Novel innovative Technologies for Efficient Crowd Density Control and Real-time Monitoring in Public Transit Systems

The proposed work seeks to develop an intelligent safety monitoring system that can detect and alert users to emergency situations such as fires, overcrowding, and accidents. This system integrates a variety of sensors, including temperature sensors, fire detection sensors, ultrasonic sensors, and infrared (IR) sensors, to continuously monitor real-time conditions in a given environment. Leveraging advanced AI algorithms, the system enhances fire detection by analysing smoke and heat signatures, monitors crowd density to identify overcrowding, and uses motion patterns to detect accidents. When an emergency is detected, the system automatically sends SMS notifications to designated emergency contacts, activates auditory and visual alarms, and logs the incident for future analysis. A web-based dashboard provides a centralized platform for real-time visualization of sensor data, detected incidents, and alert logs, ensuring a quick response and effective monitoring. The system is scalable, supporting up to four sensors, and is designed for integration with local emergency services to facilitate incident management. Through installation, configuration, and testing of its components, the proposed work aims to improve public safety by offering real-time, automated responses to critical situations, minimizing risks, and ensuring timely interventions.