AI-based traffic flow prediction

In this paper we discuss an AI-based model for end-to-end traffic prediction tasks, which combines graph convolutional networks and gated recurrent units. The spatial feature of complex topologies and dynamic temporal features can be well extracted from spatial-temporal traffic data. Experiments wit...

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Bibliographic Details
Main Author:	Su, Jingyi
Other Authors:	Wang Dan Wei
Format:	Thesis-Master by Coursework
Language:	English
Published:	Nanyang Technological University 2020
Subjects:	Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/143065
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Institution:	Nanyang Technological University
Language:	English

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record_format	dspace
spelling	sg-ntu-dr.10356-1430652023-07-04T16:52:14Z AI-based traffic flow prediction Su, Jingyi Wang Dan Wei School of Electrical and Electronic Engineering EDWWANG@ntu.edu.sg Engineering::Electrical and electronic engineering In this paper we discuss an AI-based model for end-to-end traffic prediction tasks, which combines graph convolutional networks and gated recurrent units. The spatial feature of complex topologies and dynamic temporal features can be well extracted from spatial-temporal traffic data. Experiments with real-time traffic flow data sets show that this model has better performance compared to some baseline models. Master of Science (Computer Control and Automation) 2020-07-28T00:46:35Z 2020-07-28T00:46:35Z 2020 Thesis-Master by Coursework https://hdl.handle.net/10356/143065 en application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Electrical and electronic engineering
spellingShingle	Engineering::Electrical and electronic engineering Su, Jingyi AI-based traffic flow prediction
description	In this paper we discuss an AI-based model for end-to-end traffic prediction tasks, which combines graph convolutional networks and gated recurrent units. The spatial feature of complex topologies and dynamic temporal features can be well extracted from spatial-temporal traffic data. Experiments with real-time traffic flow data sets show that this model has better performance compared to some baseline models.
author2	Wang Dan Wei
author_facet	Wang Dan Wei Su, Jingyi
format	Thesis-Master by Coursework
author	Su, Jingyi
author_sort	Su, Jingyi
title	AI-based traffic flow prediction
title_short	AI-based traffic flow prediction
title_full	AI-based traffic flow prediction
title_fullStr	AI-based traffic flow prediction
title_full_unstemmed	AI-based traffic flow prediction
title_sort	ai-based traffic flow prediction
publisher	Nanyang Technological University
publishDate	2020
url	https://hdl.handle.net/10356/143065
_version_	1772825490545967104

AI-based traffic flow prediction

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