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Classifying Vehicles' Behaviors using Global Positioning Systems Information

Authors:
Alessandro Silacci
Julien Tscherrig
Elena Mugellini
Omar Abou Khaled

Keywords: Machine learning; Intelligent Traffic System; Feature Selection; Global Positioning System

Abstract:
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%.

Pages: 50 to 53

Copyright: Copyright (c) IARIA, 2019

Publication date: February 24, 2019

Published in: conference

ISSN: 2308-3956

ISBN: 978-1-61208-685-9

Location: Athens, Greece

Dates: from February 24, 2019 to February 28, 2019