Article

Modeling outdoor thermal comfort along cycling routes at varying levels of physical accuracy to predict bike ridership in Cambridge, MA

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Abstract

The Universal Thermal Climate Index (UTCI) has been linked to outdoor activity patterns and used to evaluate the effectiveness of urban interventions to improve thermal comfort. This study investigates how simulating the urban environment at increasing levels of physical accuracy impacts UTCI values along three cycling routes in Cambridge, Massachusetts. Baseline UTCI values are estimated using a local weather file, and the following increments in physical accuracy are considered: wind-scaling, shading from buildings, shading and cooling from trees, computational fluid dynamics simulations for wind speeds, and simulated surface temperatures. With bike ridership data from Bluebikes, Boston's bike-sharing program, the relationship between bike ridership patterns and UTCI values along each route is studied. Supervised machine learning models are applied to predict bike ridership based on UTCI and other predictors. UTCI simulation results show that incorporating the various increments of accuracy influences hourly UTCI values at urban areas and exposed areas differently. Incorporating local wind speeds is especially impactful for urban areas. The statistical models trained to predict hourly bike trip counts based on UTCI and other demand and weather predictors achieved a root-mean-squared error of 1.06 trips. 47% of predictions were correct, and an additional 42% of predictions were off by 1 trip. This study demonstrates the importance of spatial refinement in simulating UTCI, and motivates future research into efficient simulation methods or rules-of-thumb for deriving spatial-temporal UTCI values. Future work into building a robust predictive model would motivate the design of thermally comfortable environments for human-powered transportation in cities.

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... For instance, Liu et al. (2016) combined measured thermal parameters with simulated wind speed to realize outdoor thermal comfort prediction, Mackey (2017) proposed a 'hybrid' outdoor thermal comfort simulation method, and Kastner and Dogan (2017) carried out simulations and validation using a similar approach. Young et al. (2022) further explored the effect of different physical input accuracies on UTCI simulation results. In terms of thermal comfort design for outdoor spaces, Kastner and Dogan (2020) showed how space use can be estimated from simulations and used to determine intervention areas for space design, while Wang et al. (2021) optimized the layout of HRB through a multiobjective optimization approach that considers additional indoor and outdoor physical indicators. ...
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... Butterfly, Swift, Eddy3D, ixCube CFD, and ProceduralCS on the Grasshopper platform are all plug-ins for CFD simulations that use the CFD solver OpenFOAM and have been validated for high reliability [24]. Given the greater operability and better reproduction of the simultaneous action of multiple wind directions in the airflow simulation [25,26], eddy3D was selected as the CFD solver for this study. Meanwhile, the thermal environment simulation was performed in Rhino and Grasshopper using ladybug [12]. ...
... Thus, the area ratio of the static wind zone is less impacted (see Table 7). The influence of morphological indicators on the static wind ratio can be accurately depicted using the following equation (Equations: (25)): ...
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... Shade can be categorized into natural shade, provided by trees, and built shade, provided by buildings. The shade from buildings and the combined shading and cooling effects from trees substantially influence thermal comfort on cycling routes (Young et al., 2022). High-rise buildings can offer considerable shaded areas, while tree canopies can mitigate shortwave solar radiation penetration, thereby decreasing surface and air temperatures in shaded areas (Roy et al., 2012). ...
... This study quantifies heat stress using the UTCI, measured in • C, which has been used widely to indicate human heat stress levels (Young et al 2021). UTCI values higher than 32 • C is generally considered strong heat stress levels (figure 2(c)). ...
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... In this regard, new studies are investigating spatial and accuracy-adaptive workflows to estimate variations in prediction accuracy and their applicability at the large urban scale. Variations in outdoor thermal comfort assessments along cycling routes in Boston, US, were investigated on the basis of the varying levels of physical and spatial accuracy of: weather data; wind scaling; building shading; simulated urban surface temperatures; and wind Preprint simulations [188]. Besides demonstrating the incremental accuracy of the approaches, the research also showed that the variations were not uniform in different urban areas, thus showing that different spatial and accuracy workflows can be used according to the urban area investigated, with a potential reduction in simulation times. ...
