Abstract

Efficient rake scheduling and demand forecasting in railway operations are essential to address the complexities of passenger demand, minimize delays, and enhance utilization. This project uses advanced machine learning methods, specifically LSTM (Long Short-Term Memory) networks and GBM (Gradient Boosting Machine), to predict demand and optimize rake scheduling dynamically. Integrating a user-friendly web interface allows realtime data monitoring, enabling railway operators to make informed decisions. By leveraging real-time data sources, including rake movement, schedules, weather, and traffic conditions, this project aims to improve operational efficiency and responsiveness. Keywords. Railway Scheduling, Rake Optimization, Demand Forecasting, LSTM, GBM, Machine Learning, RealTime Data