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On the analytical study of the service quality of Indian Railways under soft-computing paradigm

    Saibal Majumder Affiliation
    ; Aarti Singh Affiliation
    ; Anupama Singh Affiliation
    ; Mykola Karpenko Affiliation
    ; Haresh Kumar Sharma Affiliation
    ; Somnath Mukhopadhyay Affiliation

Abstract

Indian Railway Catering and Tourism Corporation (IRCTC) is among the busiest railways reservation systems since the Indian Railways (IR) is the vital and economical mode of transportation in India. Hence, rating of the trains seems to be critical aspect for selecting an appropriate train for travelling. In this study, we have considered 7 vital attributes of 500 popular trains and rate their performance based on 7 important related attributes. For this purpose, we have employed 2 different approaches to analyse of the train attributes, which eventually contribute to the overall performance of the trains. Here, we have developed a rule based rough set decision support system to analyse the criticality of the train attributes while rating the train performance. Furthermore, we have also used 3 Machine Learning (ML) model estimators: Extra Trees Classifier (ETC), Support Vector Machine Classifier (SVMC) and Multinomial Naive Bayes Classifier (MNBC) and perform their comparative analysis with respect to 7 performance metrics while predicting the overall train rating based.

Keyword : rough set theory, extra trees classifier, support vector machine classifier, multinomial naive Bayes classifier, performance metrics

How to Cite
Majumder, S., Singh, A., Singh, A., Karpenko, M., Sharma, H. K., & Mukhopadhyay, S. (2024). On the analytical study of the service quality of Indian Railways under soft-computing paradigm. Transport, 39(1), 54–63. https://doi.org/10.3846/transport.2024.21385
Published in Issue
Apr 26, 2024
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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