Trajectory User Linking in C-ITS Data Analysis

dc.contributor.author	Moso, Juliet Chebet
dc.contributor.author	Stéphane Cormier
dc.contributor.author	Hacène Fouchal
dc.contributor.author	Cyril de Runz
dc.contributor.author	Wandeto, John Mwangi
dc.date.accessioned	2021-05-24T09:43:06Z
dc.date.available	2021-05-24T09:43:06Z
dc.date.issued	2020-12-11
dc.identifier.issn	978-1-7281-7307-8
dc.identifier.uri	https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9322253
dc.identifier.uri	http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/4728
dc.description.abstract	Vehicles in an Intelligent Transport Network exchange a lot of messages. Every message sent is generated with an identifier of the transmitting vehicle. To respect the user privacy, an identifier is kept only over a specified time interval. The need that arises is, given that multiple identifiers are assigned to a vehicle, are we able to group the identifiers and detect those which belong to the same vehicle? We solved this Trajectory-User Linking problem by chaining anonymous trajectories to potential vehicles by considering similarity in movement patterns. Our method managed to link trajectory segments to their common vehicles which we validated through map matching of the trajectories using QGIS.	en_US
dc.language.iso	en	en_US
dc.publisher	IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM)	en_US
dc.title	Trajectory User Linking in C-ITS Data Analysis	en_US