On the Relationship Between Spatial-Temporal Outdoor Thermal Comfort Simulations and Bike Ridership
Predicting resident comfort throughout a city over time and predicting the impact of these thermal sensations on mobility mode choice are key information required by urban planners and policy makers to promote and implement thermal comfort concepts. The Universal Thermal Climate Index (UTCI) has bee...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/139213 |
| _version_ | 1826204197497864192 |
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| author | Young Li Wen, Elizabeth Lyn |
| author2 | Reinhart, Christoph |
| author_facet | Reinhart, Christoph Young Li Wen, Elizabeth Lyn |
| author_sort | Young Li Wen, Elizabeth Lyn |
| collection | MIT |
| description | Predicting resident comfort throughout a city over time and predicting the impact of these thermal sensations on mobility mode choice are key information required by urban planners and policy makers to promote and implement thermal comfort concepts. 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. However, calculating the UTCI at high resolutions in urban spaces is complex as it requires inputs such as the ambient temperature, relative humidity, wind speed and mean radiant temperature at the point of interest.
This thesis investigates how simulating the urban environment at increasing levels of spatial refinement impacts UTCI values along three bike routes in Cambridge, MA. As a baseline, UTCI is estimated using data from a local weather file. Then, shading from buildings and trees along the routes are considered. Next, local wind speeds are incorporated from computational fluid dynamics simulations. Finally, surface temperatures of the surrounding environment are included. Subsequently, with the UTCI simulations and publicly available 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 spatial resolution does influence hourly UTCI values and the comfort bands that they fall into, especially in urban areas. Incorporating local wind speeds provides the largest impact on UTCI values, and causes a 10% reduction in annual cold stress hours. While the increments in spatial refinement also impacts UTCI in unshaded and exposed areas, the impact is smaller than in 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 1.02 trips. 48% of predictions were correct, and an additional 40% of predictions were off by 1 trip.
This thesis 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. |
| first_indexed | 2024-09-23T12:50:26Z |
| format | Thesis |
| id | mit-1721.1/139213 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:50:26Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1392132022-01-15T03:30:44Z On the Relationship Between Spatial-Temporal Outdoor Thermal Comfort Simulations and Bike Ridership Young Li Wen, Elizabeth Lyn Reinhart, Christoph Massachusetts Institute of Technology. Department of Architecture Predicting resident comfort throughout a city over time and predicting the impact of these thermal sensations on mobility mode choice are key information required by urban planners and policy makers to promote and implement thermal comfort concepts. 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. However, calculating the UTCI at high resolutions in urban spaces is complex as it requires inputs such as the ambient temperature, relative humidity, wind speed and mean radiant temperature at the point of interest. This thesis investigates how simulating the urban environment at increasing levels of spatial refinement impacts UTCI values along three bike routes in Cambridge, MA. As a baseline, UTCI is estimated using data from a local weather file. Then, shading from buildings and trees along the routes are considered. Next, local wind speeds are incorporated from computational fluid dynamics simulations. Finally, surface temperatures of the surrounding environment are included. Subsequently, with the UTCI simulations and publicly available 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 spatial resolution does influence hourly UTCI values and the comfort bands that they fall into, especially in urban areas. Incorporating local wind speeds provides the largest impact on UTCI values, and causes a 10% reduction in annual cold stress hours. While the increments in spatial refinement also impacts UTCI in unshaded and exposed areas, the impact is smaller than in 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 1.02 trips. 48% of predictions were correct, and an additional 40% of predictions were off by 1 trip. This thesis 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. S.M. 2022-01-14T14:57:05Z 2022-01-14T14:57:05Z 2021-06 2021-07-27T20:22:44.000Z Thesis https://hdl.handle.net/1721.1/139213 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Young Li Wen, Elizabeth Lyn On the Relationship Between Spatial-Temporal Outdoor Thermal Comfort Simulations and Bike Ridership |
| title | On the Relationship Between Spatial-Temporal Outdoor Thermal Comfort Simulations and Bike Ridership |
| title_full | On the Relationship Between Spatial-Temporal Outdoor Thermal Comfort Simulations and Bike Ridership |
| title_fullStr | On the Relationship Between Spatial-Temporal Outdoor Thermal Comfort Simulations and Bike Ridership |
| title_full_unstemmed | On the Relationship Between Spatial-Temporal Outdoor Thermal Comfort Simulations and Bike Ridership |
| title_short | On the Relationship Between Spatial-Temporal Outdoor Thermal Comfort Simulations and Bike Ridership |
| title_sort | on the relationship between spatial temporal outdoor thermal comfort simulations and bike ridership |
| url | https://hdl.handle.net/1721.1/139213 |
| work_keys_str_mv | AT youngliwenelizabethlyn ontherelationshipbetweenspatialtemporaloutdoorthermalcomfortsimulationsandbikeridership |