Social graph convolutional LSTM for pedestrian trajectory prediction-Reference-Cited by-同舟云学术

Social graph convolutional LSTM for pedestrian trajectory prediction

Published:2021-02-08 Issue:3 Volume:15 Page:396-405
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET intell. transp. syst

Author:

Zhou Yutao¹,Wu Huayi¹,Cheng Hongquan¹,Qi Kunlun²,Hu Kai³,Kang Chaogui⁴⁵,Zheng Jie¹

Affiliation:

1. The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

2. School of Geography and Information Engineering China University of Geosciences (Wuhan) Wuhan China

3. School of Internet of Things Jiangnan University Wuxi China

4. School of Remote Sensing and Information Engineering Wuhan University Wuhan China

5. Center for Urban Science + Progress New York University Brooklyn New York USA

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/itr2.12033

Reference38 articles.

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2. Discrete choice models of pedestrian walking behavior

3. Bahdanau D. et al.:Neural machine translation by jointly learning to align and translate.3rd International Conference on Learning Representations. (2015) San Diego CA USA May 7–9 2015 Conference Track Proceedings

4. Bruna J. et al.:Spectral networks and locally connected networks on graphs.2nd International Conference on Learning Representations. (2014) Banff AB Canada April 14–16 2014 Conference Track Proceedings

5. Simulation of pedestrian dynamics using a two-dimensional cellular automaton

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