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dc.contributor.authorSinclair, C
dc.contributor.authorDas, S
dc.date.accessioned2021-03-17T15:02:50Z
dc.date.issued2021-03-16
dc.description.abstractThe goal of this paper is to use the unsupervised machine learning method in road accident analytics, especially using k-means clustering to identify patterns and understand the relationships between variables recorded by the UK police department. These include features like number of casualties, number of vehicles, age of vehicle and age bracket of the driver. We aim to describe clusters of accidents based on similarity measures in the features and identify what separates each one.en_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.identifier.citation2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET), 21 - 23 January 2021, Hyderabad, Indiaen_GB
dc.identifier.doi10.1109/sefet48154.2021.9375817
dc.identifier.urihttp://hdl.handle.net/10871/125145
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.rightsCopyright © 2021, IEEEen_GB
dc.subjectclusteringen_GB
dc.subjectroad accidenten_GB
dc.subjectgeospatial analyticsen_GB
dc.titleTraffic accidents analytics in UK urban areas using k-means clustering for geospatial mappingen_GB
dc.typeConference paperen_GB
dc.date.available2021-03-17T15:02:50Z
dc.identifier.isbn978-1-7281-5681-1
dc.descriptionThis is the author accepted manuscript. The final version is available from IEEE via the DOI in this record en_GB
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2021-03-16
rioxxterms.typeConference Paper/Proceeding/Abstracten_GB
refterms.dateFCD2021-03-17T13:35:46Z
refterms.versionFCDAM
refterms.dateFOA2021-03-17T15:02:58Z
refterms.panelBen_GB


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