Correlation between the built environment and dockless bike-sharing trips connecting to urban metro stations
The influence of the built environment on dockless bike-sharing (DBS) trips connecting to urban metro stations has always been a significant problem for planners. However, the evidence for correlations between microscale built-environment factors and DBS-metro transfer trips remains inconclusive. To...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Minnesota Libraries Publishing
2023-05-01
|
| Series: | Journal of Transport and Land Use |
| Subjects: | |
| Online Access: | https://www.jtlu.org/index.php/jtlu/article/view/2262 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849471307060609024 |
|---|---|
| author | Jiaomin Wei Yanyan Chen Zhuo Liu Yang Wang |
| author_facet | Jiaomin Wei Yanyan Chen Zhuo Liu Yang Wang |
| author_sort | Jiaomin Wei |
| collection | DOAJ |
| description | The influence of the built environment on dockless bike-sharing (DBS) trips connecting to urban metro stations has always been a significant problem for planners. However, the evidence for correlations between microscale built-environment factors and DBS-metro transfer trips remains inconclusive. To address this, a framework, augmented by big data, is formulated to analyze the correlation of built environment with DBS–metro transfer trips from the macroscopic and microscopic views, considering Beijing as a case study. The trip density and cycling speed are calculated based on 11,120,676 pieces of DBS data and then used to represent the characteristic of DBS-metro transfer trips in a multiple linear regression model. Furthermore, a novel method is proposed to determine the built-environment sampling area around a station by its corresponding DBS travel distances. Accordingly, 6 microscale built-environment factors are extracted from street-view images using deep learning and integrated into the analysis model, together with 14 macroscale built-environment factors and 8 potential influencing factors of socioeconomic attributes and metro station attributes. The results reveal the significant positive influence of greenery and presence of barriers on trip density and cycling speed. Additionally, presence of streetlights is found to be negatively correlated with both trip density and cycling speed. Presence of signals is also found to have an influence on DBS-metro transfer trips, but it only negatively impacts trip density.
|
| format | Article |
| id | doaj-art-e1b370483cd14281ae11d78ea57c7979 |
| institution | Kabale University |
| issn | 1938-7849 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | University of Minnesota Libraries Publishing |
| record_format | Article |
| series | Journal of Transport and Land Use |
| spelling | doaj-art-e1b370483cd14281ae11d78ea57c79792025-08-20T03:24:52ZengUniversity of Minnesota Libraries PublishingJournal of Transport and Land Use1938-78492023-05-0116110.5198/jtlu.2023.2262Correlation between the built environment and dockless bike-sharing trips connecting to urban metro stationsJiaomin Wei0Yanyan Chen1Zhuo Liu2Yang Wang3Beijing University of TechnologyBeijing University of TechnologyBeijing University of TechnologyBeijing University of TechnologyThe influence of the built environment on dockless bike-sharing (DBS) trips connecting to urban metro stations has always been a significant problem for planners. However, the evidence for correlations between microscale built-environment factors and DBS-metro transfer trips remains inconclusive. To address this, a framework, augmented by big data, is formulated to analyze the correlation of built environment with DBS–metro transfer trips from the macroscopic and microscopic views, considering Beijing as a case study. The trip density and cycling speed are calculated based on 11,120,676 pieces of DBS data and then used to represent the characteristic of DBS-metro transfer trips in a multiple linear regression model. Furthermore, a novel method is proposed to determine the built-environment sampling area around a station by its corresponding DBS travel distances. Accordingly, 6 microscale built-environment factors are extracted from street-view images using deep learning and integrated into the analysis model, together with 14 macroscale built-environment factors and 8 potential influencing factors of socioeconomic attributes and metro station attributes. The results reveal the significant positive influence of greenery and presence of barriers on trip density and cycling speed. Additionally, presence of streetlights is found to be negatively correlated with both trip density and cycling speed. Presence of signals is also found to have an influence on DBS-metro transfer trips, but it only negatively impacts trip density. https://www.jtlu.org/index.php/jtlu/article/view/2262Bike sharingBuilt environmentMetro stationStreet-view imageCycling speed |
| spellingShingle | Jiaomin Wei Yanyan Chen Zhuo Liu Yang Wang Correlation between the built environment and dockless bike-sharing trips connecting to urban metro stations Journal of Transport and Land Use Bike sharing Built environment Metro station Street-view image Cycling speed |
| title | Correlation between the built environment and dockless bike-sharing trips connecting to urban metro stations |
| title_full | Correlation between the built environment and dockless bike-sharing trips connecting to urban metro stations |
| title_fullStr | Correlation between the built environment and dockless bike-sharing trips connecting to urban metro stations |
| title_full_unstemmed | Correlation between the built environment and dockless bike-sharing trips connecting to urban metro stations |
| title_short | Correlation between the built environment and dockless bike-sharing trips connecting to urban metro stations |
| title_sort | correlation between the built environment and dockless bike sharing trips connecting to urban metro stations |
| topic | Bike sharing Built environment Metro station Street-view image Cycling speed |
| url | https://www.jtlu.org/index.php/jtlu/article/view/2262 |
| work_keys_str_mv | AT jiaominwei correlationbetweenthebuiltenvironmentanddocklessbikesharingtripsconnectingtourbanmetrostations AT yanyanchen correlationbetweenthebuiltenvironmentanddocklessbikesharingtripsconnectingtourbanmetrostations AT zhuoliu correlationbetweenthebuiltenvironmentanddocklessbikesharingtripsconnectingtourbanmetrostations AT yangwang correlationbetweenthebuiltenvironmentanddocklessbikesharingtripsconnectingtourbanmetrostations |