Abstract
Human thermal comfort is linked to multi-sensory attributes and psychological cognition besides the influence of prominent meteorological parameters. To date, even the most advanced thermal comfort models like UTCI consider the interaction between human and external surroundings to be a heat transfer problem applying thermodynamic equations in the derivation behind thermal comfort indices. Machine learning (ML) models offer the advantage of improving the accuracy of predictor variable, but they fail to determine the relationship between the independent variables and data-driven prediction models. Structural Equation Modelling (SEM), an advanced modelling technique, can expose all hidden relationships among variables using latent variables while not being data-driven like ML. This study aimed to develop a structural framework which systematically explains these relationships and quantify the effects of 24 variables on outdoor thermal comfort using SEM due to its ability to represents the response of the dependent variable to a unit change in an explanatory variable. The method employed 635 structured interviews, observations, and wide-ranging micrometeorological measurements conducted concurrently in three Pedestrianized urban alleys in Cairo. The model statistically confirmed the hypothesis that overall thermal comfort in urban spaces is significantly influenced by multiple factors. Of these, background microclimatic conditions had the greatest relevance and certain urban features the least. 1 unit increase microclimate leads to a change in thermal comfort by 0.423 and 0.349 in summer and winter, respectively. In terms of thermal perception, the model shows that individuals are willing to tolerate a wider range if certain urban features are present such as shaded seating opportunities. These outcomes may aid to provide important guidance for urban designers to enhance thermal perception in urban streets.
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