International Journal For Multidisciplinary Research

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Train Delay Analysis Using Logistic Regression Approach

Author(s) Kanak Mishra, Aaradhya Chaple, Ayush Bhutada, Harnoor Huda, Shreyas Marudwar, Dr. Pradip Selokar
Country India
Abstract Train transportation plays a crucial role in modern society, facilitating the movement of goods and people efficiently. One of the primary challenges faced in railway operations is the occurrence of train delays, which can
result from various factors, such as weather conditions, infrastructure issues, or
operational constraints. Timely detection and prediction of train delays are essential for ensuring a smooth and reliable rail transportation system. This study explores the use of logistic regression as a method for determining train delays.
Logistic regression, traditionally employed for binary classification tasks, is
adapted to model the likelihood of train delays based on a combination of relevant features and historical data. The research begins with a comprehensive collection of historical train data and past delays. This data is preprocessed and used to train the logistic regression model. The model is evaluated and fine-tuned to optimize its predictive performance, with metrics like accuracy, precision, and recall considered to assess its effectiveness. The logistic regression model's output provides a probability score for the likelihood of a train delay, which can be used to prioritize and allocate resources effectively. This predictive approach enables railway operators to make informed decisions and implement strategies to prevent or minimize delays, ultimately leading to improved rail transportation efficiency and customer satisfaction.
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-05
Cite This Train Delay Analysis Using Logistic Regression Approach - Kanak Mishra, Aaradhya Chaple, Ayush Bhutada, Harnoor Huda, Shreyas Marudwar, Dr. Pradip Selokar - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.16468
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.16468
Short DOI https://doi.org/gtp8jm
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