Abstract: Driving in dense fog creates significant challenges, particularly in Asian countries like Pakistan, where increasing traffic and air pollution contribute to reduced visibility, elevate the risk of ac-cidents, property damage, and fatalities. Accidents in such conditions are worsened by vehicle congestion and poor weather, such as dense fog. To address these issues, this study proposes an IoT-based intelligent accident detection and early warning system that uses integrated smartphone sensors to detect and monitor vehicular collisions. The system enhances risk manage-ment by autonomously detecting accidents and instantly trans-mitting essential information, including precise location, to emergency response networks for timely intervention and deci-sion-making. Additionally, the system alerts driver to possible near-collisions or hazardous conditions through real-time warn-ing alert, displayed via the Blynk application. Utilizing a smartphone's built-in sensors to detect vehicular collisions and notify the nearest first responders, along with providing real-time location tracking for paramedics and emergency victims, can significantly enhance recovery chances for victims while reducing both time and costs. The operational reliability and accuracy of the IoT-based framework for smart transportation are evaluated through numerical and simulation-based experiments, validating its efficacy in harsh environmental conditions.

Jinsong Tao, Rahat Ali, Shakeel Ahmad and Fasahat Ali, “IoT-Based Smart Accident Detection and Early Warning System for Emergency Response and Risk Management” International Journal of Advanced Computer Science and Applications(IJACSA), 16(3), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160365

@article{Tao2025,
title = {IoT-Based Smart Accident Detection and Early Warning System for Emergency Response and Risk Management},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160365},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160365},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {3},
author = {Jinsong Tao and Rahat Ali and Shakeel Ahmad and Fasahat Ali}
}