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Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks
Short-term traffic prediction under corrupted or missing data for large-scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short-term traffic state p...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423571/ https://www.ncbi.nlm.nih.gov/pubmed/34504523 http://dx.doi.org/10.1155/2021/9966382 |
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author | Ma, Tian Gong, Guanghong Ren, Yilong |
author_facet | Ma, Tian Gong, Guanghong Ren, Yilong |
author_sort | Ma, Tian |
collection | PubMed |
description | Short-term traffic prediction under corrupted or missing data for large-scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short-term traffic state prediction method based on critical road selection optimization. First, the utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method is introduced, which is considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the case of considering critical road sections. |
format | Online Article Text |
id | pubmed-8423571 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-84235712021-09-08 Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks Ma, Tian Gong, Guanghong Ren, Yilong Comput Intell Neurosci Research Article Short-term traffic prediction under corrupted or missing data for large-scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short-term traffic state prediction method based on critical road selection optimization. First, the utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method is introduced, which is considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the case of considering critical road sections. Hindawi 2021-08-30 /pmc/articles/PMC8423571/ /pubmed/34504523 http://dx.doi.org/10.1155/2021/9966382 Text en Copyright © 2021 Tian Ma et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Ma, Tian Gong, Guanghong Ren, Yilong Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks |
title | Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks |
title_full | Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks |
title_fullStr | Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks |
title_full_unstemmed | Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks |
title_short | Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks |
title_sort | short-term traffic state prediction based on the critical road selection optimization in transportation networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423571/ https://www.ncbi.nlm.nih.gov/pubmed/34504523 http://dx.doi.org/10.1155/2021/9966382 |
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