Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing-Reference-Cited by-同舟云学术

Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing

Published:2020-12 Issue:14 Volume:14 Page:2073-2082
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET intell. transp. syst

Author:

Lu Wenqi¹²³^ORCID,Yi Ziwei¹²³^ORCID,Liu Wan⁴,Gu Yuanli⁴^ORCID,Rui Yikang¹²³,Ran Bin¹²³

Affiliation:

1. School of TransportationSoutheast UniversityNanjingPeople's Republic of China

2. Joint Research Institute on Internet of Mobility, Southeast University and University of Wisconsin‐Madison, Southeast UniversityNanjingPeople's Republic of China

3. Jiangsu Key Laboratory of Urban ITSSoutheast UniversityNanjingPeople's Republic of China

4. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive TransportBeijing Jiaotong UniversityBeijingPeople's Republic of China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

National Key Research and Development Program of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-its.2020.0410

Reference57 articles.

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