Analysis of the impact of climate-driven extreme weather events (EWEs) on the UK train delays: A data-driven BN approach
Analysis of the impact of climate-driven extreme weather events (EWEs) on the UK train delays: A data-driven BN approach
Climate change exacerbates the occurrence of frequent Extreme Weather Events (EWEs), directly disrupting railway operations in numerous countries, notably the United Kingdom. Projections for the UK climate indicate an increase in rainfall intensity, warmer and wetter winters, hotter and drier summers, and more frequent and intense EWEs. Such climatic shifts cause increased weather-related railway delays, which in turn result in significant economic loss. This study develops a new risk model using a data-driven Bayesian Network (BN) to analyse the impact of climate-induced EWEs on UK train delays. The model quantifies the influence of various factors on delays, providing deeper insights into their individual and combined effects. The new model and the findings contribute to the disclosure of 1) the interconnections among the different variables influencing train delays, including the origin and destination of the train and traction type, and 2) the prediction of the quantitative extent to which the variables can jointly lead to train delays of different severity levels, incident reason, the month of occurrence, the responsible operator, and the train schedule type. Critical findings highlight the substantial negative impact of severe flooding on the operational reliability of the UK railway system. An important insight was the significant clustering of delays ranging from 80 to 90 min, particularly on Fridays, suggesting the need for targeted operational interventions in specific regions. Additionally, the analysis identified December as the most hazardous month for train delays due to EWEs, with January and July also showing elevated risk levels. This paper offers valuable insights for transport planners, enabling them to prioritise climate-related scenarios causing the most severe train delays and to formulate the associated adaptation measures and strategies rationally.
Bayesian network, Climate change, Railway delay, Risk analysis, Risk assessment
Kamalian, Leila
f52fd971-e68d-43f3-8d10-87e723dfacec
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Poo, Mark Ching-Pong
d59c3a1b-9082-4fa8-8afc-1fe493f7736b
Bras, Ana
b5eb871d-f4e8-43e7-9171-2331a601c314
Ng, Adolf K.Y.
cd54c69b-6b68-40a8-b6ba-e9281817749e
Yang, Zaili
82d4eebc-4532-4343-8555-35169e79bb6d
30 April 2025
Kamalian, Leila
f52fd971-e68d-43f3-8d10-87e723dfacec
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Poo, Mark Ching-Pong
d59c3a1b-9082-4fa8-8afc-1fe493f7736b
Bras, Ana
b5eb871d-f4e8-43e7-9171-2331a601c314
Ng, Adolf K.Y.
cd54c69b-6b68-40a8-b6ba-e9281817749e
Yang, Zaili
82d4eebc-4532-4343-8555-35169e79bb6d
Kamalian, Leila, Li, Huanhuan, Poo, Mark Ching-Pong, Bras, Ana, Ng, Adolf K.Y. and Yang, Zaili
(2025)
Analysis of the impact of climate-driven extreme weather events (EWEs) on the UK train delays: A data-driven BN approach.
Reliability Engineering & System Safety, 262, [111189].
(doi:10.1016/j.ress.2025.111189).
Abstract
Climate change exacerbates the occurrence of frequent Extreme Weather Events (EWEs), directly disrupting railway operations in numerous countries, notably the United Kingdom. Projections for the UK climate indicate an increase in rainfall intensity, warmer and wetter winters, hotter and drier summers, and more frequent and intense EWEs. Such climatic shifts cause increased weather-related railway delays, which in turn result in significant economic loss. This study develops a new risk model using a data-driven Bayesian Network (BN) to analyse the impact of climate-induced EWEs on UK train delays. The model quantifies the influence of various factors on delays, providing deeper insights into their individual and combined effects. The new model and the findings contribute to the disclosure of 1) the interconnections among the different variables influencing train delays, including the origin and destination of the train and traction type, and 2) the prediction of the quantitative extent to which the variables can jointly lead to train delays of different severity levels, incident reason, the month of occurrence, the responsible operator, and the train schedule type. Critical findings highlight the substantial negative impact of severe flooding on the operational reliability of the UK railway system. An important insight was the significant clustering of delays ranging from 80 to 90 min, particularly on Fridays, suggesting the need for targeted operational interventions in specific regions. Additionally, the analysis identified December as the most hazardous month for train delays due to EWEs, with January and July also showing elevated risk levels. This paper offers valuable insights for transport planners, enabling them to prioritise climate-related scenarios causing the most severe train delays and to formulate the associated adaptation measures and strategies rationally.
Text
1-s2.0-S0951832025003904-main
- Version of Record
More information
Accepted/In Press date: 23 April 2025
e-pub ahead of print date: 24 April 2025
Published date: 30 April 2025
Keywords:
Bayesian network, Climate change, Railway delay, Risk analysis, Risk assessment
Identifiers
Local EPrints ID: 503415
URI: http://eprints.soton.ac.uk/id/eprint/503415
ISSN: 0951-8320
PURE UUID: aca634ea-16c3-4d39-9590-db2fd3480e7e
Catalogue record
Date deposited: 30 Jul 2025 17:02
Last modified: 22 Aug 2025 02:49
Export record
Altmetrics
Contributors
Author:
Leila Kamalian
Author:
Huanhuan Li
Author:
Mark Ching-Pong Poo
Author:
Ana Bras
Author:
Adolf K.Y. Ng
Author:
Zaili Yang
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics