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 | 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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File | Description | Size | Format | |
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asi-05-00023.pdf | 349.48 kB | Adobe PDF | View/Open |
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