Intelligent Rush Hour Management in Metro Station

Abstract

Addressing the issue of high crowd density in metro stations during rush hours is indeed a significant challenge, but innovative solutions can help enhance passenger experience and streamline the boarding process. The goal is to implement a Smart Crowd Management System that provides real-time information about congestion levels in metro stations and estimates the time required for passengers to board trains during peak hours. The implementation of a Smart Crowd Management System can significantly improve the passenger experience in metro stations, making the commute more efficient and less stressful during rush hours. This proposal outlines a holistic approach combining sensor technology, machine learning, digital communication, and mobile applications to address the challenges of crowd density in metropolitan cities like Delhi. In this work, an intelligent system is developed with MATLAB/Simulink interface having fuzzy logic and neural network classifier to indicate expected time of departure and degree of congestion in the station. The outputs are displayed in TFT screen, LEDs and ThingSpeak-IoT platform. © 2024 IEEE.