Study Of Evaluation of Complexity Metrics

Abstract

The issue of the safety of railways is particularly a challenge in the countries that have extensive and active rail systems such as India. The given research suggests an AI-driven solution that will help to predict and prevent railway accidents. It does so by constantly checking the state of the operations and detecting the possible hazard in real time. The system is based on the idea of computer vision, sensor fusion, and predictive analytics processing data obtained by cameras and IoT-sensors deployed on the railways and on trains. An intelligent centralized AI unit with edge computing can help make fast decisions and react promptly to threatening circumstances. Early warnings can be given through this system and there are automated safety options such as the gate controls, alert systems and track-switching support system. The primary aim is to minimize the non-compliance, enhance the safety in the crossings, and enhance the overall operational reliability. Also, the system is equipped with microcontroller-based automation that can guarantee rapid and effective safety measures. The proposed system provides an effective and low-cost answer to the present day railway system by minimizing human participation, and bypassing the time delay caused by manual decision-making