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A Novel Method about the Representation and Discrimination of Traffic State
The representation and discrimination of various traffic states play an essential role in solving traffic accidents and congestion as the foundation of traffic state prediction. However, the existing representation of the traffic state usually only considers the road congestion layer and divides the...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570472/ https://www.ncbi.nlm.nih.gov/pubmed/32899826 http://dx.doi.org/10.3390/s20185039 |
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author | Jiang, Junfeng Chen, Qiushi Xue, Jie Wang, Haobo Chen, Zhijun |
author_facet | Jiang, Junfeng Chen, Qiushi Xue, Jie Wang, Haobo Chen, Zhijun |
author_sort | Jiang, Junfeng |
collection | PubMed |
description | The representation and discrimination of various traffic states play an essential role in solving traffic accidents and congestion as the foundation of traffic state prediction. However, the existing representation of the traffic state usually only considers the road congestion layer and divides the traffic state into congested and unblocked. Representation only at the congestion layer is difficult to reflect the road traffic state comprehensively. Therefore, we select three indicators from the layers of road congestion, road safety, and road stability, respectively, then utilizing K-means to cluster the traffic state. The clustering results can be regarded as a new type for the representation of a traffic state. As a result, the traffic states are divided into four classes, which comprehensively reflects the level of road congestion, safety, and stability. Using the four traffic states obtained from the clustering results as class labels, we applied a multi-layer perceptron (MLP) to classify the different traffic states, and the receiver operating characteristic (ROC) curve is assessed to verify the superiority of the classification results. Finally, a visual display of the real-time traffic state in a city’s central area was given. |
format | Online Article Text |
id | pubmed-7570472 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75704722020-10-28 A Novel Method about the Representation and Discrimination of Traffic State Jiang, Junfeng Chen, Qiushi Xue, Jie Wang, Haobo Chen, Zhijun Sensors (Basel) Article The representation and discrimination of various traffic states play an essential role in solving traffic accidents and congestion as the foundation of traffic state prediction. However, the existing representation of the traffic state usually only considers the road congestion layer and divides the traffic state into congested and unblocked. Representation only at the congestion layer is difficult to reflect the road traffic state comprehensively. Therefore, we select three indicators from the layers of road congestion, road safety, and road stability, respectively, then utilizing K-means to cluster the traffic state. The clustering results can be regarded as a new type for the representation of a traffic state. As a result, the traffic states are divided into four classes, which comprehensively reflects the level of road congestion, safety, and stability. Using the four traffic states obtained from the clustering results as class labels, we applied a multi-layer perceptron (MLP) to classify the different traffic states, and the receiver operating characteristic (ROC) curve is assessed to verify the superiority of the classification results. Finally, a visual display of the real-time traffic state in a city’s central area was given. MDPI 2020-09-04 /pmc/articles/PMC7570472/ /pubmed/32899826 http://dx.doi.org/10.3390/s20185039 Text en © 2020 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jiang, Junfeng Chen, Qiushi Xue, Jie Wang, Haobo Chen, Zhijun A Novel Method about the Representation and Discrimination of Traffic State |
title | A Novel Method about the Representation and Discrimination of Traffic State |
title_full | A Novel Method about the Representation and Discrimination of Traffic State |
title_fullStr | A Novel Method about the Representation and Discrimination of Traffic State |
title_full_unstemmed | A Novel Method about the Representation and Discrimination of Traffic State |
title_short | A Novel Method about the Representation and Discrimination of Traffic State |
title_sort | novel method about the representation and discrimination of traffic state |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570472/ https://www.ncbi.nlm.nih.gov/pubmed/32899826 http://dx.doi.org/10.3390/s20185039 |
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