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Title: Driver Behavior Pro ling using Machine Learning
Authors: Mullick, Soumajit
Khilar, Pabitra Mohan
Keywords: Vehicular Ad hoc Networks (VANET)
Machine Learning Intelligent Transportation Systems (ITSs)
Driver Behaviour
Safety Application
Issue Date: Mar-2020
Citation: International Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy (ETAEERE-2020), KIIT, Bhubaneswar, India, 5-6 March 2020
Abstract: The drivers’ behavior influences the traffic on road and this, in turn influences energy consumed by the vehicles and emission of pollutants from the vehicles. So it is necessary to identify drivers’ characteristics to profile their behav-ior correctly. A large amount of data is needed for the analysis which is collected by the on-Board Unit present on the vehicle. On-Board Unit has sensors that are used to collect the required data. The comparative performance of different ma-chine learning algorithms is evaluated on the data collected by the on-board unit and in turn help in profiling drivers’ behavior. The experimental result shows that the support vector machine gives an accuracy of 99.4% amongst the remaining classifier.
Description: Copyright of this paper is with proceedings publisher
Appears in Collections:Conference Papers

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