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Audio-Based Machine Learning Model for Traffic Congestion Detection
- Source :
- IEEE Transactions on Intelligent Transportation Systems. 22:7200-7207
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- The 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 Sao 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.
- Subjects :
- SIMPLE (military communications protocol)
Computer science
business.industry
Mechanical Engineering
Detector
External noise
Machine learning
computer.software_genre
Computer Science Applications
Traffic congestion
Robustness (computer science)
Automotive Engineering
Audio analyzer
Global Positioning System
Mel-frequency cepstrum
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 15580016 and 15249050
- Volume :
- 22
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Intelligent Transportation Systems
- Accession number :
- edsair.doi...........98393d38f3d6873913704d2dca497803