Cargando…

Traffic congestion prediction based on Estimated Time of Arrival

With the rapid expansion of sensor technologies and wireless network infrastructure, research and development of traffic associated applications, such as real-time traffic maps, on-demand travel route reference and traffic forecasting are gaining much more attention than ever before. In this paper,...

Descripción completa

Detalles Bibliográficos
Autores principales: Zafar, Noureen, Ul Haq, Irfan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7743965/
https://www.ncbi.nlm.nih.gov/pubmed/33326460
http://dx.doi.org/10.1371/journal.pone.0238200
_version_ 1783624338379898880
author Zafar, Noureen
Ul Haq, Irfan
author_facet Zafar, Noureen
Ul Haq, Irfan
author_sort Zafar, Noureen
collection PubMed
description With the rapid expansion of sensor technologies and wireless network infrastructure, research and development of traffic associated applications, such as real-time traffic maps, on-demand travel route reference and traffic forecasting are gaining much more attention than ever before. In this paper, we elaborate on our traffic prediction application, which is based on traffic data collected through Google Map API. Our application is a desktop-based application that predicts traffic congestion state using Estimated Time of Arrival (ETA). In addition to ETA, the prediction system takes into account various features such as weather, time period, special conditions, holidays, etc. The label of the classifier is identified as one of the five traffic states i.e. smooth, slightly congested, congested, highly congested or blockage. The results demonstrate that the random forest classification algorithm has the highest prediction accuracy of 92 percent followed by XGBoost and KNN respectively.
format Online
Article
Text
id pubmed-7743965
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-77439652020-12-31 Traffic congestion prediction based on Estimated Time of Arrival Zafar, Noureen Ul Haq, Irfan PLoS One Research Article With the rapid expansion of sensor technologies and wireless network infrastructure, research and development of traffic associated applications, such as real-time traffic maps, on-demand travel route reference and traffic forecasting are gaining much more attention than ever before. In this paper, we elaborate on our traffic prediction application, which is based on traffic data collected through Google Map API. Our application is a desktop-based application that predicts traffic congestion state using Estimated Time of Arrival (ETA). In addition to ETA, the prediction system takes into account various features such as weather, time period, special conditions, holidays, etc. The label of the classifier is identified as one of the five traffic states i.e. smooth, slightly congested, congested, highly congested or blockage. The results demonstrate that the random forest classification algorithm has the highest prediction accuracy of 92 percent followed by XGBoost and KNN respectively. Public Library of Science 2020-12-16 /pmc/articles/PMC7743965/ /pubmed/33326460 http://dx.doi.org/10.1371/journal.pone.0238200 Text en © 2020 Zafar, Ul Haq http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zafar, Noureen
Ul Haq, Irfan
Traffic congestion prediction based on Estimated Time of Arrival
title Traffic congestion prediction based on Estimated Time of Arrival
title_full Traffic congestion prediction based on Estimated Time of Arrival
title_fullStr Traffic congestion prediction based on Estimated Time of Arrival
title_full_unstemmed Traffic congestion prediction based on Estimated Time of Arrival
title_short Traffic congestion prediction based on Estimated Time of Arrival
title_sort traffic congestion prediction based on estimated time of arrival
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7743965/
https://www.ncbi.nlm.nih.gov/pubmed/33326460
http://dx.doi.org/10.1371/journal.pone.0238200
work_keys_str_mv AT zafarnoureen trafficcongestionpredictionbasedonestimatedtimeofarrival
AT ulhaqirfan trafficcongestionpredictionbasedonestimatedtimeofarrival