This study presents a solution to enhance the cities’ traffic control by classifying particular vehicles’ behaviors. A Support Vector Machine (SVM) approach is presented, enabling the system to classify cars that are looking to park and those that are simply transiting through a city. Through this paper, we also propose a new way of managing the high density of traffic data using a grid. The results show that the system is able to distinguish the two different behaviors with an accuracy averaging 80%.
Détails
Titre
Classifying vehicles' behaviors using global positioning systems information
Auteur(s)/ trice(s)
Silacci, Alessandro (School of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland) Tscherrig, Julien (School of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland) Mugellini, Elena (School of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland) Abou Khaled, Omar (School of Engineering and Architecture (HEIA-FR), HES-SO University of Applied Sciences Western Switzerland)
Date
2019-02
Publié dans
Proceedings of the 13th International Conference on Digital Society and eGovernments ICDS 2019, 24-28 February 2019, Athens, Greece
Publié par
Athens, Greece, 24-28 February 2019
Pagination
5 p.
Présenté à
International Conference on Digital Society and eGovernements (ICDS), Athens, Greece, 2019-02-24, 2019-02-28