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City-scale holographic traffic flow data based on vehicular trajectory resampling

Despite abundant accessible traffic data, researches on traffic flow estimation and optimization still face the dilemma of detailedness and integrity in the measurement. A dataset of city-scale vehicular continuous trajectories featuring the finest resolution and integrity, as known as the holograph...

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Autores principales: Wang, Yimin, Chen, Yixian, Li, Guilong, Lu, Yuhuan, He, Zhaocheng, Yu, Zhi, Sun, Weiwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9877028/
https://www.ncbi.nlm.nih.gov/pubmed/36697418
http://dx.doi.org/10.1038/s41597-022-01850-0
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author Wang, Yimin
Chen, Yixian
Li, Guilong
Lu, Yuhuan
He, Zhaocheng
Yu, Zhi
Sun, Weiwei
author_facet Wang, Yimin
Chen, Yixian
Li, Guilong
Lu, Yuhuan
He, Zhaocheng
Yu, Zhi
Sun, Weiwei
author_sort Wang, Yimin
collection PubMed
description Despite abundant accessible traffic data, researches on traffic flow estimation and optimization still face the dilemma of detailedness and integrity in the measurement. A dataset of city-scale vehicular continuous trajectories featuring the finest resolution and integrity, as known as the holographic traffic data, would be a breakthrough, for it could reproduce every detail of the traffic flow evolution and reveal the personal mobility pattern within the city. Due to the high coverage of Automatic Vehicle Identification (AVI) devices in Xuancheng city, we constructed one-month continuous trajectories of daily 80,000 vehicles in the city with accurate intersection passing time and no travel path estimation bias. With such holographic traffic data, it is possible to reproduce every detail of the traffic flow evolution. We presented a set of traffic flow data based on the holographic trajectories resampling, covering the whole city, including stationary average speed and flow data of 5-minute intervals and dynamic floating car data (FCD).
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spelling pubmed-98770282023-01-27 City-scale holographic traffic flow data based on vehicular trajectory resampling Wang, Yimin Chen, Yixian Li, Guilong Lu, Yuhuan He, Zhaocheng Yu, Zhi Sun, Weiwei Sci Data Data Descriptor Despite abundant accessible traffic data, researches on traffic flow estimation and optimization still face the dilemma of detailedness and integrity in the measurement. A dataset of city-scale vehicular continuous trajectories featuring the finest resolution and integrity, as known as the holographic traffic data, would be a breakthrough, for it could reproduce every detail of the traffic flow evolution and reveal the personal mobility pattern within the city. Due to the high coverage of Automatic Vehicle Identification (AVI) devices in Xuancheng city, we constructed one-month continuous trajectories of daily 80,000 vehicles in the city with accurate intersection passing time and no travel path estimation bias. With such holographic traffic data, it is possible to reproduce every detail of the traffic flow evolution. We presented a set of traffic flow data based on the holographic trajectories resampling, covering the whole city, including stationary average speed and flow data of 5-minute intervals and dynamic floating car data (FCD). Nature Publishing Group UK 2023-01-25 /pmc/articles/PMC9877028/ /pubmed/36697418 http://dx.doi.org/10.1038/s41597-022-01850-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Wang, Yimin
Chen, Yixian
Li, Guilong
Lu, Yuhuan
He, Zhaocheng
Yu, Zhi
Sun, Weiwei
City-scale holographic traffic flow data based on vehicular trajectory resampling
title City-scale holographic traffic flow data based on vehicular trajectory resampling
title_full City-scale holographic traffic flow data based on vehicular trajectory resampling
title_fullStr City-scale holographic traffic flow data based on vehicular trajectory resampling
title_full_unstemmed City-scale holographic traffic flow data based on vehicular trajectory resampling
title_short City-scale holographic traffic flow data based on vehicular trajectory resampling
title_sort city-scale holographic traffic flow data based on vehicular trajectory resampling
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9877028/
https://www.ncbi.nlm.nih.gov/pubmed/36697418
http://dx.doi.org/10.1038/s41597-022-01850-0
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