Evaluation of Outlier Detection Algorithms for Traffic Congestion Assessment in Smart City Traffic Data from Vehicle Sensors
On-board sensors in vehicles are able to capture real-time data representations of variables conditioning the traffic flow. Extracting knowledge by combining data from different vehicles, together with machine learning algorithms, will help both to optimize transportation systems and to maximize the drivers' and passengers' comfort. This paper provides a summary of the most common multivariate outlier detection methods and applies them to data captured from sensor vehicles with the aim to find and identify different abnormal driving conditions like traffic jams. Outlier detection represents an important task in discovering useful and valuable information, as has been proven in numerous researches. This study is based on the combination of outlier detection mechanisms together with data classification methods. The output of the outlier detection phase will then be fed into several classifiers, which have been implemented to assess if the multivariate outliers correspond with traffic congestion situations or not.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/1744232X
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Supplemental Notes:
- Copyright © 2018 Inderscience Enterprises Ltd.
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Authors:
- Bl�zquez, Ramona Ruiz
- Organero, Mario Muñoz
- Fern�ndez, Luis S�nchez
- Publication Date: 2018
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 308-321
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Serial:
- International Journal of Heavy Vehicle Systems
- Volume: 25
- Issue Number: 3-4
- Publisher: Inderscience Enterprises Limited
- ISSN: 1744-232X
- EISSN: 1741-5152
- Serial URL: http://www.inderscience.com/jhome.php?jcode=IJHVS
Subject/Index Terms
- TRT Terms: Data collection; Data management; In vehicle sensors; Real time information; Traffic congestion; Traffic flow; Traffic surveillance
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01689620
- Record Type: Publication
- Files: TRIS
- Created Date: Dec 21 2018 5:18PM