Paper Title

Data Analytics Approach for Train Time Table Performance Measure Using Automatic Train Supervision Data

Article Identifiers

Registration ID: IJNRD_191532

Published ID: IJNRD2305559

DOI: Click Here to Get

Authors

RAJALAKSHMI K

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.

How To Cite

"Data Analytics Approach for Train Time Table Performance Measure Using Automatic Train Supervision Data", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 5, page no.f341-f346, May-2023, Available :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

Downloads: 000121166

Research Area: Science

Country: Kovilpatti/Tuticorin, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305559.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305559

About Publisher

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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Call For Paper - Volume 10 | Issue 8 | August 2025

IJNRD is Scholarly open access journals, Peer-reviewed, and Refereed Journals, High Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool), Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) with Open-Access Publications.

INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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Paper Submission Open For: August 2025

Current Issue: Volume 10 | Issue 8

Last Date for Paper Submission: Till 31-Aug-2025

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Frequency: Monthly (12 issue Annually).

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