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Novel trajectory clustering method based on distance dependent Chinese restaurant process
Trajectory clustering and path modelling are two core tasks in intelligent transport systems with a wide range of applications, from modeling drivers’ behavior to traffic monitoring of road intersections. Traditional trajectory analysis considers them as separate tasks, where the system first cluste...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924552/ https://www.ncbi.nlm.nih.gov/pubmed/33816859 http://dx.doi.org/10.7717/peerj-cs.206 |
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author | Arfa, Reza Yusof, Rubiyah Shabanzadeh, Parvaneh |
author_facet | Arfa, Reza Yusof, Rubiyah Shabanzadeh, Parvaneh |
author_sort | Arfa, Reza |
collection | PubMed |
description | Trajectory clustering and path modelling are two core tasks in intelligent transport systems with a wide range of applications, from modeling drivers’ behavior to traffic monitoring of road intersections. Traditional trajectory analysis considers them as separate tasks, where the system first clusters the trajectories into a known number of clusters and then the path taken in each cluster is modelled. However, such a hierarchy does not allow the knowledge of the path model to be used to improve the performance of trajectory clustering. Based on the distance dependent Chinese restaurant process (DDCRP), a trajectory analysis system that simultaneously performs trajectory clustering and path modelling was proposed. Unlike most traditional approaches where the number of clusters should be known, the proposed method decides the number of clusters automatically. The proposed algorithm was tested on two publicly available trajectory datasets, and the experimental results recorded better performance and considerable improvement in both datasets for the task of trajectory clustering compared to traditional approaches. The study proved that the proposed method is an appropriate candidate to be used for trajectory clustering and path modelling. |
format | Online Article Text |
id | pubmed-7924552 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79245522021-04-02 Novel trajectory clustering method based on distance dependent Chinese restaurant process Arfa, Reza Yusof, Rubiyah Shabanzadeh, Parvaneh PeerJ Comput Sci Artificial Intelligence Trajectory clustering and path modelling are two core tasks in intelligent transport systems with a wide range of applications, from modeling drivers’ behavior to traffic monitoring of road intersections. Traditional trajectory analysis considers them as separate tasks, where the system first clusters the trajectories into a known number of clusters and then the path taken in each cluster is modelled. However, such a hierarchy does not allow the knowledge of the path model to be used to improve the performance of trajectory clustering. Based on the distance dependent Chinese restaurant process (DDCRP), a trajectory analysis system that simultaneously performs trajectory clustering and path modelling was proposed. Unlike most traditional approaches where the number of clusters should be known, the proposed method decides the number of clusters automatically. The proposed algorithm was tested on two publicly available trajectory datasets, and the experimental results recorded better performance and considerable improvement in both datasets for the task of trajectory clustering compared to traditional approaches. The study proved that the proposed method is an appropriate candidate to be used for trajectory clustering and path modelling. PeerJ Inc. 2019-08-12 /pmc/articles/PMC7924552/ /pubmed/33816859 http://dx.doi.org/10.7717/peerj-cs.206 Text en ©2019 Arfa et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Arfa, Reza Yusof, Rubiyah Shabanzadeh, Parvaneh Novel trajectory clustering method based on distance dependent Chinese restaurant process |
title | Novel trajectory clustering method based on distance dependent Chinese restaurant process |
title_full | Novel trajectory clustering method based on distance dependent Chinese restaurant process |
title_fullStr | Novel trajectory clustering method based on distance dependent Chinese restaurant process |
title_full_unstemmed | Novel trajectory clustering method based on distance dependent Chinese restaurant process |
title_short | Novel trajectory clustering method based on distance dependent Chinese restaurant process |
title_sort | novel trajectory clustering method based on distance dependent chinese restaurant process |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924552/ https://www.ncbi.nlm.nih.gov/pubmed/33816859 http://dx.doi.org/10.7717/peerj-cs.206 |
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