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Enhancing Train Travel Efficiency: Machine Learning- based Train Delay Prediction
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  • Vishnu Prasad L R,
  • Kabilesh S,
  • Madhan Raj K,
  • Sowmia K R
Vishnu Prasad L R
Rajalakshmi Engineering College

Corresponding Author:[email protected]

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Kabilesh S
Rajalakshmi Engineering College
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Madhan Raj K
Rajalakshmi Engineering College
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Sowmia K R
Rajalakshmi Engineering College
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Abstract

In this thorough review paper, Accurate train delay forecasting is crucial for optimizing scheduling efficiency, enhancing passenger experience, and maintaining the overall efficiency of railway systems. Traditional approaches to train delay prediction often rely on simplistic mathematical models, which may not capture the complex temporal and spatial dependencies inherent in rail networks. In this study, we propose the use of Recurrent Neural Networks (RNN) as a powerful alternative for train delay forecasting. RNNs leverage the capabilities of deep learning to effectively model the intricate relationships and dependencies within historical rail data. By combining iterative and convolutional layers, RNNs can capture the complexity of rail systems, enabling more accurate and timely predictions. Our model incorporates various factors such as historicalweather data, station-specific information, and scheduling details to provide comprehensive insights into train delays. The main objective of this study is to compare the performance of RNN-based machine learning methods with traditional approaches for train delay prediction. We analyze a large dataset encompassing different railway types and operating conditions. Through rigorous evaluation of accuracy, precision, and robustness, we demonstrate the superiority of RNN-basedmethods in train delay forecasting. Our experimental results reveal significantimprovements in short-term and long-term delay predictions, showcasing the ability of RNNs to capture time- dependent patterns and complex spatial relationships within rail systems. Furthermore, the integration of complementary data sources enhances the predictive power of the model. In conclusion, this study highlights the potential of RNN-based machine learning methods as a powerful tool for improving the accuracy and reliability of train delay prediction. By harnessing the capabilities of deep learning, we contribute to the optimization of rail operations, benefiting both passengers and rail operators in terms of efficiency and reliability.