Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/9702730 |
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| _version_ | 1832548793840566272 |
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| author | Arijit Chowdhury Tapas Chakravarty Avik Ghose Tanushree Banerjee P. Balamuralidhar |
| author_facet | Arijit Chowdhury Tapas Chakravarty Avik Ghose Tanushree Banerjee P. Balamuralidhar |
| author_sort | Arijit Chowdhury |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-07b7365929974d72971a1849dc488272 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-07b7365929974d72971a1849dc4882722025-02-03T06:12:55ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/97027309702730Investigations on Driver Unique Identification from Smartphone’s GPS Data AloneArijit Chowdhury0Tapas Chakravarty1Avik Ghose2Tanushree Banerjee3P. Balamuralidhar4Tata Consultancy Services, Building 1B, Ecospace, Plot IIF/12, New Town, Rajarhat, Kolkata, West Bengal 700156, IndiaTata Consultancy Services, Building 1B, Ecospace, Plot IIF/12, New Town, Rajarhat, Kolkata, West Bengal 700156, IndiaTata Consultancy Services, Building 1B, Ecospace, Plot IIF/12, New Town, Rajarhat, Kolkata, West Bengal 700156, IndiaTata Consultancy Services, Building 1B, Ecospace, Plot IIF/12, New Town, Rajarhat, Kolkata, West Bengal 700156, IndiaTata Consultancy Services, Whitefield, Bangalore, IndiaDriver 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.http://dx.doi.org/10.1155/2018/9702730 |
| spellingShingle | Arijit Chowdhury Tapas Chakravarty Avik Ghose Tanushree Banerjee P. Balamuralidhar Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone Journal of Advanced Transportation |
| title | Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone |
| title_full | Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone |
| title_fullStr | Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone |
| title_full_unstemmed | Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone |
| title_short | Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone |
| title_sort | investigations on driver unique identification from smartphone s gps data alone |
| url | http://dx.doi.org/10.1155/2018/9702730 |
| work_keys_str_mv | AT arijitchowdhury investigationsondriveruniqueidentificationfromsmartphonesgpsdataalone AT tapaschakravarty investigationsondriveruniqueidentificationfromsmartphonesgpsdataalone AT avikghose investigationsondriveruniqueidentificationfromsmartphonesgpsdataalone AT tanushreebanerjee investigationsondriveruniqueidentificationfromsmartphonesgpsdataalone AT pbalamuralidhar investigationsondriveruniqueidentificationfromsmartphonesgpsdataalone |