Pedestrian Wind Factor Estimation in Complex Urban Environments

Sarah Mokhtar, Matt Beveridge, Yumeng Cao, Iddo Drori
Proceedings of The 13th Asian Conference on Machine Learning, PMLR 157:486-501, 2021.

Abstract

Urban planners and policy makers face the challenge of creating livable and enjoyable citiesfor larger populations in much denser urban conditions. While the urban microclimate holdsa key role in defining the quality of urban spaces today and in the future, the integrationof wind microclimate assessment in early urban design and planning processes remains achallenge due to the complexity and high computational expense of computational fluiddynamics (CFD) simulations. This work develops a data-driven workflow for real-timepedestrian wind comfort estimation in complex urban environments which may enabledesigners, policy makers and city residents to make informed decisions about mobility,health, and energy choices. We use a conditional generative adversarial network (cGAN)architecture to reduce the computational computation while maintaining high confidencelevels and interpretability, adequate representation of urban complexity, and suitability forpedestrian comfort estimation. We demonstrate high quality wind field approximationswhile reducing computation time from days to seconds.

Cite this Paper

BibTeX
@InProceedings{pmlr-v157-mokhtar21a, title = {Pedestrian Wind Factor Estimation in Complex Urban Environments}, author = {Mokhtar, Sarah and Beveridge, Matt and Cao, Yumeng and Drori, Iddo}, booktitle = {Proceedings of The 13th Asian Conference on Machine Learning}, pages = {486--501}, year = {2021}, editor = {Balasubramanian, Vineeth N. and Tsang, Ivor}, volume = {157}, series = {Proceedings of Machine Learning Research}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v157/mokhtar21a/mokhtar21a.pdf}, url = {https://proceedings.mlr.press/v157/mokhtar21a.html}, abstract = {Urban planners and policy makers face the challenge of creating livable and enjoyable citiesfor larger populations in much denser urban conditions. While the urban microclimate holdsa key role in defining the quality of urban spaces today and in the future, the integrationof wind microclimate assessment in early urban design and planning processes remains achallenge due to the complexity and high computational expense of computational fluiddynamics (CFD) simulations. This work develops a data-driven workflow for real-timepedestrian wind comfort estimation in complex urban environments which may enabledesigners, policy makers and city residents to make informed decisions about mobility,health, and energy choices. We use a conditional generative adversarial network (cGAN)architecture to reduce the computational computation while maintaining high confidencelevels and interpretability, adequate representation of urban complexity, and suitability forpedestrian comfort estimation. We demonstrate high quality wind field approximationswhile reducing computation time from days to seconds.} }
Endnote
%0 Conference Paper %T Pedestrian Wind Factor Estimation in Complex Urban Environments %A Sarah Mokhtar %A Matt Beveridge %A Yumeng Cao %A Iddo Drori %B Proceedings of The 13th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Vineeth N. Balasubramanian %E Ivor Tsang %F pmlr-v157-mokhtar21a %I PMLR %P 486--501 %U https://proceedings.mlr.press/v157/mokhtar21a.html %V 157 %X Urban planners and policy makers face the challenge of creating livable and enjoyable citiesfor larger populations in much denser urban conditions. While the urban microclimate holdsa key role in defining the quality of urban spaces today and in the future, the integrationof wind microclimate assessment in early urban design and planning processes remains achallenge due to the complexity and high computational expense of computational fluiddynamics (CFD) simulations. This work develops a data-driven workflow for real-timepedestrian wind comfort estimation in complex urban environments which may enabledesigners, policy makers and city residents to make informed decisions about mobility,health, and energy choices. We use a conditional generative adversarial network (cGAN)architecture to reduce the computational computation while maintaining high confidencelevels and interpretability, adequate representation of urban complexity, and suitability forpedestrian comfort estimation. We demonstrate high quality wind field approximationswhile reducing computation time from days to seconds.
APA
Mokhtar, S., Beveridge, M., Cao, Y. & Drori, I.. (2021). Pedestrian Wind Factor Estimation in Complex Urban Environments. Proceedings of The 13th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 157:486-501 Available from https://proceedings.mlr.press/v157/mokhtar21a.html.

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