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Anomaly Detection for Road Traffic: A Visual Analytics Framework
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificia l Intelligence Lab (SAIL))ORCID iD: 0000-0003-2900-9335
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Interaction Lab (ILAB))ORCID iD: 0000-0001-6310-346X
Volvo Group Trucks Technology (GTT), Advanced Technology and Research, Gothenburg, Sweden.
2017 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 18, no 8, p. 2260-2270, article id 7887700Article in journal (Refereed) Published
Abstract [en]

The analysis of large amounts of multidimensional road traffic data for anomaly detection is a complex task. Visual analytics can bridge the gap between computational and human approaches to detecting anomalous behavior in road traffic, making the data analysis process more transparent. In this paper, we present a visual analytics framework that provides support for: 1) the exploration of multidimensional road traffic data; 2) the analysis of normal behavioral models built from data; 3) the detection of anomalous events; and 4) the explanation of anomalous events. We illustrate the use of this framework with examples from a large database of real road traffic data collected from several areas in Europe. Finally, we report on feedback provided by expert analysts from Volvo Group Trucks Technology, regarding its design and usability.

Place, publisher, year, edition, pages
2017. Vol. 18, no 8, p. 2260-2270, article id 7887700
Keywords [en]
Anomaly detection, visual analytics, normal traffic model, intelligent transport systems
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Interaction Lab (ILAB); INF301 Data Science
Identifiers
URN: urn:nbn:se:his:diva-14111DOI: 10.1109/TITS.2017.2675710ISI: 000407347300022Scopus ID: 2-s2.0-85017131904OAI: oai:DiVA.org:his-14111DiVA, id: diva2:1141222
Funder
Knowledge Foundation, 20140294Available from: 2017-09-14 Created: 2017-09-14 Last updated: 2018-06-11Bibliographically approved

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Riveiro, MariaLebram, Mikael

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