IoT-Based Smart Accident Detection and Early Warning System for Emergency Response and Risk Management

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.