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... For instance, Alcorn and Jiao (2023) argue that more comfortable cycling facilities exhibit a positive correlation with the use of shared bikes, a finding that our results corroborate, as depicted in Figure 7(c). Moreover, Young et al. (2022) contend that the comfort of the cycling environment not only encourages greater cycling activity but also extends the distance. Our study aligns with this perspective. ...
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Extending cycling distances is crucial for sustainable urban transport development and plays a role in encouraging the shift from motorized vehicles to public transport. However, there is a lack of research examining the combined impacts of both objective and perceived aspects of the cycling environment on cycling distance, and the existence of threshold effects remains unclear. This study uses 2019 cycling data from Shenzhen, China, employing the XGBoost algorithm to uncover the relative importance and thresholds of objective and perceived factors in the cycling environment. The results indicate that population density (24.8%), road network density (15.2%), the proportion of recreational facilities (9.1%), perceived accessibility (8.0%), and comfort (8.6%) hold high relative importance in predicting cycling distance. Also, maintaining road network density between 3 to 6 km/km2 and increasing the population density to exceed 22,000 people/km2 proves effective in extending cycling distances. Land use demonstrates a threshold effect, with cycling distances increasing when the recreational facilities share exceeds 8%, transport facilities share remains below 25%, and commercial facilities share stays below 30%. Perceived metrics exhibit a clear threshold effect. The study identifies that perceived safety indicates a psychological bottleneck in increasing cycling distance. Perceived accessibility is positively correlated with cycling distance when accessibility is at a low level, while comfort shows a positive correlation with cycling distance when comfort is at a high level. These findings can contribute to refining land planning and prioritizing resource allocation for organizations aiming to promote non-motorized travel and design bicycle-friendly environments.
... where subscripts lw, diff, and dir stand for the contribution from longwave, diffuse shortwave, and direct shortwave radiation, respectively (Walikewitz et al., 2015;Young et al., 2022): ...
... [12][13][14] The choice of an indicator that has been used in previous peer-reviewed studies also allows for comparisons between studies. The UTCI has also been applied to a range of sporting events 15 , and physical activities 16 . Despite the limitations of the UTCI, the vision of the larger research community and published peer-reviewed articles directed our use of the index. ...
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We value comments on our research paper in a Commentary in this issue (Simpson, S Afr J Sci. 2024;120(1/2), Art. #16445). Acknowledging the Universal Thermal Comfort Index (UTCI)'s limitations in capturing individual physiological responses remains important; however, we argue for its appropriateness based on recent thermophysiology and heat exchange advancements during its development and broader alignment with standardised indexing efforts. Our original research paper set out with these considerations in mind, and our conclusions remain valid. We further argue for refinement of the UTCI for specific activities instead of using the RET. Finally, future efforts should focus on monitoring data in real-world scenarios to validate and improve thermal indices
... The UTCI, distinguished for its comprehensiveness, surpasses other thermal comfort models. It is versatile enough to be utilized across diverse climatic conditions globally and is applicable at various research scales [21]. ...
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... Sun exposed building structures especially in the urban context store a high amount of solar energy through solar absorption and radiation (Rizwan, Dennis and Liu, 2008). Studies have already revealed that passive cooling strategies that provide shading for exposed (Molter and Chokhachian, 2021;Young et al., 2021). However, the large vertical surfaces of building envelopes also provide a vast design scope for the deployment of passive solar protection. ...
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... Human heat stress is a comprehensive index in uenced by the air temperature, solar radiation, humidity, and wind speed, along with physiological factors and possibly other environmental considerations. This study quanti es potential heat stress using the UTCI, which has units of °C, has been used widely to indicate the human heat stress level (Young et al., 2021). UTCI higher than 32°C is generally de ned as strong heat stress level (Fig. 2c). ...
