Public Transport Driver Identification System Using Histogram of Acceleration Data
This paper introduces a driver identification system architecture for public transport which utilizes only acceleration sensor data. The system architecture consists of three main modules which are the data collection, data preprocessing, and driver identification module. Data were collected from re...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/6372597 |
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| _version_ | 1832561012392329216 |
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| author | Nuttun Virojboonkiate Adsadawut Chanakitkarnchok Peerapon Vateekul Kultida Rojviboonchai |
| author_facet | Nuttun Virojboonkiate Adsadawut Chanakitkarnchok Peerapon Vateekul Kultida Rojviboonchai |
| author_sort | Nuttun Virojboonkiate |
| collection | DOAJ |
| description | This paper introduces a driver identification system architecture for public transport which utilizes only acceleration sensor data. The system architecture consists of three main modules which are the data collection, data preprocessing, and driver identification module. Data were collected from real operation of campus shuttle buses. In the data preprocessing module, a filtering module is proposed to remove the inactive period of the public transport data. To extract the unique behavior of the driver, a histogram of acceleration sensor data is proposed as a main feature of driver identification. The performance of our system is evaluated in many important aspects, considering axis of acceleration, sliding window size, number of drivers, classifier algorithms, and driving period. Additionally, the case study of impostor detection is implemented by modifying the driver identification module to identify a car thief or carjacking. Our driver identification system can achieve up to 99% accuracy and the impostor detection system can achieve the F1 score of 0.87. As a result, our system architecture can be used as a guideline for implementing the real driver identification system and further driver identification researches. |
| format | Article |
| id | doaj-art-4b370dcd9a854c29bf3f70a9c761d4f8 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-4b370dcd9a854c29bf3f70a9c761d4f82025-02-03T01:26:10ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/63725976372597Public Transport Driver Identification System Using Histogram of Acceleration DataNuttun Virojboonkiate0Adsadawut Chanakitkarnchok1Peerapon Vateekul2Kultida Rojviboonchai3Chulalongkorn University Big Data Analytics and IoT Center (CUBIC), Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandChulalongkorn University Big Data Analytics and IoT Center (CUBIC), Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandChulalongkorn University Big Data Analytics and IoT Center (CUBIC), Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandChulalongkorn University Big Data Analytics and IoT Center (CUBIC), Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandThis paper introduces a driver identification system architecture for public transport which utilizes only acceleration sensor data. The system architecture consists of three main modules which are the data collection, data preprocessing, and driver identification module. Data were collected from real operation of campus shuttle buses. In the data preprocessing module, a filtering module is proposed to remove the inactive period of the public transport data. To extract the unique behavior of the driver, a histogram of acceleration sensor data is proposed as a main feature of driver identification. The performance of our system is evaluated in many important aspects, considering axis of acceleration, sliding window size, number of drivers, classifier algorithms, and driving period. Additionally, the case study of impostor detection is implemented by modifying the driver identification module to identify a car thief or carjacking. Our driver identification system can achieve up to 99% accuracy and the impostor detection system can achieve the F1 score of 0.87. As a result, our system architecture can be used as a guideline for implementing the real driver identification system and further driver identification researches.http://dx.doi.org/10.1155/2019/6372597 |
| spellingShingle | Nuttun Virojboonkiate Adsadawut Chanakitkarnchok Peerapon Vateekul Kultida Rojviboonchai Public Transport Driver Identification System Using Histogram of Acceleration Data Journal of Advanced Transportation |
| title | Public Transport Driver Identification System Using Histogram of Acceleration Data |
| title_full | Public Transport Driver Identification System Using Histogram of Acceleration Data |
| title_fullStr | Public Transport Driver Identification System Using Histogram of Acceleration Data |
| title_full_unstemmed | Public Transport Driver Identification System Using Histogram of Acceleration Data |
| title_short | Public Transport Driver Identification System Using Histogram of Acceleration Data |
| title_sort | public transport driver identification system using histogram of acceleration data |
| url | http://dx.doi.org/10.1155/2019/6372597 |
| work_keys_str_mv | AT nuttunvirojboonkiate publictransportdriveridentificationsystemusinghistogramofaccelerationdata AT adsadawutchanakitkarnchok publictransportdriveridentificationsystemusinghistogramofaccelerationdata AT peeraponvateekul publictransportdriveridentificationsystemusinghistogramofaccelerationdata AT kultidarojviboonchai publictransportdriveridentificationsystemusinghistogramofaccelerationdata |