Advanced Transport Engineering Techniques for Monitoring Driving Habits Through In-vehicle Telematics Systems

Year : 2024 | Volume : 11 | Issue : 03 | Page : 33-42
    By

    Shahin Mirbakhsh,

Abstract

Global road traffic fatalities are on the rise, largely due to preventable driving behaviors. In-vehicle telematics has emerged as a technology capable of enhancing driving behavior, widely adopted by insurance companies to monitor their clients’ behaviors. This systematic synthesizes how in-vehicle telematics has been modeled and analyzed. The paper conducted electronic searches on Scopus and Web of Science, selecting studies with a sample size of at least 10 participants, data collected over multiple days, and publication dates from 2010 onwards. Forty-five relevant papers were included, with 27 rated as “good” in quality assessment. The literature showed a split in focus regarding in-vehicle telematics. Some studies explored its utility for insurance purposes, while others investigated its impact on driving behavior. Machine learning analyses were prevalent, particularly in studies focusing on insurance outcomes. Acceleration, braking, and speed were the most frequently analyzed variables. Future research should include demographic information to understand how in-vehicle telematics influences driving behaviors across different demographic groups. Additionally, employing multi-level models would better capture the hierarchical nature of telematics data, which includes individual trip data for each driver.

Keywords: Advanced transport engineering, in-vehicle telematics, driving habits monitoring, road traffic fatalities, driving behavior analysis

[This article belongs to Trends in Transport Engineering and Applications ]

How to cite this article:
Shahin Mirbakhsh. Advanced Transport Engineering Techniques for Monitoring Driving Habits Through In-vehicle Telematics Systems. Trends in Transport Engineering and Applications. 2024; 11(03):33-42.
How to cite this URL:
Shahin Mirbakhsh. Advanced Transport Engineering Techniques for Monitoring Driving Habits Through In-vehicle Telematics Systems. Trends in Transport Engineering and Applications. 2024; 11(03):33-42. Available from: https://journals.stmjournals.com/ttea/article=2024/view=196989



