Developing a 3D City Digital Twin: Enhancing Walkability through a Green Pedestrian Network (GPN) in the City of Imola, Italy

Predominantly, dense historical cities face insufficient pedestrian-level greenery in the urban spaces. The lack of greenery impacts the human thermal comfort on the walking paths, which contributes to a considerable reduction in pedestrian flow rate. This study aims at developing a model to assess pedestrian-level thermal comfort in city environments and then evaluate the feasibility of creating a green pedestrian network (GPN). Imola, as a historical city in Italy with a compact urban pattern, is selected as the case study of this paper. To accomplish this, a three-dimensional digital twin at city scale is developed for the recognition of real-time shade patterns and for designing a GPN in this city. The 3D model of the proposed digital twin is developed in the Rhinoceros platform, and the physiological equivalence temperature (PET) is simulated through EnergyPlus, Honeybee, and Ladybug components in grasshopper. This study provides the city with a digital twin that is capable of examining pedestrian-level thermal comfort for designing a GPN based on real-time PET in the compact urban morphology of Imola. The PET model indicates that during the hottest hour of the 25th of June, pedestrians in open spaces can experience 3 °C more than on narrow shaded streets. The results are validated based on in situ datasets that prove the reliability of the developed digital twin for the GPN. It provides urban planners and policy makers with a precise and useful methodology for simulating the effects of pedestrian-level urban greenery on human thermal comfort and also guarantees the functionality of policies in different urban settings.

[1]  C. Chau,et al.  Analyzing the influence of urban morphological features on pedestrian thermal comfort , 2022, Urban Climate.

[2]  E. Ng,et al.  Comparing different recalibrated methods for estimating mean radiant temperature in outdoor environment , 2022, Building and Environment.

[3]  J. Weng,et al.  Effects of Bottom-Overhead Design Variables on Pedestrian-Level Thermal Comfort during Summertime in Different High-Rise Residential Buildings: A Case Study in Chongqing, China , 2022, Buildings.

[4]  C. Ren,et al.  Investigating pedestrian-level greenery in urban forms in a high-density city for urban planning , 2022, Sustainable Cities and Society.

[5]  Pir Mohammad,et al.  Evaluating the role of the albedo of material and vegetation scenarios along the urban street canyon for improving pedestrian thermal comfort outdoors , 2021, Urban Climate.

[6]  F. D. Luca,et al.  The role of building morphology on pedestrian level comfort in Northern climate , 2021, Journal of Physics: Conference Series.

[7]  S. Tsoka,et al.  Climate change and built environment - the role of urban greenery as a mitigation strategy in Greek urban areas , 2021, IOP Conference Series: Earth and Environmental Science.

[8]  A. Barbaresi,et al.  Narrowing uncertainties in forecasting urban building energy demand through an optimal archetyping method , 2021 .

[9]  H. Simon,et al.  Advances in Simulating Radiative Transfer in Complex Environments , 2021, Applied Sciences.

[10]  Marcel Antal,et al.  An Overview of Digital Twins Application Domains in Smart Energy Grid , 2021, ArXiv.

[11]  Patrizia Tassinari,et al.  Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis , 2020 .

[12]  E. S. Krayenhoff,et al.  A multi-layer urban canopy meteorological model with trees (BEP-Tree): Street tree impacts on pedestrian-level climate , 2020 .

[13]  M. Gholami,et al.  A Comparison of Energy and Thermal Performance of Rooftop Greenhouses and Green Roofs in Mediterranean Climate: A Hygrothermal Assessment in WUFI , 2020 .

[14]  E. S. Krayenhoff,et al.  COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees , 2019, Geoscientific Model Development.

[15]  Tzu-Ping Lin,et al.  Outdoor human thermal perception in various climates: A comprehensive review of approaches, methods and quantification. , 2018, The Science of the total environment.

[16]  M. A. Hermida,et al.  Effects of thermophysiological and non-thermal factors on outdoor thermal perceptions: The Tomebamba Riverbanks case , 2018, Building and Environment.

[17]  M. Taleghani.  The impact of increasing urban surface albedo on outdoor summer thermal comfort within a university campus , 2018, Urban Climate.

[18]  Jonas Allegrini,et al.  Parametric study of the influence of environmental factors and tree properties on the transpirative cooling effect of trees , 2018 .

[19]  Jean-Louis Scartezzini,et al.  An overview of simulation tools for predicting the mean radiant temperature in an outdoor space , 2017 .

[20]  L. Norford,et al.  Wind, Sun, Surface Temperature, and Heat Island: Critical Variables for High-Resolution Outdoor Thermal Comfort , 2017, Building Simulation Conference Proceedings.

[21]  V. Masson,et al.  Evaluating the impacts of greening scenarios on thermal comfort and energy and water consumptions for adapting Paris city to climate change , 2017 .

[22]  O. Bastian,et al.  Using the Concepts of Green Infrastructure and Ecosystem Services to Specify Leitbilder for Compact and Green Cities—The Example of the Landscape Plan of Dresden (Germany) , 2017 .

[23]  Nigel J. Tapper,et al.  The influence of increasing tree cover on mean radiant temperature across a mixed development suburb in Adelaide, Australia , 2016 .

[24]  J. Beringer,et al.  Temperature and human thermal comfort effects of street trees across three contrasting street canyon environments , 2016, Theoretical and Applied Climatology.

[25]  Neveen Hamza,et al.  Thermal perception of outdoor urban spaces in the hot arid region of Cairo, Egypt , 2016 .

[26]  Erik Johansson,et al.  Instruments and methods in outdoor thermal comfort studies – The need for standardization , 2014 .

[27]  김형수.  도시 재생(Urban Regeneration)의 본질 , 2014 .

[28]  D. Lai,et al.  Studies of outdoor thermal comfort in northern China , 2014 .

[29]  E. S. Krayenhoff,et al.  A Multi-layer Radiation Model for Urban Neighbourhoods with Trees , 2014, Boundary-Layer Meteorology.

[30]  Liang Chen,et al.  Simulation of the effect of downtown greenery on thermal comfort in subtropical climate using PET index: a case study in Hong Kong , 2013 .

[31]  Liang Chen,et al.  Outdoor thermal comfort and outdoor activities: A review of research in the past decade , 2012 .

[32]  L. Shashua-Bar,et al.  The cooling efficiency of urban landscape strategies in a hot dry climate , 2009 .

[33]  F. Lindberg,et al.  SOLWEIG 1.0 – Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings , 2008, International journal of biometeorology.

[34]  H. Mayer,et al.  Modelling radiation fluxes in simple and complex environments: basics of the RayMan model , 2007, International journal of biometeorology.

[35]  A. Matzarakis,et al.  Assessment of the microclimatic and human comfort conditions in a complex urban environment: Modelling and measurements , 2006 .

[36]  A. Cheadle,et al.  Operational Definitions of Walkable Neighborhood: Theoretical and Empirical Insights. , 2006, Journal of physical activity & health.

[37]  P. Höppe,et al.  The physiological equivalent temperature – a universal index for the biometeorological assessment of the thermal environment , 1999, International journal of biometeorology.

[38]  M. Austin,et al.  Semantic Behavior Modeling and Event-Driven Reasoning for Urban System of Systems , 2018 .

[39]  A. R. Ennos,et al.  The effect of tree shade and grass on surface and globe temperatures in an urban area , 2012 .

[40]  H. Mayer,et al.  Modelling radiation fluxes in simple and complex environments—application of the RayMan model , 2007, International journal of biometeorology.