Correlating the effect of Covid-19 lockdown with mobility impacts: a time Series study using noise sensors data

Detalhes bibliográficos
Autor(a) principal: Pascale, Antonio
Data de Publicação: 2022
Outros Autores: Mancini, Simona, d'Orey, Pedro, Guarnaccia, Claudio, Coelho, Margarida C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/33636
Resumo: The Covid-19 crisis forced governments around the world to rapidly enact several restrictions to face the associated health emergency. The Portuguese government was no exception and, following the example of other countries, established various limitations to flat the contagions curve. This led to inevitable repercussions on mobility and environmental indicators including noise. This research aims to assess the impact of the lockdown due to Covid-19 disease on the noise levels recorded in the city of Porto, Portugal. Data from four noise sensors located in strategic spots of the city were used to calibrate and validate Time Series Models, allowing to impute the missing values in the datasets and rebuild them. The trend and the cyclic information were extracted from the reconstructed datasets using decomposition techniques. Finally, a Spearman correlation analysis between noise levels values and traffic volumes (extracted from five inductive loop detectors, located nearby the noise sensors) was performed. Results show that the noise levels series present a daily seasonal pattern and the trends values decreased from 6.7 to 7.5 dBA during the first lockdown period (March-May 2020). Moreover, the noise levels tend to gradually rise after the removal of restrictions. Finally, there is a monotonic relationship between noise levels and traffic volumes values, as confirmed by the positive moderate-to-high correlation coefficients found, and the sharp drop of the former during March-May 2020 can be attributed to the strong reduction of road traffic flows in the city.
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spelling Correlating the effect of Covid-19 lockdown with mobility impacts: a time Series study using noise sensors dataTraffic NoiseTime Series ModelsCovid-19Inductive LoopsThe Covid-19 crisis forced governments around the world to rapidly enact several restrictions to face the associated health emergency. The Portuguese government was no exception and, following the example of other countries, established various limitations to flat the contagions curve. This led to inevitable repercussions on mobility and environmental indicators including noise. This research aims to assess the impact of the lockdown due to Covid-19 disease on the noise levels recorded in the city of Porto, Portugal. Data from four noise sensors located in strategic spots of the city were used to calibrate and validate Time Series Models, allowing to impute the missing values in the datasets and rebuild them. The trend and the cyclic information were extracted from the reconstructed datasets using decomposition techniques. Finally, a Spearman correlation analysis between noise levels values and traffic volumes (extracted from five inductive loop detectors, located nearby the noise sensors) was performed. Results show that the noise levels series present a daily seasonal pattern and the trends values decreased from 6.7 to 7.5 dBA during the first lockdown period (March-May 2020). Moreover, the noise levels tend to gradually rise after the removal of restrictions. Finally, there is a monotonic relationship between noise levels and traffic volumes values, as confirmed by the positive moderate-to-high correlation coefficients found, and the sharp drop of the former during March-May 2020 can be attributed to the strong reduction of road traffic flows in the city.Elsevier2022-04-07T14:56:23Z2022-01-01T00:00:00Z2022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/33636eng10.1016/j.trpro.2022.02.015Pascale, AntonioMancini, Simonad'Orey, PedroGuarnaccia, ClaudioCoelho, Margarida C.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T12:04:24Zoai:ria.ua.pt:10773/33636Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:04:53.900783Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Correlating the effect of Covid-19 lockdown with mobility impacts: a time Series study using noise sensors data
title Correlating the effect of Covid-19 lockdown with mobility impacts: a time Series study using noise sensors data
spellingShingle Correlating the effect of Covid-19 lockdown with mobility impacts: a time Series study using noise sensors data
Pascale, Antonio
Traffic Noise
Time Series Models
Covid-19
Inductive Loops
title_short Correlating the effect of Covid-19 lockdown with mobility impacts: a time Series study using noise sensors data
title_full Correlating the effect of Covid-19 lockdown with mobility impacts: a time Series study using noise sensors data
title_fullStr Correlating the effect of Covid-19 lockdown with mobility impacts: a time Series study using noise sensors data
title_full_unstemmed Correlating the effect of Covid-19 lockdown with mobility impacts: a time Series study using noise sensors data
title_sort Correlating the effect of Covid-19 lockdown with mobility impacts: a time Series study using noise sensors data
author Pascale, Antonio
author_facet Pascale, Antonio
Mancini, Simona
d'Orey, Pedro
Guarnaccia, Claudio
Coelho, Margarida C.
author_role author
author2 Mancini, Simona
d'Orey, Pedro
Guarnaccia, Claudio
Coelho, Margarida C.
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Pascale, Antonio
Mancini, Simona
d'Orey, Pedro
Guarnaccia, Claudio
Coelho, Margarida C.
dc.subject.por.fl_str_mv Traffic Noise
Time Series Models
Covid-19
Inductive Loops
topic Traffic Noise
Time Series Models
Covid-19
Inductive Loops
description The Covid-19 crisis forced governments around the world to rapidly enact several restrictions to face the associated health emergency. The Portuguese government was no exception and, following the example of other countries, established various limitations to flat the contagions curve. This led to inevitable repercussions on mobility and environmental indicators including noise. This research aims to assess the impact of the lockdown due to Covid-19 disease on the noise levels recorded in the city of Porto, Portugal. Data from four noise sensors located in strategic spots of the city were used to calibrate and validate Time Series Models, allowing to impute the missing values in the datasets and rebuild them. The trend and the cyclic information were extracted from the reconstructed datasets using decomposition techniques. Finally, a Spearman correlation analysis between noise levels values and traffic volumes (extracted from five inductive loop detectors, located nearby the noise sensors) was performed. Results show that the noise levels series present a daily seasonal pattern and the trends values decreased from 6.7 to 7.5 dBA during the first lockdown period (March-May 2020). Moreover, the noise levels tend to gradually rise after the removal of restrictions. Finally, there is a monotonic relationship between noise levels and traffic volumes values, as confirmed by the positive moderate-to-high correlation coefficients found, and the sharp drop of the former during March-May 2020 can be attributed to the strong reduction of road traffic flows in the city.
publishDate 2022
dc.date.none.fl_str_mv 2022-04-07T14:56:23Z
2022-01-01T00:00:00Z
2022
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/33636
url http://hdl.handle.net/10773/33636
dc.language.iso.fl_str_mv eng
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dc.relation.none.fl_str_mv 10.1016/j.trpro.2022.02.015
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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