Smart Metro: Real-Time Passenger Counting and Compartment Occupancy Optimization Using Iot and Deep Learning

The "Smart Metro: Real-time passenger counting and compartment occupancy optimization using IOT and Deep Learning" project focuses on optimizing urban transportation efficiency through the utilization of IoT technology for the collection and analysis of metro passenger data.The system employs a distributed peoplecounting approach, integrating hardware components like the ESP-32 Wi-Fi camera module and a 9V battery with the assistance of Raspberry Pi.Object detection is facilitated by the YOLO algorithm, providing real-time insights into compartment occupancy.The innovative system enables the monitoring of available space within metro coaches, and the collected data is accessible through a dedicated website.This empowers commuters to make informed decisions based on current passenger loads as a metro approaches the station.The primary objectives include improving capacity utilization, reducing congestion, and enhancing the overall commuter experience in smart cities.