ARCHIVES

Managing passenger congestion on railway platforms is a significant challenge, particularly during peak hours, where uneven crowd distribution across train compartments leads to delays and safety risks. This paper presents a Real-Time Visual Crowd Guidance System for Railway Stations (PreRideVision), designed to improve passenger flow and ensure safer boarding. The system uses a camera connected to a Python-based processing unit to continuously monitor crowd density near each train compartment. Computer vision techniques, specifically OpenCV-based people detection, are employed to analyze the captured video in real time and classify crowd levels. Based on the detected crowd conditions, the processed data is transmitted via serial communication to an ESP32 microcontroller, which controls visual guidance components such as red, yellow, and green LED indicators, a buzzer, and an LCD display. When high crowd density is detected, the system activates a red indicator along with a warning message prompting passengers to move to the next compartment; moderate crowd levels are indicated using yellow signals, while low-density areas are marked with green indicators encouraging entry.In addition to crowd indication, the system supports basic passenger movement guidance by directing users toward less crowded compartments and helping regulate entry and exit flow during train boarding. By relying on clear visual indicators rather than mobile applications, the system remains accessible to all passengers. This real-time visual and audio guidance approach reduces congestion, improves passenger distribution, and enhances overall safety. The proposed solution is cost-effective, scalable, and suitable for integration into existing railway infrastructure.