Preprint
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... The former uses fully integrated microclimate modelling engines that produce highly accurate maps that account for fluid and thermodynamic interactions and consider air flow, surface heat exchange, water, and vegetation. Hybrid approaches combine independently simulated variables to map their collective impact while ignoring interactions between parameters [17], [18], [27]. Both approaches can take up to a month to run for an annual study of an urban block [17], making them largely unusable for design integration. ...
... Moreover, human thermal comfort indices such as heat index (Ming et al., 2018); effective temperature (Wu and Liao, 2020) and comprehensive comfort index (CCI) , are also considered in the modeling of travel demand. Young et al. (2022) have found a bellshaped effect of temperature on travel demand of active modes. For example, the temperature is positively associated with cycling ridership while it turns into a negative effect when it is higher than a specific value, such as 28℃ (Miranda-Moreno and Nosal, 2011). ...
... Hence, the scope of this study was narrowed to assessing the data quality by low-speed sampling. We tested a thermal-biking campaign [6] in the Esposizione Universale Roma (EUR) district in Rome, Italy. The data quality was assessed by finding the time lag of sampling nuanced Ta and Rh variations below the driving speeds (< 6 m/s). ...
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... Therefore, in recent years, several studies have focused on the role of urban layouts and furniture [3][4][5], urban canyon geometries [6][7][8][9], materials applied in cities [10,11], and vegetations [12][13][14] on the airflow in cities and consequently natural ventilation of buildings and the space between them. Furthermore several studies analysed different urban configurations and provided design recommendations for achieving outdoor thermal comfort in outdoor spaces [15][16][17][18][19]. ...
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... The transient nature of thermal comfort has been widely discussed in indoor studies (Vellei et al. 2021). However, such understandings of outdoor environments are generally lacking (Young et al. 2022). ...
Chapter
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Over the past century more than 100 indices have been developed and used to assess bioclimatic conditions for human beings. The majority of these indices are used sporadically or for specific purposes. Some are based on generalized results of measurements (wind chill, cooling power, wet bulb temperature) and some on the empirically observed reactions of the human body to thermal stress (physiological strain, effective temperature). Those indices that are based on human heat balance considerations are referred to as "rational indices". Several simple human heat balance models are known and are used in research and practice. This paper presents a comparative analysis of the newly developed Universal Thermal Climate Index (UTCI), and some of the more prevalent thermal indices. The analysis is based on three groups of data: global data-set, synoptic datasets from Europe, and local scale data from special measurement campaigns of COST Action 730. We found the present indices to express bioclimatic conditions reasonably only under specific meteorological situations, while the UTCI represents specific climates, weather, and locations much better. Furthermore, similar to the human body, the UTCI is very sensitive to changes in ambient stimuli: temperature, solar radiation, wind and humidity. UTCI depicts temporal variability of thermal conditions better than other indices. The UTCI scale is able to express even slight differences in the intensity of meteorological stimuli.
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Outdoor thermal comfort simulation simulations rely on the mean radiant temperature (MRT) seen by pedestrians as an important input that remains difficult to compute. Especially for large urban models, computing relevant surface temperatures and radiation fluxes that make up the MRT is a daunting task in terms of simulation setup and the computational overhead. We propose a new algorithm to estimate exterior surface temperatures of building facades, roofs, and ground surfaces in an arbitrary urban 3D model. The algorithm discretizes all model surfaces and clusters them by material properties and sky and sun exposure to reduce computational complexity. The model setup is fully automated, and the algorithm is implemented in the popular Rhino3d CAD environment. We demonstrate the accuracy of the algorithm by comparing both the resulting external surface temperatures against a high-fidelity simulation and the final MRT against real-world measurements. We report an RMSE of 1.8 °C and 2.0 °C, respectively, while reducing simulation times by a factor of ∼80. Envisioned applications of the algorithm range from rapid microclimate simulations in fast-paced urban design processes to large scale urban comfort evaluation of existing cities.
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There has been no study on the impacts of street tree interval on the street-level radiant environment, although it has been a key factor in the design of thermally comfortable streets. In this study, the variation in pedestrian mean radiant temperature (MRT) is investigated as a function of the tree interval using a newly developed multilayer MRT model. Tree size and street size are considered in the model experiments. The results show that MRT was most effectively reduced by large trees, indicating that they may be planted at wider intervals compared to smaller trees to achieve comparable MRT reductions. Furthermore, as the tree interval decreases, MRT reduction was increased exponentially by small trees, while MRT reduction was increased linearly by large trees. Therefore, urban planners can reduce pedestrian exposure to radiation by planting either larger trees or closely spaced smaller trees. These results provide insight into optimal configurations of street trees for maximum reduction of MRT in a variety of urban canyon configurations.
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This study presents a novel approach to validate the capability of biometeorological indices to predict the likelihood of urban dwellers to be outside during midday. Over a period of ten months three Wi-Fi scanners were used in a public courtyard in Cambridge, MA, to record outside dwelling patterns. Based on encrypted MacIDs courtyard attendees could be divided into 16,000 regulars and 676,000 visitors. Universal Thermal Climate Index (UTCI) predictions based on a combination of measured microclimatic conditions and mean radiant temperature simulations using ENVI-met were shown to strongly correlate with the number of regulars present during lunchtime with coefficients of determination (R2) of 92% during spring and 70% during summer/fall, respectively. Lunchtime attendance peaked for UTCI values in the thermal comfort and moderate heat stress ranges. In parallel, the probability for regulars to have lunch outside more than doubled during those UTCI conditions and the median lunchbreak length increased from 8 min to 12 min. These findings suggest that UTCI can be used as a reliable environmental performance metric to support the design and preservation of comfortable outdoor spaces. The reported use of public Wi-Fi data can help city governments to better understand – and potentially improve – the use of outdoor spaces while maintaining the privacy of their constituents.
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The study encompasses the validation of the dynamic, RADIANCE-based daylight simulation method DAYSIM, which uses the concept of daylight coefficients and the Perez sky model to predict the short-time-step development of indoor illuminances. Measured and simulated illuminances have been compared under 10,097 sky conditions in a full-scale test office with a double glazing and external venetian blinds. The additional planning effort for the designer compared to a conventional daylight simulation is addressed. It has been found that the treatment of direct sunlight strongly influences the accuracy of the daylight coefficient method. Three different simulation modes for the direct sunlight are investigated. The simulation results prove that indoor illuminances can be modeled with comparable accuracy for various blind settings under arbitrary sky conditions. Daylight autonomies are predicted with an accuracy below 2% points, where simulation errors stem with roughly equal parts from the raytracing and the sky model.
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A numerical simulation method is developed for predicting the effective radiation area and the projected area of a human body for any posture. This method is based on the solar heat gain simulation for buildings. To confirm the validity of the present method, predicted effective radiation area factors and projected area factors for both standing and seated persons are compared with those by the measurements. It was found that predicted values agree quite well with those by the subjective experiments within 10% accuracy. The effective radiation area and the diagrams of the projected area factors for a person sitting on the floor are illustrated. Moreover, the angle factors between a standing person and rectangular planes are calculated and compared with the results by Fanger.
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The relative humidity (RH) and the dewpoint temperature (td) are two widely used indicators of the amount of moisture in air. The exact conversion from RH to td, as well as highly accurate approximations, are too complex to be done easily without the help of a calculator or computer. However, there is a very simple rule of thumb that can be very useful for approximating the conversion for moist air (RH > 50%) which does not appear to be widely known by the meteorological community: td decreases by about 1°C for every 5% decrease in RH (starting at td = t, the dry bulb temperature, when RH = 100%). This article examines the mathematical basis and accuracy of this and other relationships between the dewpoint and relative humidity. Several useful applications of the simple conversion are presented, in particular the computation of the cumulus cloud-base level (or lifting condensation level) as zLCL (20 + t/5) (100 - RH), where zLCL is in meters when t is in degrees Celcius and RH in percent. Finally, a historical perspective is given with anecdotes about some of the early work in this field.
2018 revision of world urbanization prospects
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Living Streets : Strategies for Crafting Public Space
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