: A comprehensive survey

Toru Seo et al.

ANNUAL REVIEWS IN CONTROL (2017)

添加到收藏夹

Article Engineering, Multidisciplinary

Robust Missing Traffic Flow Imputation Considering Nonnegativity and Road Capacity

Huachun Tan et al.

MATHEMATICAL PROBLEMS IN ENGINEERING (2014)

添加到收藏夹

Article Mathematics, Interdisciplinary Applications

COMPARATIVE MODEL ACCURACY OF A DATA-FITTED GENERALIZED AW-RASCLE-ZHANG MODEL

Shimao Fan et al.

NETWORKS AND HETEROGENEOUS MEDIA (2014)

添加到收藏夹

Article Economics

Phase transition model of non-stationary traffic flow: Definition, properties and solution method

Sebastien Blandin et al.

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2013)

添加到收藏夹

Article Transportation Science & Technology

Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

Li Li et al.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2013)

添加到收藏夹

Article Engineering, Civil

Data-Fitted First-Order Traffic Models and Their Second-Order Generalizations Comparison by Trajectory and Sensor Data

Shimao Fan et al.

TRANSPORTATION RESEARCH RECORD (2013)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Reconstructing the Traffic State by Fusion of Heterogeneous Data

Martin Treiber et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2011)

添加到收藏夹

Article Engineering, Civil

Hybrid Extended Kalman Filtering Approach for Traffic Density Estimation Along Signalized Arterials Use of Global Positioning System Data

Xuan Di et al.

TRANSPORTATION RESEARCH RECORD (2010)

添加到收藏夹

Article Automation & Control Systems

An adaptive freeway traffic state estimator

Yibing Wang et al.

AUTOMATICA (2009)

添加到收藏夹

Article Economics

Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing

Yibing Wang et al.

TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE (2008)

添加到收藏夹

Article Transportation Science & Technology

Estimation of missing traffic counts using factor, genetic, neural, and regression techniques

M Zhong et al.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2004)

添加到收藏夹

Article Economics

A non-equilibrium traffic model devoid of gas-like behavior

HM Zhang

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2002)

添加到收藏夹

Article Mathematics, Applied

Resurrection of second order models of traffic flow

A Aw et al.

SIAM JOURNAL ON APPLIED MATHEMATICS (2000)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.