Classifying vehicles' behaviors using global positioning systems information

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)

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%.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
HumanTech - Technology for Human Wellbeing Institute
Publisher:
Athens, Greece, 24-28 February 2019
Date:
2019-02
Athens, Greece
24-28 February 2019
Pagination:
5 p.
Published in:
Proceedings of the 13th International Conference on Digital Society and eGovernments ICDS 2019, 24-28 February 2019, Athens, Greece
ISSN:
2308-3965
ISBN:
978-1-61208-685-9
External resources:
Appears in Collection:



 Record created 2020-01-14, last modified 2020-01-14

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