Design and Implementation of an Intelligent Vehicle Tracking and Accident Alert System


Authors : Olugbenga Oloniyo; Yusuf, Babatunde Misbau; Oladimeji Dupe Victoria

Volume/Issue : Volume 10 - 2025, Issue 9 - September


Google Scholar : https://tinyurl.com/ywr3n6wk

Scribd : https://tinyurl.com/ynkym7du

DOI : https://doi.org/10.38124/ijisrt/25sep348

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Abstract : Road traffic accidents are still a critical global public health problem, with delayed emergency response that contributes significantly to increased mortality rate. This paper presents the design, implementation, and comprehensive evaluation of a cost-effective vehicle tracking and accident alert system utilizing GPS and GSM technology. The proposed system continuously monitors vehicle location through a NEO-6M GPS module and detects collision events via an MPU6050 accelerometer using a multi-threshold detection algorithm. In the case of accident detection, the system automatically transmits precise location coordinates to predefined emergency contacts through a SIM800L GSM module, which significantly reduces critical alert time. The prototype underwent rigorous testing under different environmental conditions, demonstrating 96% accuracy in accident detection with a 4% false positive rate and an average alert time of 4.8 seconds. Field samples of driving in the real world confirmed reliable operation in urban, suburban and rural areas. This research contributes to intelligent transport systems by providing an accessible, open architecture solution that can significantly improve emergency services and potentially reduce mortality rates, especially in resource -limited environments.

Keywords : Accelerometer, Accident Detection, GPS, GSM, Vehicle Tracking.

References :

  1. Amat, R., Mallick, S., & Suna, P. (2023). Smart accident detection and emergency notification system with GPS and GSM integration. International Journal of Recent Technology and Engineering, 11(6), 1–6. https://www.ijrte.org/wp-content/uploads/papers/v11i6/F75060311623
  2. Bhoyar, M., Meshram, S., & Dhenge, A. (2024). Automatic vehicle accident detection and messaging system using Arduino Uno, GSM & GPS. International Journal of Research Publication and Reviews, 5(12), 1065–1067.
  3. Chitraranjan, C., Vipulananthan, V., & Sritharan, T. (2025). Vision-based collision warning systems with deep learning: A systematic review. J. Imaging, 11(2), 64. https://doi.org/10.3390/jimaging11020064
  4. Chowdhury, A., Kaisar, S., Khoda, M. E., Naha, R., Khoshkholghi, M. A., & Aiash, M. (2023). IoT-based emergency vehicle services in intelligent transportation system. Sensors, 23(11), 5324. https://doi.org/10.3390/s23115324
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  7. National Bureau of Statistics. (2025). Road transport data Q1 2025. https://nigerianstat.gov.ng/elibrary/read/1241395
  8. Srikanth, M. S., Kumar, T. G. K., & Sharma, V. (2021). Automatic vehicle service monitoring and tracking system using IoT and machine learning. In A. Pasumpon Pandian et al. (Eds.), Computer Networks, Big Data and IoT (Lecture Notes on Data Engineering and Communications Technologies, Vol. 66, pp. 953–964). Springer. https://doi.org/10.1007/978-981-16-0965-7_72
  9. Wang, H., Li, Z., Xue, Y., & Hao, L. (2021). Decision support system for adaptive restoration control of transmission system. Journal of Modern Power Systems and Clean Energy, 9(4), 870–885. https://doi.org/10.35833/MPCE.2021.0006
  10. World Health Organization. (2023). Global status report on road safety 2023. https://www.who.int/teams/social-determinants-of-health/safety-and-mobility/global-status-report-on-road-safety-2023
  11. Zhang, Y., & Sung, Y. (2023). Traffic accident detection using background subtraction and CNN encoder–transformer decoder in video frames. Mathematics, 11(13), 2884. https://doi.org/10.3390/math11132884

Road traffic accidents are still a critical global public health problem, with delayed emergency response that contributes significantly to increased mortality rate. This paper presents the design, implementation, and comprehensive evaluation of a cost-effective vehicle tracking and accident alert system utilizing GPS and GSM technology. The proposed system continuously monitors vehicle location through a NEO-6M GPS module and detects collision events via an MPU6050 accelerometer using a multi-threshold detection algorithm. In the case of accident detection, the system automatically transmits precise location coordinates to predefined emergency contacts through a SIM800L GSM module, which significantly reduces critical alert time. The prototype underwent rigorous testing under different environmental conditions, demonstrating 96% accuracy in accident detection with a 4% false positive rate and an average alert time of 4.8 seconds. Field samples of driving in the real world confirmed reliable operation in urban, suburban and rural areas. This research contributes to intelligent transport systems by providing an accessible, open architecture solution that can significantly improve emergency services and potentially reduce mortality rates, especially in resource -limited environments.

Keywords : Accelerometer, Accident Detection, GPS, GSM, Vehicle Tracking.

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Paper Submission Last Date
31 - December - 2025

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