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@article{182428,
        author = {Anas Syed Shoebul Hasan and Sridhar Gummalla and Subramanian K.M},
        title = {Predicting Short-Term Arrival Delays in Freight Rail Operations Using XGBoost and Voting Classifiers},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {2497-2502},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182428},
        abstract = {Despite rail’s growing popularity as a mode of freight transportation due to its role in intermodal transportation and numerous economic and environmental benefits, optimizing all aspects of rail infrastructure use remains a significant challenge. To address this issue, various methods for developing train disruption prediction models have been used. However, these models continue to struggle with accurately predicting short-term arrival delay times, as well as identifying the causes of delays and the expected impact on operations. The lack of information available to operators makes it difficult for them to effectively mitigate the effects of disruptions. The goal of this study is to investigate a set of data-driven models for the short-term prediction of arrival delay time using data and then investigate the effects of the features associated with the arrival delay time. For our dataset, the lightGBM model outperformed other models in predicting the arrival delay time in freight rail operations, with departure delay time, trip distance, and train composition appearing to be the most influential features in predicting the arrival delay time in the short-term. Knowing a train’s arrival delay time allows you to estimate future operational time, providing more support to reduce disruptions and subsequent operational delays via a simple web service.},
        keywords = {Arrival delay prediction, Fright rail operations, LightGBM, Machine Learning, Real-Time Prediction, Transportation Analytics, Voting Classifier, XGBoost},
        month = {July},
        }