北京航空航天大学主页平台系统 Rongye Shi--Home-- A physics-informed deep learning paradigm for traffic state and fundamental diagram estimation

Personal Information

MORE+

Associate Professor
Supervisor of Doctorate Candidates

E-Mail:

Date of Employment:2023-01-16

School/Department:Institute of Artificial Intelligence

Education Level:博士研究生

Business Address:New Building B1007

Contact Information:shirongye@buaa.edu.cn

Alma Mater:Carnegie Mellon University

Rongye Shi

Education Level:博士研究生

Alma Mater:Carnegie Mellon University

Paper

Current position: Home / Paper

A physics-informed deep learning paradigm for traffic state and fundamental diagram estimation

DOI number:10.1109/TITS.2021.3106259
Journal:IEEE Transactions on Intelligent Transportation Systems
Co-author:Zhaobin Mo,Kuang Huang,Qiang Du
First Author:Rongye Shi
Indexed by:Journal paper
Correspondence Author:Xuan Di
Document Type:J
Volume:23
Issue:8
Page Number:11688-11698
Translation or Not:no
Date of Publication:2022-08-01
Included Journals:SCI、EI

Next One:TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI