Heterogeneous machine learning ensembles for predicting train delays

Al Ghamdi, Mostafa, Parr, Gerard ORCID: https://orcid.org/0000-0002-9365-9132 and Wang, Wenjia (2024) Heterogeneous machine learning ensembles for predicting train delays. IEEE Transactions on Intelligent Transportation Systems, 25 (6). pp. 5138-5153. ISSN 1524-9050

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Abstract

Train delays have been a serious persisting problem in the UK and also many other countries. Due to increasing demand, rail networks are running close to their full capacity. As a consequence, an initial delay can cause many knock-on delays to other trains, and this is the main reason for the overall deterioration in the performance of the rail networks. Therefore, it is really useful to have an AI-based method that can predict delays accurately and reliably, to help train controllers to make and apply alternative plans in time to reduce or prevent further delays, when a delay occurs. However, existing machine learning models are not only inaccurate but more importantly unreliable. In this study, we have proposed a new approach to build heterogeneous ensembles with two novel model selection methods based on accuracy and diversity. We tested our heterogeneous ensembles using the real-world data and the results indicated that they are more accurate and robust than single models and state-of-the-art homogeneous ensembles, e.g. Random Forest and XGBoost. We then verified their performances with an independent dataset from a different train operating company and found that they achieved the consistent and accurate results.

Item Type: Article
Uncontrolled Keywords: train delay prediction,machine learning,ensemble,boosting classifiers,random forest,atmospheric modeling,predictive models,random forest,heterogeneous ensemble,rails,diversity,data models,delays,ensemble learning,mechanical engineering,automotive engineering,computer science applications,4* ,/dk/atira/pure/subjectarea/asjc/2200/2210
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Data Science and Statistics
Faculty of Science > Research Groups > Smart Emerging Technologies
Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 09 Jan 2024 01:35
Last Modified: 13 Jul 2024 01:43
URI: https://ueaeprints.uea.ac.uk/id/eprint/94109
DOI: 10.1109/TITS.2023.3337858

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