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Journal of Advanced Transportation
Volume 2018, Article ID 9702730, 11 pages
https://doi.org/10.1155/2018/9702730
Research Article

Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone

1Tata Consultancy Services, Building 1B, Ecospace, Plot IIF/12, New Town, Rajarhat, Kolkata, West Bengal 700156, India
2Tata Consultancy Services, Whitefield, Bangalore, India

Correspondence should be addressed to Tapas Chakravarty; moc.liamg@eeei.sapat

Received 28 August 2017; Revised 27 December 2017; Accepted 15 January 2018; Published 8 February 2018

Academic Editor: David Martín

Copyright © 2018 Arijit Chowdhury et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Driver identification is an emerging area of interest in vehicle telematics, automobile control, and insurance. Recent body of works indicates that it may be possible to uniquely identify a driver using multiple dedicated sensors. In this paper, we present an approach for driver identification using smartphone GPS data alone. For our experiments, we collected data from 38 drivers for two months. We quantified the driver’s natural style by extracting a set of 137 statistical features from data generated for each completed trip. The analysis shows that, for the “driver identification” problem, an average accuracy of 82.3% is achieved for driver groups of 4-5 drivers. This is comparable to the state of the arts where mostly a multisensor approach has been taken. Further, it is shown that certain behavioral attributes like high driving skill impact identification accuracy. We observe that Random Forest classifier offers the best results. These results have great implications for various stakeholders since the proposed method can identify a driver based on his/her naturalistic driving style which is quantified in terms of statistical parameters extracted from only GPS data.