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 :
- 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), 16. https://www.ijrte.org/wp-content/uploads/papers/v11i6/F75060311623
- 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), 10651067.
- 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
- 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
- Fernandez, S. G., Palanisamy, R., & Vijayakumar, K. (2022). GPS & GSM based accident detection and auto intimation. Indonesian Journal of Electrical Engineering and Computer Science, 11(1), 356361. https://doi.org/10.11591/ijeecs.v11.i1.pp356-361
- Gorakh, J., Choudhary, A., & Bajanghate, P. (2024). GPS-based vehicle monitoring and challan generation. International Journal of Research in Computer and Information Technology, 2(1), Special Issue. Suryodaya College of Engineering & Technology, Nagpur, India.
- National Bureau of Statistics. (2025). Road transport data Q1 2025. https://nigerianstat.gov.ng/elibrary/read/1241395
- 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. 953964). Springer. https://doi.org/10.1007/978-981-16-0965-7_72
- 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), 870885. https://doi.org/10.35833/MPCE.2021.0006
- 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
- Zhang, Y., & Sung, Y. (2023). Traffic accident detection using background subtraction and CNN encodertransformer 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.