Published Paper Details:

Railway accidents are a serious safety issue and are frequently triggered by track failures, signal malfunctions, and train collisions. In this paper, a Railway Accident Prevention System based on IoT-based sensors and real-time communication is proposed to improve railway safety. The system includes LiDAR sensors for detecting rail track cracks, wireless communication modules for train-to-train collision avoidance, and intelligent signaling mechanisms for avoiding human mistakes. The system to be proposed uses an ESP32 microcontroller for processing data and wireless notifications to inform railway authorities of impending dangers. Through real-time monitoring and automation, the system seeks to reduce derailments, signal accidents, and train collisions, making the railway network safer. Experimental results show enhanced accuracy in track anomaly detection and proper communication between trains and control stations. This study emphasizes the viability of IoT-based railway safety solutions for preventing accidents and enhancing operational efficiency.