RailSmart

Indian Railways is used by millions daily, yet passengers constantly face uncertainty due to delays and missed connections. Existing journey planners focus only on shortest or fastest routes, ignoring real-world unpredictability. We were inspired to solve this gap by creating a system that helps passengers choose not just fast routes, but reliable ones.

Our project, RailSmart AI, is an intelligent route optimization system that predicts delays at the station level and recommends the most efficient travel routes. It considers total journey time, waiting time, and delay risk to suggest routes that minimize disruptions and improve travel reliability.

We built the system using a data-driven approach on Databricks, leveraging big data tools like Spark and Delta Lake. Historical train and station data were processed to identify delay patterns, and machine learning models were used to predict delays. These predictions were then integrated into a route optimization algorithm to generate smarter travel recommendations.

One of the biggest challenges was handling incomplete and inconsistent railway data. Accurately predicting delays at a station level required careful feature engineering and cleaning of large datasets. Another challenge was balancing multiple factors—time, waiting, and risk—into a single optimization model without oversimplifying the problem.

We successfully built a system that goes beyond traditional route planning by incorporating delay intelligence. Our model can provide practical, real-world route suggestions that reduce the chances of missed connections. Integrating AI with large-scale data processing in a meaningful way was a key achievement

Through this project, we learned how to apply machine learning to real-world transportation problems. We gained experience in handling large datasets, building predictive models, and designing optimization systems. Most importantly, we learned how AI can create tangible impact by solving everyday problems faced by millions of people.

Built With

• along-with-sql-for-querying-structured-data.-the-project-runs-on-databricks
• and-backend-logic
• enabling-scalable-data-processing-using-pyspark.-we-used-libraries-like-pandas
• leveraging-its-lakehouse-architecture-with-delta-lake-for-efficient-and-reliable-data-storage
• machine-learning
• numpy
• python
• railway-running-history