Assessment of Qatar’s road network under sea-level-rise scenarios using traffic simulation and graph theory

<p dir="ltr">Sea-level rise (SLR) threatens every dimension of <u>sustainable development</u>, testing infrastructure resilience and adaptation. This research develops a multi-stage framework to evaluate <u>road network</u> vulnerability under varying SLR scen...

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Main Author: Mohammad Zaher Serdar (17191381) (author)
Other Authors: Abdel Rahman Marian (22828202) (author), Eyad Masad (14153484) (author)
Published: 2025
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author Mohammad Zaher Serdar (17191381)
author2 Abdel Rahman Marian (22828202)
Eyad Masad (14153484)
author2_role author
author
author_facet Mohammad Zaher Serdar (17191381)
Abdel Rahman Marian (22828202)
Eyad Masad (14153484)
author_role author
dc.creator.none.fl_str_mv Mohammad Zaher Serdar (17191381)
Abdel Rahman Marian (22828202)
Eyad Masad (14153484)
dc.date.none.fl_str_mv 2025-09-10T15:00:00Z
dc.identifier.none.fl_str_mv 10.1016/j.trd.2025.104827
dc.relation.none.fl_str_mv https://figshare.com/articles/journal_contribution/Assessment_of_Qatar_s_road_network_under_sea-level-rise_scenarios_using_traffic_simulation_and_graph_theory/30860354
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Engineering
Environmental engineering
Environmental sciences
Climate change impacts and adaptation
Environmental management
Climate Change Impacts
Sea Level Rise Modelling
Traffic Simulation
Graph Metrics
Resilience and Vulnerability Assessment
Road Network
dc.title.none.fl_str_mv Assessment of Qatar’s road network under sea-level-rise scenarios using traffic simulation and graph theory
dc.type.none.fl_str_mv Text
Journal contribution
info:eu-repo/semantics/publishedVersion
text
contribution to journal
description <p dir="ltr">Sea-level rise (SLR) threatens every dimension of <u>sustainable development</u>, testing infrastructure resilience and adaptation. This research develops a multi-stage framework to evaluate <u>road network</u> vulnerability under varying SLR scenarios through geospatial analysis, traffic simulation, and graph theory. High-resolution maps derived from IPCC-AR6 and NOAA projections show that without adaptation measures, SLR may affect approximately 3–11% of land and 2–17% of roads in Qatar, potentially increasing trip durations up to 15 times. Importantly, the estimated impacts are indicative trends rather than definitive outcomes linked to specific emissions scenarios. In parallel, the paper examines the effectiveness of several graph metrics in evaluating road network performance under SLR-induced disruptions. The goal of this exercise is to identify metrics that strongly correlate with severity levels and simulation outcomes, supporting their utility in resilience assessment. Finally, the paper outlines a practical roadmap to advance SLR risk simulation and support the development of effective adaptation strategies to enhance Qatar’s resilience. The adaptability of the proposed framework and roadmap also enables their application to other geographical contexts, with minimal refinement and appropriate localization. Future research may extend this work by incorporating localized and temporal dynamics and analyzing additional critical infrastructure systems​.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Transportation Research Part D: Transport and Environment<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.trd.2025.104827" target="_blank">https://dx.doi.org/10.1016/j.trd.2025.104827</a></p>
eu_rights_str_mv openAccess
id Manara2_9fddc08bf0f54ea2f4df2b793c21f252
identifier_str_mv 10.1016/j.trd.2025.104827
network_acronym_str Manara2
network_name_str Manara2
oai_identifier_str oai:figshare.com:article/30860354
publishDate 2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
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rights_invalid_str_mv CC BY 4.0
spelling Assessment of Qatar’s road network under sea-level-rise scenarios using traffic simulation and graph theoryMohammad Zaher Serdar (17191381)Abdel Rahman Marian (22828202)Eyad Masad (14153484)EngineeringEnvironmental engineeringEnvironmental sciencesClimate change impacts and adaptationEnvironmental managementClimate Change ImpactsSea Level Rise ModellingTraffic SimulationGraph MetricsResilience and Vulnerability AssessmentRoad Network<p dir="ltr">Sea-level rise (SLR) threatens every dimension of <u>sustainable development</u>, testing infrastructure resilience and adaptation. This research develops a multi-stage framework to evaluate <u>road network</u> vulnerability under varying SLR scenarios through geospatial analysis, traffic simulation, and graph theory. High-resolution maps derived from IPCC-AR6 and NOAA projections show that without adaptation measures, SLR may affect approximately 3–11% of land and 2–17% of roads in Qatar, potentially increasing trip durations up to 15 times. Importantly, the estimated impacts are indicative trends rather than definitive outcomes linked to specific emissions scenarios. In parallel, the paper examines the effectiveness of several graph metrics in evaluating road network performance under SLR-induced disruptions. The goal of this exercise is to identify metrics that strongly correlate with severity levels and simulation outcomes, supporting their utility in resilience assessment. Finally, the paper outlines a practical roadmap to advance SLR risk simulation and support the development of effective adaptation strategies to enhance Qatar’s resilience. The adaptability of the proposed framework and roadmap also enables their application to other geographical contexts, with minimal refinement and appropriate localization. Future research may extend this work by incorporating localized and temporal dynamics and analyzing additional critical infrastructure systems​.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Transportation Research Part D: Transport and Environment<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.trd.2025.104827" target="_blank">https://dx.doi.org/10.1016/j.trd.2025.104827</a></p>2025-09-10T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.trd.2025.104827https://figshare.com/articles/journal_contribution/Assessment_of_Qatar_s_road_network_under_sea-level-rise_scenarios_using_traffic_simulation_and_graph_theory/30860354CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/308603542025-09-10T15:00:00Z
spellingShingle Assessment of Qatar’s road network under sea-level-rise scenarios using traffic simulation and graph theory
Mohammad Zaher Serdar (17191381)
Engineering
Environmental engineering
Environmental sciences
Climate change impacts and adaptation
Environmental management
Climate Change Impacts
Sea Level Rise Modelling
Traffic Simulation
Graph Metrics
Resilience and Vulnerability Assessment
Road Network
status_str publishedVersion
title Assessment of Qatar’s road network under sea-level-rise scenarios using traffic simulation and graph theory
title_full Assessment of Qatar’s road network under sea-level-rise scenarios using traffic simulation and graph theory
title_fullStr Assessment of Qatar’s road network under sea-level-rise scenarios using traffic simulation and graph theory
title_full_unstemmed Assessment of Qatar’s road network under sea-level-rise scenarios using traffic simulation and graph theory
title_short Assessment of Qatar’s road network under sea-level-rise scenarios using traffic simulation and graph theory
title_sort Assessment of Qatar’s road network under sea-level-rise scenarios using traffic simulation and graph theory
topic Engineering
Environmental engineering
Environmental sciences
Climate change impacts and adaptation
Environmental management
Climate Change Impacts
Sea Level Rise Modelling
Traffic Simulation
Graph Metrics
Resilience and Vulnerability Assessment
Road Network