Train Waitlisted Ticket Confirmation Prediction Using Machine Learning
2024 Second International Conference on Advanced Computing & Communication Technologies (ICACCTech)
Publisher: IEEE
Authors: Ruchi, Fardin Khan, Hitesh Kumar, Kamalveer Singh, Sachin Yadav
Overview
In India's railway system, millions of passengers face uncertainty due to waitlisted tickets. Predicting whether these tickets will get confirmed is a highly complex task, as it depends on multiple factors such as booking patterns, cancellations, passenger behavior, and travel seasonality.
Our Contribution
We developed a machine learning framework that predicts the likelihood of waitlisted tickets being confirmed with remarkable accuracy. By leveraging historical booking data, cancellation records, and passenger preferences, our system helps both passengers and railway authorities in making informed decisions.
Model Used: Light Gradient Boosting Machine (LightGBM)
Accuracy Achieved: 96.67%
Key Features Considered: booking time, travel routes, seasonal demand, and cancellation probabilities
Impact
For passengers: reduces uncertainty and improves travel planning.
For railway authorities: helps in efficient resource management, forecasting seat availability, and improving customer satisfaction.
For the future: opens doors to integrating real-time digital ticketing data for even more accurate predictions.
This work demonstrates how AI & ML can address real-world transportation challenges, making rail travel smarter and more reliable.