Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively
Driver profiling is a widely used tool in fleet management and driver-specific insurance because it differentiates drivers based on their driving behaviors, such as aggressive and non-aggressive, which correspond to different levels of driving risk. However, most existing driver profiling methods re...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/180908 |
| _version_ | 1826127245732741120 |
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| author | Lyu, Shengfei Wang, Di Yang, Xuehao Miao, Chunyan |
| author2 | College of Computing and Data Science |
| author_facet | College of Computing and Data Science Lyu, Shengfei Wang, Di Yang, Xuehao Miao, Chunyan |
| author_sort | Lyu, Shengfei |
| collection | NTU |
| description | Driver profiling is a widely used tool in fleet management and driver-specific insurance because it differentiates drivers based on their driving behaviors, such as aggressive and non-aggressive, which correspond to different levels of driving risk. However, most existing driver profiling methods require all drivers to drive on the same predefined route or type of roads, simply to make sure their driving behaviors are comparable. This premise makes these methods not be able to profile drivers who drive on arbitrary roads, which constitute the real-world scenarios for most drivers. To enable the profiling of drivers using their naturalistic driving data, i.e., driving trajectories recorded while they were driving on arbitrary roads at their own free will, in this paper, we propose a novel method named cLustering rOads And Drivers Successively (LOADS). Specifically, LOADS first categorizes the roads into different types using the extracted characteristics of all drivers driving on the respective roads. It then groups drivers into different clusters to obtain their profile labels (e.g., aggressive or non-aggressive) using the extracted driving characteristics on each road type. We conduct extensive experiments using two real-world driving trajectory datasets comprising thousands of driving trajectories of hundreds of drivers. Statistical analysis results indicate that the driver groups identified by LOADS have significantly different driving styles. To the best of our knowledge, LOADS is the first method that focuses on profiling drivers who drive on arbitrary roads, showing a great potential to enable real-world driver profiling applications. |
| first_indexed | 2025-03-09T14:30:52Z |
| format | Journal Article |
| id | ntu-10356/180908 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T14:30:52Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1809082024-11-04T05:41:41Z Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively Lyu, Shengfei Wang, Di Yang, Xuehao Miao, Chunyan College of Computing and Data Science Continental-NTU Corporate Lab Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Computer and Information Science Driver profiling Arbitrary roads Driver profiling is a widely used tool in fleet management and driver-specific insurance because it differentiates drivers based on their driving behaviors, such as aggressive and non-aggressive, which correspond to different levels of driving risk. However, most existing driver profiling methods require all drivers to drive on the same predefined route or type of roads, simply to make sure their driving behaviors are comparable. This premise makes these methods not be able to profile drivers who drive on arbitrary roads, which constitute the real-world scenarios for most drivers. To enable the profiling of drivers using their naturalistic driving data, i.e., driving trajectories recorded while they were driving on arbitrary roads at their own free will, in this paper, we propose a novel method named cLustering rOads And Drivers Successively (LOADS). Specifically, LOADS first categorizes the roads into different types using the extracted characteristics of all drivers driving on the respective roads. It then groups drivers into different clusters to obtain their profile labels (e.g., aggressive or non-aggressive) using the extracted driving characteristics on each road type. We conduct extensive experiments using two real-world driving trajectory datasets comprising thousands of driving trajectories of hundreds of drivers. Statistical analysis results indicate that the driver groups identified by LOADS have significantly different driving styles. To the best of our knowledge, LOADS is the first method that focuses on profiling drivers who drive on arbitrary roads, showing a great potential to enable real-world driver profiling applications. Agency for Science, Technology and Research (A*STAR) This study is supported under the RIE2020 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). 2024-11-04T05:41:41Z 2024-11-04T05:41:41Z 2024 Journal Article Lyu, S., Wang, D., Yang, X. & Miao, C. (2024). Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively. Memetic Computing, 16(3), 255-267. https://dx.doi.org/10.1007/s12293-024-00416-4 1865-9284 https://hdl.handle.net/10356/180908 10.1007/s12293-024-00416-4 2-s2.0-85198090099 3 16 255 267 en IAF-ICP Memetic Computing © 2024 The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. All rights reserved. |
| spellingShingle | Computer and Information Science Driver profiling Arbitrary roads Lyu, Shengfei Wang, Di Yang, Xuehao Miao, Chunyan Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively |
| title | Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively |
| title_full | Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively |
| title_fullStr | Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively |
| title_full_unstemmed | Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively |
| title_short | Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively |
| title_sort | driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively |
| topic | Computer and Information Science Driver profiling Arbitrary roads |
| url | https://hdl.handle.net/10356/180908 |
| work_keys_str_mv | AT lyushengfei driverprofilingusingtrajectoriesonarbitraryroadsbyclusteringroadsanddriverssuccessively AT wangdi driverprofilingusingtrajectoriesonarbitraryroadsbyclusteringroadsanddriverssuccessively AT yangxuehao driverprofilingusingtrajectoriesonarbitraryroadsbyclusteringroadsanddriverssuccessively AT miaochunyan driverprofilingusingtrajectoriesonarbitraryroadsbyclusteringroadsanddriverssuccessively |