Analysis of the spatio-temporal impact of the built environment on shared bicycle ridership density
Abstract The spatiotemporal nonstationarity of shared bicycle usage, a sustainable and eco-friendly mode of transportation, is believed to be influenced by the built environment. However, the specific spatial and temporal impacts of built environment factors on shared bicycle trips are not yet fully...
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Springer
2025-01-01
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| Series: | Computational Urban Science |
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| Online Access: | https://doi.org/10.1007/s43762-024-00153-x |
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| author | Na Li Tianqun Wang |
| author_facet | Na Li Tianqun Wang |
| author_sort | Na Li |
| collection | DOAJ |
| description | Abstract The spatiotemporal nonstationarity of shared bicycle usage, a sustainable and eco-friendly mode of transportation, is believed to be influenced by the built environment. However, the specific spatial and temporal impacts of built environment factors on shared bicycle trips are not yet fully understood. This study investigates the relationship between the built environment and shared bicycle ridership in Shenzhen, a city where the distribution of shared bicycles is relatively dense, by utilizing multisource urban big data. Key independent variables were selected based on the “5Ds” dimensions of the built environment, and the performance of two models—Geographically Weighted Regression (GWR) and Geographically and Temporally Weighted Regression (GTWR)—were compared. The analysis evaluates the impact of the built environment on the density of shared bicycle ridership, incorporating both spatial and temporal dimensions. The results of the study found that the GTWR model used in this paper can effectively explain the spatio-temporal heterogeneity of built environment-related variables on shared bicycle trips with high goodness of fit. And the regression fit coefficients of the model show that the effects of different built environment indicators on the density of shared bicycle ridership are significantly different in both time and space. Among them, road network density, catering POI density, traffic POI density and POI diversity have a facilitating effect on shared bicycle travels, particularly during peak hours on weekdays and in central urban areas. Shopping POI density shows different effects on shared bike use in different times and spaces. While the distance from the city center and the nearest distance to the bus station have a suppressive effect on shared bicycle use, they show opposite degrees of influence in the spatial distribution. The results can provide more precise guidance for future rational transportation strategies or sustainable urban planning. |
| format | Article |
| id | doaj-art-65cc7fe63d75425c9f85feb077d6ed71 |
| institution | Kabale University |
| issn | 2730-6852 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Computational Urban Science |
| spelling | doaj-art-65cc7fe63d75425c9f85feb077d6ed712025-01-12T12:11:51ZengSpringerComputational Urban Science2730-68522025-01-015111710.1007/s43762-024-00153-xAnalysis of the spatio-temporal impact of the built environment on shared bicycle ridership densityNa Li0Tianqun Wang1School of Economics and Management, Tianjin Chengjian UniversitySchool of Economics and Management, Tianjin Chengjian UniversityAbstract The spatiotemporal nonstationarity of shared bicycle usage, a sustainable and eco-friendly mode of transportation, is believed to be influenced by the built environment. However, the specific spatial and temporal impacts of built environment factors on shared bicycle trips are not yet fully understood. This study investigates the relationship between the built environment and shared bicycle ridership in Shenzhen, a city where the distribution of shared bicycles is relatively dense, by utilizing multisource urban big data. Key independent variables were selected based on the “5Ds” dimensions of the built environment, and the performance of two models—Geographically Weighted Regression (GWR) and Geographically and Temporally Weighted Regression (GTWR)—were compared. The analysis evaluates the impact of the built environment on the density of shared bicycle ridership, incorporating both spatial and temporal dimensions. The results of the study found that the GTWR model used in this paper can effectively explain the spatio-temporal heterogeneity of built environment-related variables on shared bicycle trips with high goodness of fit. And the regression fit coefficients of the model show that the effects of different built environment indicators on the density of shared bicycle ridership are significantly different in both time and space. Among them, road network density, catering POI density, traffic POI density and POI diversity have a facilitating effect on shared bicycle travels, particularly during peak hours on weekdays and in central urban areas. Shopping POI density shows different effects on shared bike use in different times and spaces. While the distance from the city center and the nearest distance to the bus station have a suppressive effect on shared bicycle use, they show opposite degrees of influence in the spatial distribution. The results can provide more precise guidance for future rational transportation strategies or sustainable urban planning.https://doi.org/10.1007/s43762-024-00153-xUrban trafficSpatio-temporal heterogeneityGeographically and temporally weighted regression modelShared bicycleBuilt environment |
| spellingShingle | Na Li Tianqun Wang Analysis of the spatio-temporal impact of the built environment on shared bicycle ridership density Computational Urban Science Urban traffic Spatio-temporal heterogeneity Geographically and temporally weighted regression model Shared bicycle Built environment |
| title | Analysis of the spatio-temporal impact of the built environment on shared bicycle ridership density |
| title_full | Analysis of the spatio-temporal impact of the built environment on shared bicycle ridership density |
| title_fullStr | Analysis of the spatio-temporal impact of the built environment on shared bicycle ridership density |
| title_full_unstemmed | Analysis of the spatio-temporal impact of the built environment on shared bicycle ridership density |
| title_short | Analysis of the spatio-temporal impact of the built environment on shared bicycle ridership density |
| title_sort | analysis of the spatio temporal impact of the built environment on shared bicycle ridership density |
| topic | Urban traffic Spatio-temporal heterogeneity Geographically and temporally weighted regression model Shared bicycle Built environment |
| url | https://doi.org/10.1007/s43762-024-00153-x |
| work_keys_str_mv | AT nali analysisofthespatiotemporalimpactofthebuiltenvironmentonsharedbicycleridershipdensity AT tianqunwang analysisofthespatiotemporalimpactofthebuiltenvironmentonsharedbicycleridershipdensity |