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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/45QPK8P
Repositorysid.inpe.br/mtc-m21d/2021/11.18.13.02   (restricted access)
Last Update2021:11.18.13.02.53 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2021/11.18.13.02.53
Metadata Last Update2022:04.03.23.14.05 (UTC) administrator
DOI10.1109/TITS.2020.3003111
ISSN1524-9050
Citation KeyGattoFors:2021:AuMaLe
TitleAudio-Based Machine Learning Model for Traffic Congestion Detection
Year2021
MonthNov.
Access Date2024, May 09
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size2012 KiB
2. Context
Author1 Gatto, Rubens Cruz
2 Forster, Carlos Henrique Quartucci
Resume Identifier1 8JMKD3MGP5W/3C9JJ7D
ORCID1 0000-0003-3803-505X
2 0000-0003-3390-1051
Group1 COPDT-CGIP-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Tecnológico de Aeronáutica (ITA)
Author e-Mail Address1 rubens.gatto@inpe.br
2 forster@ita.br
JournalIEEE Transactions on Intelligent Transportation Systems
Volume22
Number11
Pages7200-7207
Secondary MarkA1_ENGENHARIAS_IV A1_ENGENHARIAS_I B1_CIÊNCIA_DA_COMPUTAÇÃO
History (UTC)2021-11-18 13:03:59 :: simone -> administrator :: 2021
2022-04-03 23:14:05 :: administrator -> simone :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsaudio signal processing
machine learning
Traffic
AbstractThe present work approaches intelligent traffic evaluation and congestion detection using sound sensors and machine learning. For this, two important problems are addressed: traffic condition assessment from audio data, and analysis of audio under uncontrolled environments. By modeling the traffic parameters and the sound generation from passing vehicles and using the produced audio as a source of data for learning the traffic audio patterns, we provide a solution that copes with the time, the cost and the constraints inherent to the activity of traffic monitoring. External noise sources were introduced to produce more realistic acoustic scenes and to verify the robustness of the methods presented. Audio-based monitoring becomes a simple and low-cost option, comparing to other methods based on detector loops, or GPS, and as good as camera-based solutions, without some of the common problems of image-based monitoring, such as occlusions and light conditions. The approach is evaluated with data from audio analysis of traffic registered in locations around the city of São Jose dos Campos, Brazil, and audio files from places around the world, downloaded from YouTube. Its validation shows the feasibility of traffic automatic audio monitoring as well as using machine learning algorithms to recognize audio patterns under noisy environments.
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4. Conditions of access and use
Languageen
Target Filegatto_audio.pdf
User Groupsimone
Reader Groupadministrator
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Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KUES5
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.59.44 2
sid.inpe.br/bibdigital/2022/04.03.23.11 2
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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