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Impact of COVID-19 pandemic on low-carbon shared traffic scheduling under machine learning model
The present work aims to expand the application of machine learning models in predicting and identifying traffic flow data and provide a reference for the scheduling and management of shared traffic against the Coronavirus Disease 2019 (COVID-19) pandemic. First, a time segmentation-based prediction...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer India
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8231089/ http://dx.doi.org/10.1007/s13198-021-01176-x |
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author | Liu, Xin Li, Shunlong |
author_facet | Liu, Xin Li, Shunlong |
author_sort | Liu, Xin |
collection | PubMed |
description | The present work aims to expand the application of machine learning models in predicting and identifying traffic flow data and provide a reference for the scheduling and management of shared traffic against the Coronavirus Disease 2019 (COVID-19) pandemic. First, a time segmentation-based prediction model is proposed considering the classification superiority of Support Vector Machine (SVM) and combining the Optimal Segmentation Algorithm (OSA), denoted as OSA-SVM. Second, an algorithm for generating a shared traffic flow sequence is proposed based on the historical data of shared traffic flow. Finally, a shared traffic flow moment identification model is constructed based on the label propagation algorithm and the Random Forest (RF) model. Comparative analysis suggests that the OSA-SVM regression prediction model can accurately fit the fluctuations caused by the shared traffic flow data; however, its overall effect is not good, with deviation from the actual traffic sequence. Introducing historical data for weighting processing improves the goodness-of-fit of the regression prediction model significantly, maintaining at the level of 0.66–0.71 after one week. The stochastic gradient descent algorithm can provide a better weighted processing effect. The RF model shows the best recognition effect for the shared traffic data stream compared with other models, presenting an excellent performance in dealing with the imbalance and instability problems. The proposed model and algorithm have outstanding prediction and recognition accuracy in shared traffic scheduling, playing an active role in traffic control during COVID-19 prevention and control. |
format | Online Article Text |
id | pubmed-8231089 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer India |
record_format | MEDLINE/PubMed |
spelling | pubmed-82310892021-06-28 Impact of COVID-19 pandemic on low-carbon shared traffic scheduling under machine learning model Liu, Xin Li, Shunlong Int J Syst Assur Eng Manag Original Article The present work aims to expand the application of machine learning models in predicting and identifying traffic flow data and provide a reference for the scheduling and management of shared traffic against the Coronavirus Disease 2019 (COVID-19) pandemic. First, a time segmentation-based prediction model is proposed considering the classification superiority of Support Vector Machine (SVM) and combining the Optimal Segmentation Algorithm (OSA), denoted as OSA-SVM. Second, an algorithm for generating a shared traffic flow sequence is proposed based on the historical data of shared traffic flow. Finally, a shared traffic flow moment identification model is constructed based on the label propagation algorithm and the Random Forest (RF) model. Comparative analysis suggests that the OSA-SVM regression prediction model can accurately fit the fluctuations caused by the shared traffic flow data; however, its overall effect is not good, with deviation from the actual traffic sequence. Introducing historical data for weighting processing improves the goodness-of-fit of the regression prediction model significantly, maintaining at the level of 0.66–0.71 after one week. The stochastic gradient descent algorithm can provide a better weighted processing effect. The RF model shows the best recognition effect for the shared traffic data stream compared with other models, presenting an excellent performance in dealing with the imbalance and instability problems. The proposed model and algorithm have outstanding prediction and recognition accuracy in shared traffic scheduling, playing an active role in traffic control during COVID-19 prevention and control. Springer India 2021-06-25 2022 /pmc/articles/PMC8231089/ http://dx.doi.org/10.1007/s13198-021-01176-x Text en © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Liu, Xin Li, Shunlong Impact of COVID-19 pandemic on low-carbon shared traffic scheduling under machine learning model |
title | Impact of COVID-19 pandemic on low-carbon shared traffic scheduling under machine learning model |
title_full | Impact of COVID-19 pandemic on low-carbon shared traffic scheduling under machine learning model |
title_fullStr | Impact of COVID-19 pandemic on low-carbon shared traffic scheduling under machine learning model |
title_full_unstemmed | Impact of COVID-19 pandemic on low-carbon shared traffic scheduling under machine learning model |
title_short | Impact of COVID-19 pandemic on low-carbon shared traffic scheduling under machine learning model |
title_sort | impact of covid-19 pandemic on low-carbon shared traffic scheduling under machine learning model |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8231089/ http://dx.doi.org/10.1007/s13198-021-01176-x |
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