Built environment determinants of bicycle volume: A longitudinal analysis
This study examines determinants of bicycle volume in the built environment with a five-year bicycle count dataset from Seattle, Washington. A generalized linear mixed model (GLMM) is used to capture the bicycle volume change over time while controlling for temporal autocorrelations. The GLMM assume...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Minnesota
2017-06-01
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| Series: | Journal of Transport and Land Use |
| Subjects: | |
| Online Access: | https://www.jtlu.org/index.php/jtlu/article/view/892 |
| _version_ | 1819131466958766080 |
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| author | Peng Chen Jiangping Zhou Feiyang Sun |
| author_facet | Peng Chen Jiangping Zhou Feiyang Sun |
| author_sort | Peng Chen |
| collection | DOAJ |
| description | This study examines determinants of bicycle volume in the built environment with a five-year bicycle count dataset from Seattle, Washington. A generalized linear mixed model (GLMM) is used to capture the bicycle volume change over time while controlling for temporal autocorrelations. The GLMM assumes that bicycle count follows a Poisson distribution. The model results show that (1) the variables of non-winter seasons, peak hours, and weekends are positively associated with the increase of bicycle counts over time; (2) bicycle counts are fewer in steep areas; (3) bicycle counts are greater in zones with more mixed land use, a higher percentage of water bodies, or a greater percentage of workplaces; (4) the increment of bicycle infrastructure is positively associated with the increase of bicycle volume; and (5) bicycling is more popular in neighborhoods with a greater percentage of whites and younger adults. It concludes that areas with a smaller slope variation, a higher employment density, and a shorter distance to water bodies encourage bicycling. This conclusion suggests that to best boost bicycling, decision-makers should consider building more bicycle facilities in flat areas and integrating the facilities with employment densification and open-space creation and planning. |
| first_indexed | 2024-12-22T09:15:58Z |
| format | Article |
| id | doaj.art-0b5c1d3c3eed45989efed746c4df2d45 |
| institution | Directory Open Access Journal |
| issn | 1938-7849 |
| language | English |
| last_indexed | 2024-12-22T09:15:58Z |
| publishDate | 2017-06-01 |
| publisher | University of Minnesota |
| record_format | Article |
| series | Journal of Transport and Land Use |
| spelling | doaj.art-0b5c1d3c3eed45989efed746c4df2d452022-12-21T18:31:18ZengUniversity of MinnesotaJournal of Transport and Land Use1938-78492017-06-0110110.5198/jtlu.2017.892266Built environment determinants of bicycle volume: A longitudinal analysisPeng Chen0Jiangping Zhou1Feiyang Sun2Harbin Institute of Technology Shenzhen CampusUniversity of Hong KongUniversity of WashingtonThis study examines determinants of bicycle volume in the built environment with a five-year bicycle count dataset from Seattle, Washington. A generalized linear mixed model (GLMM) is used to capture the bicycle volume change over time while controlling for temporal autocorrelations. The GLMM assumes that bicycle count follows a Poisson distribution. The model results show that (1) the variables of non-winter seasons, peak hours, and weekends are positively associated with the increase of bicycle counts over time; (2) bicycle counts are fewer in steep areas; (3) bicycle counts are greater in zones with more mixed land use, a higher percentage of water bodies, or a greater percentage of workplaces; (4) the increment of bicycle infrastructure is positively associated with the increase of bicycle volume; and (5) bicycling is more popular in neighborhoods with a greater percentage of whites and younger adults. It concludes that areas with a smaller slope variation, a higher employment density, and a shorter distance to water bodies encourage bicycling. This conclusion suggests that to best boost bicycling, decision-makers should consider building more bicycle facilities in flat areas and integrating the facilities with employment densification and open-space creation and planning.https://www.jtlu.org/index.php/jtlu/article/view/892bicycle volumebuilt environmentlongitudinal data analysisgeneralized linear mixed model |
| spellingShingle | Peng Chen Jiangping Zhou Feiyang Sun Built environment determinants of bicycle volume: A longitudinal analysis Journal of Transport and Land Use bicycle volume built environment longitudinal data analysis generalized linear mixed model |
| title | Built environment determinants of bicycle volume: A longitudinal analysis |
| title_full | Built environment determinants of bicycle volume: A longitudinal analysis |
| title_fullStr | Built environment determinants of bicycle volume: A longitudinal analysis |
| title_full_unstemmed | Built environment determinants of bicycle volume: A longitudinal analysis |
| title_short | Built environment determinants of bicycle volume: A longitudinal analysis |
| title_sort | built environment determinants of bicycle volume a longitudinal analysis |
| topic | bicycle volume built environment longitudinal data analysis generalized linear mixed model |
| url | https://www.jtlu.org/index.php/jtlu/article/view/892 |
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