Improving On-Time Performance: Predicting Train Delays with Machine Learning Techniques

The paper discusses the Indian railway system, the fourth-largest rail network in the world, and identifies a prevalent issue experienced by railway passengers—train delays. The primary focus of this research is to leverage advanced machine learning techniques to address this challenge, with a specific emphasis on predicting train delays using a Random Forest Classifier model. By developing this predictive model, the study offers significant potential benefits for both passengers and railway authorities. Passengers will be able to plan their journeys more effectively, taking into account potential delays, thereby enhancing their overall travel experience. Meanwhile, for the Indian railways, this research provides a valuable tool to identify bottlenecks and other contributing factors that lead to delays for specific trains. This research aims to significantly improve the performance and efficiency of the Indian railway system. Through the integration of cutting-edge machine learning methods with comprehensive real-world railway data, the paper aims to create a solution that not only enhances passenger satisfaction but also enables railway authorities to optimize operations and elevate service quality. The paper's multidimensional approach endeavors to pave the way for a more dependable and efficient railway network. By addressing the challenge of train delays, this work aims to create a positive impact on all stakeholders involved, fostering smoother journeys and improved services for the entire ecosystem.