Assessing transport resilience to heavy rainfall: An integrated modelling framework with metrics
Abstract
Road passenger transport faces acute disruption risks from flash flooding from heavy rainfall. To capture system complexity and dynamics for assessing its flood resilience, this study (a) proposes performance metrics to quantify disruption impacts from physical, operational, environmental, social, and economic dimensions, and (b) develops an integrated modelling framework to assess selected metrics by combining rainfall modelling, surface water flood modelling, and agent-based transport modelling to simulate asset-level disruptions and user behavioural responses. This methodology is demonstrated through a 1-in-30-year rainfall event in Tyne and Wear, North East England. Three behavioural response scenarios to rainfall are simulated, including a worst-case scenario, an ideal-response scenario, and an early-warning scenario. Results highlight the potential of behavioural adaptation in enhancing road flood resilience by reducing journey delays, cancellations, and economic losses. The framework and multidimensional performance metrics offer a transferable approach for stress-testing rainfall scenarios and evaluating interventions, supporting evidence-based climate-resilient transport planning.
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BibTeX
@misc{bi2026assessing,
title = {{Assessing transport resilience to heavy rainfall: An integrated modelling framework with metrics}},
author = {Wei Bi and David Alvarez Castro and Yimeng Liu and Abdullah Kahraman and Alistair Ford and Roberto Palacin and Kristen MacAskill},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.16963},
eprint = {2609.16963},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.16963},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Assessing transport resilience to heavy rainfall: An integrated modelling framework with metrics
AU - Wei Bi
AU - David Alvarez Castro
AU - Yimeng Liu
AU - Abdullah Kahraman
AU - Alistair Ford
AU - Roberto Palacin
AU - Kristen MacAskill
PY - 2026
DA - 2026/09/15
PB - arXiv
DO - 10.48550/arxiv.2609.16963
UR - https://arxiv.org/abs/2609.16963
AB - Road passenger transport faces acute disruption risks from flash flooding from heavy rainfall. To capture system complexity and dynamics for assessing its flood resilience, this study (a) proposes performance metrics to quantify disruption impacts from physical, operational, environmental, social, and economic dimensions, and (b) develops an integrated modelling framework to assess selected metrics by combining rainfall modelling, surface water flood modelling, and agent-based transport modelling to simulate asset-level disruptions and user behavioural responses. This methodology is demonstrated through a 1-in-30-year rainfall event in Tyne and Wear, North East England. Three behavioural response scenarios to rainfall are simulated, including a worst-case scenario, an ideal-response scenario, and an early-warning scenario. Results highlight the potential of behavioural adaptation in enhancing road flood resilience by reducing journey delays, cancellations, and economic losses. The framework and multidimensional performance metrics offer a transferable approach for stress-testing rainfall scenarios and evaluating interventions, supporting evidence-based climate-resilient transport planning.
ER -