Big data for traffic estimation and prediction: a survey of data and tools

Big data for traffic estimation and prediction: a survey of data and tools

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data an...

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Main Authors: Jiang, Weiwei, Luo, Jiayun
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2023
Subjects:
Engineering::Computer science and engineering
Big Data
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Online Access:https://hdl.handle.net/10356/164221
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1642212023-01-10T04:00:08Z Big data for traffic estimation and prediction: a survey of data and tools Jiang, Weiwei Luo, Jiayun School of Computer Science and Engineering Engineering::Computer science and engineering Big Data Call Detail Records Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies. Published version 2023-01-10T04:00:08Z 2023-01-10T04:00:08Z 2022 Journal Article Jiang, W. & Luo, J. (2022). Big data for traffic estimation and prediction: a survey of data and tools. Applied System Innovation, 5(1), 23-. https://dx.doi.org/10.3390/asi5010023 2571-5577 https://hdl.handle.net/10356/164221 10.3390/asi5010023 2-s2.0-85124627439 1 5 23 en Applied System Innovation © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Big Data
Call Detail Records
spellingShingle Engineering::Computer science and engineering
Big Data
Call Detail Records
Jiang, Weiwei
Luo, Jiayun
Big data for traffic estimation and prediction: a survey of data and tools
description Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Jiang, Weiwei
Luo, Jiayun
format Article
author Jiang, Weiwei
Luo, Jiayun
author_sort Jiang, Weiwei
title Big data for traffic estimation and prediction: a survey of data and tools
title_short Big data for traffic estimation and prediction: a survey of data and tools
title_full Big data for traffic estimation and prediction: a survey of data and tools
title_fullStr Big data for traffic estimation and prediction: a survey of data and tools
title_full_unstemmed Big data for traffic estimation and prediction: a survey of data and tools
title_sort big data for traffic estimation and prediction: a survey of data and tools
publishDate 2023
url https://hdl.handle.net/10356/164221
_version_ 1756370556912926720

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