How the built environment shapes the spatiotemporal imbalances in dockless bike-sharing usage: A network-based analysis of Shenzhen, China.
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| Title: | How the built environment shapes the spatiotemporal imbalances in dockless bike-sharing usage: A network-based analysis of Shenzhen, China. |
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| Authors: | Wei, Zijun1 (AUTHOR) 2510006@tongji.edu.cn, Lin, Keyu1 (AUTHOR) Keyu_L@tongji.edu.cn, Diao, Mi1,2 (AUTHOR) Diaomi@tongji.edu.cn |
| Source: | Applied Geography. Aug2026, Vol. 193, pN.PAG-N.PAG. 1p. |
| Subjects: | Bicycle sharing programs, Built environment, Cities & towns, Urban transportation, Graph theory, Transit-oriented development |
| Geographic Terms: | Shenzhen (Guangdong Sheng, China : East), China |
| Abstract: | Dockless Bike Sharing (DBS) has become a dominant bike-sharing model in many cities due to its flexibility. However, this also intensifies operational rebalancing challenges. The built environment can influence DBS usage through multiple channels, potentially leading to usage imbalances. These imbalances, defined as the net change in bicycle inventory at specific locations, are critical to effective system management. Using large-scale DBS trip data, we apply a K-means clustering approach to aggregate daily origins and destinations into virtual stations, and construct a DBS complex network with these stations as nodes. We then analyze the spatiotemporal dynamics and community structure of the DBS network, and employ regression models to examine the effects of built-environment factors on DBS usage imbalance. The results reveal pronounced tidal patterns in DBS usage imbalance during morning and evening peak periods, whereas the community structure of the network remains relatively stable. Residential areas primarily function as origins of DBS trips during the morning peak, increasing the risk of bike shortages at nearby stations, while industrial areas function as destinations, leading to bike accumulation; the opposite pattern emerges during the evening peak. Metro stations serve as key intermodal transfer hubs, with morning peak inflows leading to bike accumulation and evening peak outflows increasing shortage risks. Our analyses suggest that transit-oriented development and jobs-housing separation constitute important structural mechanisms underlying DBS imbalance. These findings provide empirical evidence on how the built environment shapes spatial imbalance in DBS use, thereby supporting more effective operation and planning. • A DBS network is constructed by clustering trip origins and destinations as virtual stations. • DBS usage imbalance shows tidal patterns during peak periods, while community structure remains stable. • Development density affects DBS usage intensity, while land use shapes trip generation and attraction. • Transit-oriented development (TOD) and jobs–housing separation provide a spatial framework for interpreting DBS usage imbalance. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Geography is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195068848 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: How the built environment shapes the spatiotemporal imbalances in dockless bike-sharing usage: A network-based analysis of Shenzhen, China. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wei%2C+Zijun%22">Wei, Zijun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 2510006@tongji.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Lin%2C+Keyu%22">Lin, Keyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Keyu_L@tongji.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Diao%2C+Mi%22">Diao, Mi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> Diaomi@tongji.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Geography%22">Applied Geography</searchLink>. Aug2026, Vol. 193, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bicycle+sharing+programs%22">Bicycle sharing programs</searchLink><br /><searchLink fieldCode="DE" term="%22Built+environment%22">Built environment</searchLink><br /><searchLink fieldCode="DE" term="%22Cities+%26+towns%22">Cities & towns</searchLink><br /><searchLink fieldCode="DE" term="%22Urban+transportation%22">Urban transportation</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+theory%22">Graph theory</searchLink><br /><searchLink fieldCode="DE" term="%22Transit-oriented+development%22">Transit-oriented development</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Shenzhen+%28Guangdong+Sheng%2C+China+%3A+East%29%22">Shenzhen (Guangdong Sheng, China : East)</searchLink><br /><searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Dockless Bike Sharing (DBS) has become a dominant bike-sharing model in many cities due to its flexibility. However, this also intensifies operational rebalancing challenges. The built environment can influence DBS usage through multiple channels, potentially leading to usage imbalances. These imbalances, defined as the net change in bicycle inventory at specific locations, are critical to effective system management. Using large-scale DBS trip data, we apply a K-means clustering approach to aggregate daily origins and destinations into virtual stations, and construct a DBS complex network with these stations as nodes. We then analyze the spatiotemporal dynamics and community structure of the DBS network, and employ regression models to examine the effects of built-environment factors on DBS usage imbalance. The results reveal pronounced tidal patterns in DBS usage imbalance during morning and evening peak periods, whereas the community structure of the network remains relatively stable. Residential areas primarily function as origins of DBS trips during the morning peak, increasing the risk of bike shortages at nearby stations, while industrial areas function as destinations, leading to bike accumulation; the opposite pattern emerges during the evening peak. Metro stations serve as key intermodal transfer hubs, with morning peak inflows leading to bike accumulation and evening peak outflows increasing shortage risks. Our analyses suggest that transit-oriented development and jobs-housing separation constitute important structural mechanisms underlying DBS imbalance. These findings provide empirical evidence on how the built environment shapes spatial imbalance in DBS use, thereby supporting more effective operation and planning. • A DBS network is constructed by clustering trip origins and destinations as virtual stations. • DBS usage imbalance shows tidal patterns during peak periods, while community structure remains stable. • Development density affects DBS usage intensity, while land use shapes trip generation and attraction. • Transit-oriented development (TOD) and jobs–housing separation provide a spatial framework for interpreting DBS usage imbalance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Geography is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.apgeog.2026.104061 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Bicycle sharing programs Type: general – SubjectFull: Built environment Type: general – SubjectFull: Cities & towns Type: general – SubjectFull: Urban transportation Type: general – SubjectFull: Graph theory Type: general – SubjectFull: Transit-oriented development Type: general – SubjectFull: Shenzhen (Guangdong Sheng, China : East) Type: general – SubjectFull: China Type: general Titles: – TitleFull: How the built environment shapes the spatiotemporal imbalances in dockless bike-sharing usage: A network-based analysis of Shenzhen, China. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wei, Zijun – PersonEntity: Name: NameFull: Lin, Keyu – PersonEntity: Name: NameFull: Diao, Mi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01436228 Numbering: – Type: volume Value: 193 Titles: – TitleFull: Applied Geography Type: main |
| ResultId | 1 |
