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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*,this paper presented a novel ensemble method for predicting train delays and produced much improved accuracy than those of any individual models. this ieee journal has a relatively high impact factor, arguably the best journal on the domain. ,/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 AI 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: | 27 Nov 2024 10:35 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/94109 |
| DOI: | 10.1109/TITS.2023.3337858 |
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