Using Classification for Traffic Prediction in Smart Cities

Smart cities emerge as highly sophisticated bionetworks, providing smart services and ground-breaking solutions. This paper relates classification with Smart City projects, particularly focusing on traffic prediction. A systematic literature review identifies the main topics and methods used, emphas...

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Autores principales: Christantonis, Konstantinos, Tjortjis, Christos, Manos, Anastassios, Filippidou, Despina Elizabeth, Mougiakou, Εleni, Christelis, Evangelos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256407/
http://dx.doi.org/10.1007/978-3-030-49161-1_5
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author Christantonis, Konstantinos
Tjortjis, Christos
Manos, Anastassios
Filippidou, Despina Elizabeth
Mougiakou, Εleni
Christelis, Evangelos
author_facet Christantonis, Konstantinos
Tjortjis, Christos
Manos, Anastassios
Filippidou, Despina Elizabeth
Mougiakou, Εleni
Christelis, Evangelos
author_sort Christantonis, Konstantinos
collection PubMed
description Smart cities emerge as highly sophisticated bionetworks, providing smart services and ground-breaking solutions. This paper relates classification with Smart City projects, particularly focusing on traffic prediction. A systematic literature review identifies the main topics and methods used, emphasizing on various Smart Cities components, such as data harvesting and data mining. It addresses the research question whether we can forecast traffic load based on past data, as well as meteorological conditions. Results have shown that various models can be developed based on weather data with varying level of success.
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spelling pubmed-72564072020-05-29 Using Classification for Traffic Prediction in Smart Cities Christantonis, Konstantinos Tjortjis, Christos Manos, Anastassios Filippidou, Despina Elizabeth Mougiakou, Εleni Christelis, Evangelos Artificial Intelligence Applications and Innovations Article Smart cities emerge as highly sophisticated bionetworks, providing smart services and ground-breaking solutions. This paper relates classification with Smart City projects, particularly focusing on traffic prediction. A systematic literature review identifies the main topics and methods used, emphasizing on various Smart Cities components, such as data harvesting and data mining. It addresses the research question whether we can forecast traffic load based on past data, as well as meteorological conditions. Results have shown that various models can be developed based on weather data with varying level of success. 2020-05-06 /pmc/articles/PMC7256407/ http://dx.doi.org/10.1007/978-3-030-49161-1_5 Text en © IFIP International Federation for Information Processing 2020 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 Article
Christantonis, Konstantinos
Tjortjis, Christos
Manos, Anastassios
Filippidou, Despina Elizabeth
Mougiakou, Εleni
Christelis, Evangelos
Using Classification for Traffic Prediction in Smart Cities
title Using Classification for Traffic Prediction in Smart Cities
title_full Using Classification for Traffic Prediction in Smart Cities
title_fullStr Using Classification for Traffic Prediction in Smart Cities
title_full_unstemmed Using Classification for Traffic Prediction in Smart Cities
title_short Using Classification for Traffic Prediction in Smart Cities
title_sort using classification for traffic prediction in smart cities
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256407/
http://dx.doi.org/10.1007/978-3-030-49161-1_5
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