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dc.contributor.author
Tasios, Dimitrios
en
dc.date.accessioned
2020-05-20T13:55:25Z
dc.date.available
2020-05-21T00:00:51Z
dc.date.issued
2020-05-20
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29446
dc.rights
Default License
dc.subject
Data mining
en
dc.subject
Traffic data
el
dc.title
Mining Traffic Data
en
heal.type
masterThesis
en_US
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Data Science
en_US
heal.publicationDate
2018-12-12
heal.abstract
Over 1.25 million people are killed, and 20-50 million people are seriously impacted by road traffic injuries on earth every year according to the world bank. This dissertation aims to the identification of traffic accident patterns in Cyprus, according to data collected by the local Police from 2007 to 2014. The dataset contains general, human based and vehicle-based information about the accidents. With the help of data mining, several patterns are extracted. Several classifiers were applied to the dataset in order to extract patterns related to the human factor, the car factor and the general data for every single accident. Findings from classification could be used by local authorities for acci-dent prevention and by insurance companies for risk analysis.
el
heal.advisorName
Tjortjis, Christos
el
heal.committeeMemberName
Berberidis, Christos
en
heal.committeeMemberName
Baltagiannis, Agamemnon
en
heal.academicPublisher
IHU
el
heal.academicPublisherID
ihu
en_US


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