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Correcting and complementing freeway traffic accident data using mahalanobis distance based outlier detection
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.ORCID iD: 0000-0001-5824-425X
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.ORCID iD: 0000-0003-0891-2859
2017 (English)In: Technical Gazette, ISSN 1330-3651, E-ISSN 1848-6339, Vol. 24, no 5, p. 1597-1607Article in journal (Refereed) Published
Abstract [en]

A huge amount of traffic data is archived which can be used in data mining especially supervised learning. However, it is not being fully used due to lack of accurate accident information (labels). In this study, we improve a Mahalanobis distance based algorithm to be able to handle differential data to estimate flow fluctuations and detect accidents and use it to support correcting and complementing accident information. The outlier detection algorithm provides accurate suggestions for accident occurring time, duration and direction. We also develop a system with interactive user interface to realize this procedure. There are three contributions for data handling. Firstly, we propose to use multi-metric traffic data instead of single metric for traffic outlier detection. Secondly, we present a practical method to organise traffic data and to evaluate the organisation for Mahalanobis distance. Thirdly, we describe a general method to modify Mahalanobis distance algorithms to be updatable. © 2017, Strojarski Facultet. All rights reserved.

Place, publisher, year, edition, pages
Strojarski Facultet , 2017. Vol. 24, no 5, p. 1597-1607
Keyword [en]
Accident data, Data labelling, Differential distance, Mahalanobis distance, Outlier detection, Traffic data, Updatable algorithm, Accidents, Data mining, Statistics, User interfaces, Mahalanobis distances, Data handling
National Category
Communication Systems Computer and Information Sciences
Identifiers
URN: urn:nbn:se:bth-15472DOI: 10.17559/TV-20150616163905ISI: 000417100300037Scopus ID: 2-s2.0-85032512786OAI: oai:DiVA.org:bth-15472DiVA: diva2:1156201
Note

Funded by National Natural Science Foundation of China

Funding nr. 61364019

Available from: 2017-11-10 Created: 2017-11-10 Last updated: 2018-01-16Bibliographically approved

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Sun, BinCheng, WeiBai, GuohuaGoswami, Prashant

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