Detecting and interpreting non‐recurrent congestion from traffic and social media data-Reference-Cited by-同舟云学术

Detecting and interpreting non‐recurrent congestion from traffic and social media data

Published:2021-08-21 Issue:12 Volume:15 Page:1461-1477
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET intell. transp. syst

Author:

Luan Sen¹,Ma, Xiaolei¹²,Li Meng³,Su Yuelong⁴,Dong Zhenning⁴

Affiliation:

1. School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control Beihang University Beijing China

2. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

3. Department of Civil Engineering Tsinghua University Beijing China

4. AutoNavi Software Co., Ltd. Beijing 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/itr2.12104

Reference52 articles.

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4. Kamran S. Haas O.:A multilevel traffic incidents detection approach: Identifying traffic patterns and vehicle behaviours using real‐time GPS data.2007 IEEE Intelligent Vehicles Symposium.IEEE pp.912–917(2007b)

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