Exploring the Impact of Built Environment Factors on the Relationships between Bike Sharing and Public Transportation: A Case Study of New York
Bike sharing offers a usable form of feeder transportation for connecting to public transportation and effectively meets unmet travel demands, alleviating the pressure on public transportation systems by diverting urban commuters. To advance the comprehension of how the built environment shapes the...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/12/7/293 |
| _version_ | 1797436011775524864 |
|---|---|
| author | Baohua Wei Lei Zhu |
| author_facet | Baohua Wei Lei Zhu |
| author_sort | Baohua Wei |
| collection | DOAJ |
| description | Bike sharing offers a usable form of feeder transportation for connecting to public transportation and effectively meets unmet travel demands, alleviating the pressure on public transportation systems by diverting urban commuters. To advance the comprehension of how the built environment shapes the relationship between bike-sharing systems and public transport modes, we implement a categorization framework that divides bike-sharing data into three distinct patterns: competition, integration, and complementation, based on their coordination with public transportation. The SLM model is employed to investigate the complex correlations between the relationship patterns and four key groups of environmental factors encompassing land use, transportation systems, urban design, and social economy. We find a strong correlation between four groups of environmental factors and three relationship patterns. Furthermore, the built environment variables exhibit significant variations across the three patterns. Users in the competitive mode prefer the flexibility of shared bikes and place a higher value on the sightseeing and leisure benefits. Instead, users in the integration and complementation modes tend to prefer shared bikes to supplement unmet travel demand and place a higher value on commuting benefits. These findings can benefit urban planners seeking to encourage greater diversity in transportation modes and incentivize more commuting. |
| first_indexed | 2024-03-09T10:55:34Z |
| format | Article |
| id | doaj.art-ad6a151d1f874507a8fbf2e8e1ed4236 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T10:55:34Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-ad6a151d1f874507a8fbf2e8e1ed42362023-12-01T01:40:13ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-07-0112729310.3390/ijgi12070293Exploring the Impact of Built Environment Factors on the Relationships between Bike Sharing and Public Transportation: A Case Study of New YorkBaohua Wei0Lei Zhu1School of Architecture, Southeast University, Nanjing 210096, ChinaSchool of Architecture, Southeast University, Nanjing 210096, ChinaBike sharing offers a usable form of feeder transportation for connecting to public transportation and effectively meets unmet travel demands, alleviating the pressure on public transportation systems by diverting urban commuters. To advance the comprehension of how the built environment shapes the relationship between bike-sharing systems and public transport modes, we implement a categorization framework that divides bike-sharing data into three distinct patterns: competition, integration, and complementation, based on their coordination with public transportation. The SLM model is employed to investigate the complex correlations between the relationship patterns and four key groups of environmental factors encompassing land use, transportation systems, urban design, and social economy. We find a strong correlation between four groups of environmental factors and three relationship patterns. Furthermore, the built environment variables exhibit significant variations across the three patterns. Users in the competitive mode prefer the flexibility of shared bikes and place a higher value on the sightseeing and leisure benefits. Instead, users in the integration and complementation modes tend to prefer shared bikes to supplement unmet travel demand and place a higher value on commuting benefits. These findings can benefit urban planners seeking to encourage greater diversity in transportation modes and incentivize more commuting.https://www.mdpi.com/2220-9964/12/7/293bike sharingpublic transportationspatial patterngeographical variabilityspatial autoregressive model |
| spellingShingle | Baohua Wei Lei Zhu Exploring the Impact of Built Environment Factors on the Relationships between Bike Sharing and Public Transportation: A Case Study of New York ISPRS International Journal of Geo-Information bike sharing public transportation spatial pattern geographical variability spatial autoregressive model |
| title | Exploring the Impact of Built Environment Factors on the Relationships between Bike Sharing and Public Transportation: A Case Study of New York |
| title_full | Exploring the Impact of Built Environment Factors on the Relationships between Bike Sharing and Public Transportation: A Case Study of New York |
| title_fullStr | Exploring the Impact of Built Environment Factors on the Relationships between Bike Sharing and Public Transportation: A Case Study of New York |
| title_full_unstemmed | Exploring the Impact of Built Environment Factors on the Relationships between Bike Sharing and Public Transportation: A Case Study of New York |
| title_short | Exploring the Impact of Built Environment Factors on the Relationships between Bike Sharing and Public Transportation: A Case Study of New York |
| title_sort | exploring the impact of built environment factors on the relationships between bike sharing and public transportation a case study of new york |
| topic | bike sharing public transportation spatial pattern geographical variability spatial autoregressive model |
| url | https://www.mdpi.com/2220-9964/12/7/293 |
| work_keys_str_mv | AT baohuawei exploringtheimpactofbuiltenvironmentfactorsontherelationshipsbetweenbikesharingandpublictransportationacasestudyofnewyork AT leizhu exploringtheimpactofbuiltenvironmentfactorsontherelationshipsbetweenbikesharingandpublictransportationacasestudyofnewyork |