References

  1. Australian Institute of Health and Welfare. Australian Burden of Disease Study 2022, Summary. [Online]. 2022. Available at https://www.aihw.gov.au/reports/burden-of-disease/australian-burden-of-disease-study-2022/contents/summary.
  2. Ayuso M, Guillén M, Nielsen JP. Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data. SSRN Electron J. [Online]. 2019. Available at https://doi.org/10.2139/ssrn.2885214.
  3. Azadani M, Boukerche A. Siamese temporal convolutional networks for driver identification using driver steering behavior analysis. IEEE Trans Intell Transport Syst. 2022; 23 (10): 18076–18087.
  4. Brühwiler L, Fu C, Huang H, Longhi L, Weibel R. Predicting individuals’ car accident risk by trajectory, driving events, and geographical context. Comput Environ Urban Syst. 2022; 93: 101760.
  5. Choudhary V, Shunko M, Netessine S, Koo S. Nudging drivers to safety: evidence from a field experiment. SSRN Electron J. [Online]. 2019. Available at https://doi.org/10.2139/ssrn.3491302.
  6. Peer S, Muermann A, Sallinger K. App-based feedback on safety to novice drivers: learning and monetary incentives. Transport Res Part F Traffic Psychol Behav. 2020; 71: 198–219.
  7. Savelonas M, Vernikos I, Mantzekis D, Spyrou E, Tsakiri A, Karkanis S. Hybrid representation of sensor data for the classification of driving behaviour. Appl Sci. 2021; 11 (18): 8574.
  8. Kadkhodayi A, Jabeli M, Aghdam H, Mirbakhsh S. Artificial intelligence-based real-time traffic management. J Electr Electron Eng. 2023; 2 (4): 368–373.
  9. Choudhary V, Shunko M, Netessine S. Does immediate feedback make you not try as hard? A study on automotive telematics. Manuf Serv Oper Manage. 2021; 23 (4): 835–853.
  10. Desai M, Phadke A. Internet of things based vehicle monitoring system. In: 2017 Fourteenth International Conference on Wireless and Optical Communications Networks (WOCN), Mumbai, India, February 24–26, 2017. pp. 1–3. doi: 1109/WOCN.2017.8065840.
  11. Eftekhari HR, Ghatee M. A similarity-based neuro-fuzzy modeling for driving behavior recognition applying fusion of smartphone sensors. J Intell Transport Syst. 2019; 23 (1): 72–83.
  12. Figueredo GP, Agrawal U, Mase JM, Mesgarpour M, Wagner C, Soria D, et al. Identifying heavy goods vehicle driving styles in the United Kingdom. IEEE Trans Intell Transport Syst. 2019; 20 (9): 3324–3336.
  13. Liu L, Xue J, Mahdi M, Mirbakhsh S, Du Q. Enhanced automated intersection control for platooning AVs: a fusion model with significant traffic, safety, and environmental improvements. J Electr Electron Eng. 2023; 2 (4): 359–367.
  14. Ayuso M, Pérez-Marín AM. The use of telematics devices to improve automobile insurance rates. Risk Anal. 2019; 39 (3): 662–672.
  15. Henckaerts R, Antonio K. The added value of dynamically updating motor insurance prices with telematics collected driving behavior data. Insurance Math Econ. 2022; 105: 79–95.
  16. Høye A. Vehicle registration year, age, and weight – untangling the effects on crash risk. Accid Anal Prev. 2019; 123: 1–11.
  17. Kirushanth S, Kabaso B. Telematics and road safety. In: 2018 2nd International Conference on Telematics and Future Generation Networks (TAFGEN), Kuching, Malaysia, July 24–26, 2018. pp. 103–108.
  18. Li S, Xu LD, Zhao S. The internet of things: a survey. Inform Syst Front. 2015; 17: 243–259.
  19. Mantouka EG, Vlahogianni EI. Deep reinforcement learning for personalized driving recommendations to mitigate aggressiveness and riskiness: modeling and impact assessment. Transport Res Part C Emerg Technol. 2022; 142: 103770.
  20. Mao H, Guo F, Deng X, Doerzaph ZR. Decision-adjusted driver risk predictive models using kinematics information. Accid Anal Prev. 2021; 156: 106088.
  21. Mase JM, Majid S, Mesgarpour M, Torres MT, Figueredo GP, Chapman P. Evaluating the impact of heavy goods vehicle driver monitoring and coaching to reduce risky behaviour. Accid Anal Prev. 2020; 146: 105754.
  22. Meng S, Wang H, Shi Y, Gao G. Improving automobile insurance claims frequency prediction with telematics car driving data. ASTIN Bull. 2021; 52 (2): 363–391.
  23. NSW Insurance Regulatory Authority. NSW Young Drivers Telematics Trial. [Online]. 2019. Available at https://www.sira.nsw.gov.au/__data/assets/pdf_file/0010/556264/NSW-Young-Drivers-Telematics-Trial.pdf.
  24. Osafune T, Takahashi T, Kiyama N, Sobue T, Yamaguchi H, Higashino T. Analysis of accident risks from driving behaviors. Int J Intell Transport Syst Res. 2016; 15 (3): 192–202.
  25. Payyanadan RP, Maus A, Sanchez FA, Lee JD, Miossi L, Abera A, et al. Using trip diaries to mitigate route risk and risky driving behavior among older drivers. Accid Anal Prev. 2017; 106: 480–491.
  26. Priyadharshini G, Ferni Ukrit M. An empirical evaluation of importance-based feature selection methods for the driver identification task using OBD data. Int J Syst Assur Eng Manage. 2022. In press. doi: 1007/s13198-022-01695-1.
  27. Siami M, Naderpour M, Lu J. A mobile telematics pattern recognition framework for driving behavior extraction. IEEE Trans Intell Transport Syst. 2021; 22 (3): 1459–1472.
  28. So B, Boucher JP, Valdez EA. Cost-sensitive multi-class AdaBoost for understanding driving behavior with telematics. SSRN Electron J. [Online]. 2020. Available at https://doi.org/10.2139/ssrn.3644708.
Regular Issue Subscription Original Research
Volume 11
Issue 03
Received 05/09/2024
Accepted 12/09/2024
Published 16/09/2024
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