Paper Title
Data Analytics Approach for Train Time Table Performance Measure Using Automatic Train Supervision Data
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Registration ID: IJNRD_191532
Published ID: IJNRD2305559
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Keywords
RANDOM FOREST, LOGISTICS REGRESSION.
Abstract
ABSTRACT: Passenger train delay significantly influences riders to choose rail transport as their mode choice. This paper proposes real-time passenger train delay prediction (PTDP) models using machine learning techniques. In this article, the impact on PTPD models using real-time with real-time-based data-frame structure (RT-DFS) and history-based data-frame structure (RWH-DFS) is investigated. The results show that PTDP models using MLP with RWH-DFS outperformed all other models. The influence of external variables such as historical delay profiles (HDPD), ridership and population, day of the week, geography and weather information on real-time PTPD models is further analyzed and discussed. This system is very important for improving airport traffic efficiency to improve accuracy in predicting train arrival delay time. In our process, we need to take the input as a time series dataset. After that, machine learning algorithms like logistic regression and random forest should be implemented. Experimental results show that each algorithm has accuracy and error values. The model has good predictive accuracy and can track the trends of many delay indicators well.
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How To Cite (APA)
RAJALAKSHMI K (May-2023). Data Analytics Approach for Train Time Table Performance Measure Using Automatic Train Supervision Data. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), f341-f346. https://ijnrd.org/papers/IJNRD2305559.pdf
Issue
Volume 8 Issue 5, May-2023
Pages : f341-f346
Other Publication Details
Paper Reg. ID: IJNRD_191532
Published Paper Id: IJNRD2305559
Research Area: Science
Author Type: Indian Author
Country: Kovilpatti/Tuticorin, Tamilnadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2305559.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305559
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