ClimBridge: Surrogate models and optimisation for sustainable transportation

Valizadeh, Nima, Hamed, Naeima, Yao, Fulong, Haggar, Paul Christian ORCID: https://orcid.org/0000-0001-8753-1848, Potoglou, Dimitris ORCID: https://orcid.org/0000-0003-3060-7674, Gillard, Jon, Rana, Omer F. ORCID: https://orcid.org/0000-0003-3597-2646, Vohra, Karn, Bloss, William J. and Sharma, Ashish 2026. ClimBridge: Surrogate models and optimisation for sustainable transportation. Sharma, Ashish, Potoglou, Dimitris, Miranda, Fabio, Radcliffe, Jonathan and Anderies, John M., eds. Clean Energy and Equitable Transportation Solutions, IGI Global, pp. 175-190. (10.4018/979-8-3373-9908-9.ch010)

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Abstract

Cities need computationally efficient, trustworthy ways to evaluate the environmental impact of (cleaner) travel options without running a full climate-and-chemistry model each time. ClimBridge pairs a trusted simulator with a physics-aware surrogate, enabling transport planners to explore multiple “what-if” questions over a significantly shorter time frame than running more complex, computationally intensive models. e describe the trade-offs between the accuracy of model outcomes and computational complexity, helping to address challenges related to uncertainty and novel decision metrics. The role of utilising machine learning to develop surrogate models in this context is highlighted.

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Schools > Geography and Planning (GEOPL) Schools > Computational & Mathematical Sciences Schools > School of Social and Spatial Sciences Schools > Computer Science & Informatics
Publisher:	IGI Global
ISBN:	9798337399089
Last Modified:	07 Sep 2026 15:49
URI:	https://orca.cardiff.ac.uk/id/eprint/188963

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