ItemConference Paper

Predictive Analytics for Train Timeliness Using Long Short Term Memory and Machine Learning Techniques

Title
Predictive Analytics for Train Timeliness Using Long Short Term Memory and Machine Learning Techniques
Creator
Johny, Akshay; Senthil Vadivu, M.; Jeevanand, E.S.
Description
Train delays are one of the most persistent issue faced by Indian Railways and it has still been the issue with all the modern infrastructure and increasing travel demands. Traditional methods mainly depend on historical averages and simple modelling which fail to capture complex patterns in delays caused. This research aims to build machine learning and neural network models to analyse historical data from past train journeys and make predictions for future train journeys. Machine learning models include Decision Trees, XGBoost, Random Forest, Extra Trees and a neural network model LSTM to predict the delay for a particular train on a given day based on the previous running status. The highest accuracy of 94.02% was found using LSTM model and a lowest of 72.65% for Decision Tree Regressor algorithm. 2025 IEEE.
Source
4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.1049-1054
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Decision Trees; Machine Learning; RNN; Train Delay; XGBoost
Coverage
Johny A., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, India; Senthil Vadivu M., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, India; Jeevanand E.S., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833156587-9;
Format
online
Language
English
Type
Conference paper

Citation

Johny, Akshay; Senthil Vadivu, M.; Jeevanand, E.S., “Predictive Analytics for Train Timeliness Using Long Short Term Memory and Machine Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25886.

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