Real-Time Train Tracking and Time Prediction System For Railway System in Sri Lanka

This research focuses on enhancing Sri Lanka’s railway system through a Real-Time Train Tracking and Time Prediction System, tailored to the country’s unique railway network. The system integrates advanced technologies like machine learning and GPS for precise time predictions, which are communicated via a website. The study begins with an in-depth analysis of Sri Lanka’s existing railway infrastructure and operational complexities. Train NO. 1015, which travels daily from Colombo to Badulla, was selected for this research. Factors such as weather, passenger delays, operational requirements, signaling errors, and staff delays were identified as challenges to accurate time predictions through an extensive data analysis of past train delay data. The system design includes a machine learning algorithm using LSTM (Long Short-Term Memory) neural networks for predicting train arrival times, and a user-friendly interface for displaying these predictions. The goal is to provide real-time insights for railway authorities and reliable travel information for passengers through a user-friendly web application.