Data-driven Train Delay Prediction
(2024)- Abstract
- In Sweden, extensive use and the inherent heterogeneity of train traffic have significantly increased the sensitivity of the train system so that the delay of one train can easily propagate to others. Repeated experiences with train delays can lead passengers to perceive that railway transportation is unreliable. Despite being considered a green mode of transport, this environmental benefit truly comes into effect when passenger volumes are sufficiently high. Therefore, minimising train delays becomes crucial to promote a modal shift from private vehicles to railways.
The advent of advanced technologies facilitating the collection and storage of extensive train operation data has paved the way for addressing train delay issues from a... (More) - In Sweden, extensive use and the inherent heterogeneity of train traffic have significantly increased the sensitivity of the train system so that the delay of one train can easily propagate to others. Repeated experiences with train delays can lead passengers to perceive that railway transportation is unreliable. Despite being considered a green mode of transport, this environmental benefit truly comes into effect when passenger volumes are sufficiently high. Therefore, minimising train delays becomes crucial to promote a modal shift from private vehicles to railways.
The advent of advanced technologies facilitating the collection and storage of extensive train operation data has paved the way for addressing train delay issues from a data-driven perspective, thus leading to a predominant focus on train delay prediction research. To develop theoretical and practical knowledge for the continuous advancement of decision support tools, this thesis aims to explore and understand data-driven train delay prediction. The thesis is grounded in the findings of six papers. Paper 1 systematically reviews existing literature on data-driven approaches for predicting train delays, captures commonly adopted technical solutions, and identifies weaknesses in current models. It suggests promising directions for future research in this area while highlighting under-researched prediction issues. To ascertain useful input variables, Papers 2 and 3 employ statistical regression to quantify the relationship between various explanatory variables and train delays. Papers 4 and 5 address the development of robust data-driven train delay prediction models, introducing dynamic multi-output models capable of continuously predicting train arrival delays for multiple downstream stations at arbitrary prediction times. To enhance performance, the studies further introduce error adjustment strategies that continuously correct predictions based on observed train traffic information. To ensure real-world effectiveness, Paper 6 seeks to construct an evaluation framework for a thorough assessment of train delay prediction models.
The main contribution of the thesis is twofold. Firstly, it sheds light on the current practices in data-driven train delay prediction studies, synthesising progress in various aspects of model development and highlighting the limitations of existing modelling techniques. Secondly, the thesis introduces innovative approaches to enhance model performance. For example, it identifies limitations in current evaluation processes and introduces an evaluation framework to address these gaps. Recognizing the limitations of the current focus on one-step-ahead prediction for practical application, the thesis introduces a dynamic multi-output modelling framework that generates predictions for all downstream stations at arbitrary times. Overall, the thesis helps to bring greater transparency to this growing field of research, with the ultimate goal of accelerating the adoption of data-driven approaches in the railway research community.
(Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/913e4821-55bf-49c5-ba29-e2877df4fa1a
- author
- Tiong, Kah Yong LU
- supervisor
- opponent
-
- Prof. Liu, Ronghui, University of Leeds, UK.
- organization
- publishing date
- 2024-05-08
- type
- Thesis
- publication status
- published
- subject
- keywords
- Machine learning, Predictive models, Data-driven, Railways, Train Delays
- pages
- 112 pages
- publisher
- Lund University Faculty of Engineering, Technology and Society, Transport and Roads, Lund, Sweden
- defense location
- Lecture Hall V:A, building V, John Ericssons väg 1, Faculty of Engineering LTH, Lund University, Lund.
- defense date
- 2024-05-08 14:00:00
- ISBN
- 978-91-8039-971-5
- 978-91-8039-970-8
- project
- Utvärdering av ankomstprognoser för tåg
- language
- English
- LU publication?
- yes
- id
- 913e4821-55bf-49c5-ba29-e2877df4fa1a
- date added to LUP
- 2024-03-14 07:11:25
- date last changed
- 2024-04-15 15:32:38
@phdthesis{913e4821-55bf-49c5-ba29-e2877df4fa1a,
abstract = {{In Sweden, extensive use and the inherent heterogeneity of train traffic have significantly increased the sensitivity of the train system so that the delay of one train can easily propagate to others. Repeated experiences with train delays can lead passengers to perceive that railway transportation is unreliable. Despite being considered a green mode of transport, this environmental benefit truly comes into effect when passenger volumes are sufficiently high. Therefore, minimising train delays becomes crucial to promote a modal shift from private vehicles to railways.<br/>The advent of advanced technologies facilitating the collection and storage of extensive train operation data has paved the way for addressing train delay issues from a data-driven perspective, thus leading to a predominant focus on train delay prediction research. To develop theoretical and practical knowledge for the continuous advancement of decision support tools, this thesis aims to explore and understand data-driven train delay prediction. The thesis is grounded in the findings of six papers. Paper 1 systematically reviews existing literature on data-driven approaches for predicting train delays, captures commonly adopted technical solutions, and identifies weaknesses in current models. It suggests promising directions for future research in this area while highlighting under-researched prediction issues. To ascertain useful input variables, Papers 2 and 3 employ statistical regression to quantify the relationship between various explanatory variables and train delays. Papers 4 and 5 address the development of robust data-driven train delay prediction models, introducing dynamic multi-output models capable of continuously predicting train arrival delays for multiple downstream stations at arbitrary prediction times. To enhance performance, the studies further introduce error adjustment strategies that continuously correct predictions based on observed train traffic information. To ensure real-world effectiveness, Paper 6 seeks to construct an evaluation framework for a thorough assessment of train delay prediction models. <br/>The main contribution of the thesis is twofold. Firstly, it sheds light on the current practices in data-driven train delay prediction studies, synthesising progress in various aspects of model development and highlighting the limitations of existing modelling techniques. Secondly, the thesis introduces innovative approaches to enhance model performance. For example, it identifies limitations in current evaluation processes and introduces an evaluation framework to address these gaps. Recognizing the limitations of the current focus on one-step-ahead prediction for practical application, the thesis introduces a dynamic multi-output modelling framework that generates predictions for all downstream stations at arbitrary times. Overall, the thesis helps to bring greater transparency to this growing field of research, with the ultimate goal of accelerating the adoption of data-driven approaches in the railway research community.<br/>}},
author = {{Tiong, Kah Yong}},
isbn = {{978-91-8039-971-5}},
keywords = {{Machine learning; Predictive models; Data-driven; Railways; Train Delays}},
language = {{eng}},
month = {{05}},
publisher = {{Lund University Faculty of Engineering, Technology and Society, Transport and Roads, Lund, Sweden}},
school = {{Lund University}},
title = {{Data-driven Train Delay Prediction}},
url = {{https://lup.lub.lu.se/search/files/179928525/Tiong_Kah_Yong_-_WEBB.pdf}},
year = {{2024}},
}