What question did this study set out to answer?

To develop a system that predicts train delays and provides advisory for passengers using machine learning techniques.

April 14, 2026Open Access

SmartRail: A Machine Learning-Based Train Delay Prediction and Passenger Advisory System

Key Points

To develop a system that predicts train delays and provides advisory for passengers using machine learning techniques.
Developed SmartRail as a web-based application using Flask, HTML, CSS, and JavaScript.
Utilized a Random Forest regression model trained on a structured dataset with journey attributes.
Implemented delay reason analysis and station wait planning features in the application.
Achieved an R² score of 0.9014 indicating strong predictive accuracy.
Resulted in a mean absolute error of 7.98 minutes in delay predictions on the test dataset.
Showcased the effectiveness of predictive analytics for improving passenger decision-making.

Abstract

This paper presents SmartRail, a machine learning-based train delay prediction and passenger advisory system developed as a final year project. The system uses a Random Forest regression model trained on a structured dataset to estimate train delays based on journey attributes such as category, season, day, and distance. The system provides additional features including delay reason analysis, passenger advisory, and station wait planning through a web-based interface built using Flask, HTML, CSS, JavaScript, and SQLite. The model achieved an R² score of 0.9014 and a mean absolute error of 7.98 minutes on the test dataset. Although the current implementation uses synthetic data, it demonstrates the potential of predictive analytics in improving railway passenger decision-making and intelligent transport systems.

SmartRail: A Machine Learning-Based Train Delay Prediction and Passenger Advisory System

Key Points

Abstract

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