Railway stations are complex environments in which the risk of accidents is a continuous problem because of the variables such as equipment failure, human mistake, and external uncertain conditions. This outlines a machine learning-based solution that can predict what kind of accident will happen at a railway station using historical information and corresponding input features. Rather than simply detecting the occurrence of risk, the system classifies the nature of possible accidents e.g., derailments, collisions, or technical failure so more specific preventive measures can be taken. There are several classification algorithms, such as Decision Trees, Support Vector Machine, and Logistic Regression, which are trained and tested to identify the most precise model for this purpose. The strategy is based on structured data analysis that converts historical records of accidents into significant patterns that are capable of predicting the nature of the incident expected to happen under the same circumstances in the future. By comparing different models and examining their performance statistics, this picks the best predictive approach. To improve prediction accuracy, a Voting Classifier is utilized. The end objective is to enable proactive safety measures at railway stations by providing a tool that not just alerts of potential hazard but also indicates the type of accident to alert the authorities to implement timely, and occasion-specific measures of safety.


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    Titel :

    Proactive Railway Safety: ML-based Risk Prediction


    Beteiligte:
    Kumar Rapolu, Praveen (Autor:in) / Ramya, Pannala (Autor:in) / Teja, G.Sai (Autor:in) / Krishna, V.Rama (Autor:in)


    Erscheinungsdatum :

    11.06.2025


    Format / Umfang :

    668327 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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