Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in Nanjing
A public bicycle system (PBS) is a promising countermeasure for the traffic issues induced by rapid urbanization, and it is widely acknowledged that the built environment has a significant impact on the use of a PBS. However, as the urban built-up area expands, different regions within a city can ex...
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| Format: | Article |
| Language: | English |
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University of Minnesota Libraries Publishing
2020-11-01
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| Series: | Journal of Transport and Land Use |
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| Online Access: | https://www.jtlu.org/index.php/jtlu/article/view/1568 |
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| author | Cheng Lyu Xinhua Wu Yang Liu Zhiyuan Liu Xun Yang |
| author_facet | Cheng Lyu Xinhua Wu Yang Liu Zhiyuan Liu Xun Yang |
| author_sort | Cheng Lyu |
| collection | DOAJ |
| description | A public bicycle system (PBS) is a promising countermeasure for the traffic issues induced by rapid urbanization, and it is widely acknowledged that the built environment has a significant impact on the use of a PBS. However, as the urban built-up area expands, different regions within a city can exhibit diverse characteristics. The spatial effects and differences among regions have been neglected by existing studies. To better understand how the urban built environment affects PBS ridership, this study conducts a quantitative analysis of the spatial relationship. It introduces a multi-scale geographically weighted regression (MGWR) to accomplish this task and conducts and evaluates a case study of the PBS in Nanjing, China. Six types of “D” variables (density, diversity, design, destination accessibility, distance to transit, and demand management) are involved in the analysis. The proposed method outperforms linear regression and standard geographically weighted regression (GWR) in terms of explanatory power. The modeling results demonstrate different influencing patterns between traditional downtown areas and newly built-up areas, especially for the density of population, road network, parking space, and various points of interest. |
| format | Article |
| id | doaj-art-353b8ba9b3c6442bbfe72b8b15ac7549 |
| institution | OA Journals |
| issn | 1938-7849 |
| language | English |
| publishDate | 2020-11-01 |
| publisher | University of Minnesota Libraries Publishing |
| record_format | Article |
| series | Journal of Transport and Land Use |
| spelling | doaj-art-353b8ba9b3c6442bbfe72b8b15ac75492025-08-20T01:51:04ZengUniversity of Minnesota Libraries PublishingJournal of Transport and Land Use1938-78492020-11-0113110.5198/jtlu.2020.1568Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in NanjingCheng Lyu0Xinhua Wu1Yang Liu2Zhiyuan Liu3Xun Yang4Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, ChinaJiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, ChinaJiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, ChinaJiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, ChinaSoutheast UniversityA public bicycle system (PBS) is a promising countermeasure for the traffic issues induced by rapid urbanization, and it is widely acknowledged that the built environment has a significant impact on the use of a PBS. However, as the urban built-up area expands, different regions within a city can exhibit diverse characteristics. The spatial effects and differences among regions have been neglected by existing studies. To better understand how the urban built environment affects PBS ridership, this study conducts a quantitative analysis of the spatial relationship. It introduces a multi-scale geographically weighted regression (MGWR) to accomplish this task and conducts and evaluates a case study of the PBS in Nanjing, China. Six types of “D” variables (density, diversity, design, destination accessibility, distance to transit, and demand management) are involved in the analysis. The proposed method outperforms linear regression and standard geographically weighted regression (GWR) in terms of explanatory power. The modeling results demonstrate different influencing patterns between traditional downtown areas and newly built-up areas, especially for the density of population, road network, parking space, and various points of interest.https://www.jtlu.org/index.php/jtlu/article/view/1568Public bicycle systemBuilt environmentMulti-scale geographically weighted regressionSpatial heterogeneity |
| spellingShingle | Cheng Lyu Xinhua Wu Yang Liu Zhiyuan Liu Xun Yang Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in Nanjing Journal of Transport and Land Use Public bicycle system Built environment Multi-scale geographically weighted regression Spatial heterogeneity |
| title | Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in Nanjing |
| title_full | Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in Nanjing |
| title_fullStr | Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in Nanjing |
| title_full_unstemmed | Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in Nanjing |
| title_short | Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in Nanjing |
| title_sort | exploring multi scale spatial relationship between built environment and public bicycle ridership a case study in nanjing |
| topic | Public bicycle system Built environment Multi-scale geographically weighted regression Spatial heterogeneity |
| url | https://www.jtlu.org/index.php/jtlu/article/view/1568 |
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