Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164221
Title: Big data for traffic estimation and prediction: a survey of data and tools
Authors: Jiang, Weiwei
Luo, Jiayun
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: 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
Journal: Applied System Innovation 
Abstract: 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.
URI: https://hdl.handle.net/10356/164221
ISSN: 2571-5577
DOI: 10.3390/asi5010023
Schools: School of Computer Science and Engineering 
Rights: © 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/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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