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Cited by 33Urban Heat Island MitigationClimate Change and Health ImpactsSmart Cities and Technologies

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Assessing and forecasting collective urban heat exposure with smart city digital twins

Open access
Pan, Xiyu, Mavrokapnidis, Dimitris, Ly, Hoang T., Mohammadi, Neda, Taylor, John E.·2024·Scientific Reports
DOI 10.1038/s41598-024-59228-8PMC PMC11053083
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Abstract

Due to population growth, climate change, and the urban heat island effect, heat exposure is becoming an important issue faced by urban built environments. Heat exposure assessment is a prerequisite for mitigation measures to reduce the impact of heat exposure. However, there is limited research on urban heat exposure assessment approaches that provides fine-scale spatiotemporal heat exposure information, integrated with meteorological status and human collective exposure as they move about in cities, to enable proactive heat exposure mitigation measures. Smart city digital twins (SCDTs) provide a new potential avenue for addressing this gap, enabling fine spatiotemporal scales, human-infrastructure interaction modeling, and predictive and decision support capabilities. This study aims to develop and test an SCDT for collective urban heat exposure assessment and forecasting. Meteorological sensors and computer vision techniques were implemented in Columbus, Georgia, to acquire temperature, humidity, and passersby count data. These data were then integrated into a collective temperature humidity index. A time-series prediction model and a crowd simulation were employed to predict future short-term heat exposures based on the data accumulated by this SCDT and to support heat exposure mitigation efforts. The results demonstrate the potential of SCDT to enhance public safety by providing city officials with a tool for discovering, predicting, and, ultimately, mitigating community exposure to extreme heat.

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Crossref · 10.1038/s41598-024-59228-8

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Figures

Figure 1 The framework of the collective urban heat exposure assessment SCDT.
Figure 1 The framework of the collective urban heat exposure assessment SCDT.CC BY · PubMed Central
Figure 2 Selected case study sites, intersections of Broadway and the 10th Street and 11th Street, Columbus, GA.
Figure 2 Selected case study sites, intersections of Broadway and the 10th Street and 11th Street, Columbus, GA.CC BY · PubMed Central
Figure 3 THI values between August 16th–22nd, 2020 for the two intersections in the case study.
Figure 3 THI values between August 16th–22nd, 2020 for the two intersections in the case study.CC BY · PubMed Central
Figure 4 Street videos collected by cameras undergo passersby analysis by the YOLO and Deep SORT algorithm.
Figure 4 Street videos collected by cameras undergo passersby analysis by the YOLO and Deep SORT algorithm.CC BY · PubMed Central
Figure 5 Passersby count per four hours between Aug 16th–22nd, 2020 for the two intersections in the case study.
Figure 5 Passersby count per four hours between Aug 16th–22nd, 2020 for the two intersections in the case study.CC BY · PubMed Central
Figure 6 CTHI between August 16th–22nd, 2020 for the two intersections in the case study.
Figure 6 CTHI between August 16th–22nd, 2020 for the two intersections in the case study.CC BY · PubMed Central
Figure 7 Predictions of the THI on August 22nd at the 10th and 11th Street intersections. The SARIMA model for the 10th Street intersection was defined by (2, 0, 2)(4, 0, 1)24, and the SARIMA for 11th Street intersection was defined by (3, 1, 0)(5, 0, 0)24. The R2 value in the figure indicates the prediction accuracy for August 22nd.
Figure 7 Predictions of the THI on August 22nd at the 10th and 11th Street intersections. The SARIMA model for the 10th Street intersection was defined by (2, 0, 2)(4, 0, 1)24, and the SARIMA for 11th Street intersection was defined by (3, 1, 0)(5, 0, 0)24. The R2 value in the figure indicates the prediction accuracy for August 22nd.CC BY · PubMed Central
Figure 8 Predictions of the passersby count on August 22nd at the 10th and 11th Street intersections. The SARIMA model for 10th Street was defined by (4, 1, 0)(3, 0, 1)24, and the SARIMA for 11th Street was defined by (4, 2, 3)(2, 1, 1)24. The R2 value in the figure indicates the prediction accuracy for August 22nd.
Figure 8 Predictions of the passersby count on August 22nd at the 10th and 11th Street intersections. The SARIMA model for 10th Street was defined by (4, 1, 0)(3, 0, 1)24, and the SARIMA for 11th Street was defined by (4, 2, 3)(2, 1, 1)24. The R2 value in the figure indicates the prediction accuracy for August 22nd.CC BY · PubMed Central
Figure 9 Agent-based simulation settings for predicting the spatiotemporal distribution of event participants.
Figure 9 Agent-based simulation settings for predicting the spatiotemporal distribution of event participants.CC BY · PubMed Central
Figure 10 Prediction of the CTHI at the 10th Street intersection, the 11th Street intersection, and the updated CTHI by using the results from the simulation at the 11th Street intersection.
Figure 10 Prediction of the CTHI at the 10th Street intersection, the 11th Street intersection, and the updated CTHI by using the results from the simulation at the 11th Street intersection.CC BY · PubMed Central

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Citations

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Metadata

Authors
Pan, X., Mavrokapnidis, D., Ly, H. T., Mohammadi, N., & Taylor, J. E.
Affiliations
  • Georgia Institute of Technology (US)
  • University College London (GB)
Corresponding author
Taylor, John E.
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2024-04-26
Volume
14
Issue
1
Pages
9653-9653
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-024-59228-8
PMC
PMC11053083
OpenAlex
W4395676953
Cited by
33
References
5
Citations by year
2026: 17 · 2025: 11 · 2024: 5
FWCI
1.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • SSRN Electronic Journal (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
National Science Foundation (1837021)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
  • Social Sciences › Urban Studies › Smart Cities and Technologies
Keywords
urban heat island, crowd simulation, weather sensors, computer vision, public safety, Columbus, Georgia

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