Data aggregation impacts on built environment-mode share models around public transit stations
This study examines how data aggregation influences the relationship between the built environment (BE) and mode share around 2,794 rail and BRT stations in the United States, using both inferential and machine learning methods. The results indicate that data aggregation impacts the outcomes of BE-...
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| Format: | Article |
| Language: | English |
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University of Minnesota Libraries Publishing
2025-05-01
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| Series: | Journal of Transport and Land Use |
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| Online Access: | https://jtlu.org/index.php/jtlu/article/view/2676 |
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| author | Seyed Sajjad Abdollahpour Huyen T. K. Le Ralph Buehler Steve Hankey |
| author_facet | Seyed Sajjad Abdollahpour Huyen T. K. Le Ralph Buehler Steve Hankey |
| author_sort | Seyed Sajjad Abdollahpour |
| collection | DOAJ |
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This study examines how data aggregation influences the relationship between the built environment (BE) and mode share around 2,794 rail and BRT stations in the United States, using both inferential and machine learning methods. The results indicate that data aggregation impacts the outcomes of BE-mode share models, regardless of the data analysis approach. Models using network buffers are less affected by data aggregation compared to those using circular buffers, Thiessen polygons, or administrative boundaries (block groups). In addition, the optimal buffer sizes for capturing BE effects and minimizing sensitivity to data aggregation for active and public transit modes are 800 meters for BRT stations and 1000 meters for rail stations, while 1200 meters is effective for private vehicle mode share at both rail and BRT stations. Furthermore, key BE features in commuting mode share models—such as employment density, jobs per household, intersection density, residential density, distance from the central business district, job accessibility (active), and regional population density—remain robust against data aggregation. We recommend that urban and transportation planners account for aggregation biases and apply multiple methods when evaluating BE's impact on mode share around public transit stations to inform more effective policy recommendations.
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| format | Article |
| id | doaj-art-bc41a642498042e3bae239a688cd8a97 |
| institution | DOAJ |
| issn | 1938-7849 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | University of Minnesota Libraries Publishing |
| record_format | Article |
| series | Journal of Transport and Land Use |
| spelling | doaj-art-bc41a642498042e3bae239a688cd8a972025-08-20T03:08:17ZengUniversity of Minnesota Libraries PublishingJournal of Transport and Land Use1938-78492025-05-0118110.5198/jtlu.2025.2676Data aggregation impacts on built environment-mode share models around public transit stationsSeyed Sajjad Abdollahpour0Huyen T. K. Le1Ralph Buehler2Steve Hankey3Virginia Polytechnic Institute and State University The Ohio State UniversityVirginia Polytechnic Institute and State UniversityVirginia Polytechnic Institute and State University This study examines how data aggregation influences the relationship between the built environment (BE) and mode share around 2,794 rail and BRT stations in the United States, using both inferential and machine learning methods. The results indicate that data aggregation impacts the outcomes of BE-mode share models, regardless of the data analysis approach. Models using network buffers are less affected by data aggregation compared to those using circular buffers, Thiessen polygons, or administrative boundaries (block groups). In addition, the optimal buffer sizes for capturing BE effects and minimizing sensitivity to data aggregation for active and public transit modes are 800 meters for BRT stations and 1000 meters for rail stations, while 1200 meters is effective for private vehicle mode share at both rail and BRT stations. Furthermore, key BE features in commuting mode share models—such as employment density, jobs per household, intersection density, residential density, distance from the central business district, job accessibility (active), and regional population density—remain robust against data aggregation. We recommend that urban and transportation planners account for aggregation biases and apply multiple methods when evaluating BE's impact on mode share around public transit stations to inform more effective policy recommendations. https://jtlu.org/index.php/jtlu/article/view/2676Travel behaviorLand useUrban formZoning and scale effectsModifiable areal unit of problem |
| spellingShingle | Seyed Sajjad Abdollahpour Huyen T. K. Le Ralph Buehler Steve Hankey Data aggregation impacts on built environment-mode share models around public transit stations Journal of Transport and Land Use Travel behavior Land use Urban form Zoning and scale effects Modifiable areal unit of problem |
| title | Data aggregation impacts on built environment-mode share models around public transit stations |
| title_full | Data aggregation impacts on built environment-mode share models around public transit stations |
| title_fullStr | Data aggregation impacts on built environment-mode share models around public transit stations |
| title_full_unstemmed | Data aggregation impacts on built environment-mode share models around public transit stations |
| title_short | Data aggregation impacts on built environment-mode share models around public transit stations |
| title_sort | data aggregation impacts on built environment mode share models around public transit stations |
| topic | Travel behavior Land use Urban form Zoning and scale effects Modifiable areal unit of problem |
| url | https://jtlu.org/index.php/jtlu/article/view/2676 |
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