Introducing the cool, quiet city competition: predicting smartwatch-reported heat and noise with digital twin metrics

Author

• Clayton MILLER, Singapore Management UniversityFollow
• Matias QUINTANA
• Mario FREI
• Xuan Yun CHUA
• Chun FU
• Bianca PICCHETTI
• Winston YAP
• Adrian CHONG
• Filip BILJECKI

Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2024

Abstract

The productivity and satisfaction of humans in the built environment is impacted significantly by their exposure to high temperature and various noise sources. This paper outlines the city-scale collection of 12,009 smartwatch-driven micro-survey responses that were collected alongside 2,825,243 physiological and environmental measurements from 106 people using the open-source Cozie-Apple platform combined with geolocation-driven urban digital twin metrics from the Urbanity Python package. This paper introduces a machine learning competition that will be launched for participants to compete in training models on the various contextual data to predict noise distraction and source as well as thermal preference across a diversity of spaces. The winning solutions of this competition will provide evidence of the types of pre-processing, modeling, and ensembling methods that provide the most accurate solutions for this context.

Keywords

Thermal comfort, Acoustic comfort, Machine learning competition

Discipline

Energy Policy | Environmental Design

Research Areas

Integrative Research Areas

Publication

BuildSys '23: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16

First Page

298

Last Page

299

Identifier

10.1145/3600100.3626269

Publisher

ACM

City or Country

New York

Citation

MILLER, Clayton; QUINTANA, Matias; FREI, Mario; CHUA, Xuan Yun; FU, Chun; PICCHETTI, Bianca; YAP, Winston; CHONG, Adrian; and BILJECKI, Filip. Introducing the cool, quiet city competition: predicting smartwatch-reported heat and noise with digital twin metrics. (2024). BuildSys '23: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16. 298-299.
Available at: https://ink.library.smu.edu.sg/cis_research/606

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

https://doi.org/10.1145/3600100.3626269