IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i11p3220-d238709.html
   My bibliography  Save this article

Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data

Author

Listed:
  • Fan Yang

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China)

  • Fan Ding

    (Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA)

  • Xu Qu

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China)

  • Bin Ran

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China)

Abstract

Dockless shared-bikes have become a new transportation mode in major urban cities in China. Excessive number of shared-bikes can occupy a significant amount of roadway surface and cause trouble for pedestrians and auto vehicle drivers. Understanding the trip pattern of shared-bikes is essential in estimating the reasonable size of shared-bike fleet. This paper proposed a methodology to estimate the shared-bike trip using location-based social network data and conducted a case study in Nanjing, China. The ordinary least square, geographically weighted regression (GWR) and semiparametric geographically weighted regression (SGWR) methods are used to establish the relationship among shared-bike trip, distance to the subway station and check ins in different categories of the point of interest (POI). This method could be applied to determine the reasonable number of shared-bikes to be launched in new places and economically benefit in shared-bike management.

Suggested Citation

  • Fan Yang & Fan Ding & Xu Qu & Bin Ran, 2019. "Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3220-:d:238709
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/11/3220/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/11/3220/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Llorca, Carlos & Ji, Joanna & Molloy, Joseph & Moeckel, Rolf, 2018. "The usage of location based big data and trip planning services for the estimation of a long-distance travel demand model. Predicting the impacts of a new high speed rail corridor," Research in Transportation Economics, Elsevier, vol. 72(C), pages 27-36.
    2. Cheng, Long & Chen, Xuewu & Yang, Shuo & Cao, Zhan & De Vos, Jonas & Witlox, Frank, 2019. "Active travel for active ageing in China: The role of built environment," Journal of Transport Geography, Elsevier, vol. 76(C), pages 142-152.
    3. Kou, Zhaoyu & Cai, Hua, 2019. "Understanding bike sharing travel patterns: An analysis of trip data from eight cities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 785-797.
    4. A. Stewart Fotheringham & Martin Charlton & Chris Brunsdon, 1997. "Measuring Spatial Variations in Relationships with Geographically Weighted Regression," Advances in Spatial Science, in: Manfred M. Fischer & Arthur Getis (ed.), Recent Developments in Spatial Analysis, chapter 4, pages 60-82, Springer.
    5. Wang, Jueyu & Lindsey, Greg, 2019. "Do new bike share stations increase member use: A quasi-experimental study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 1-11.
    6. Yuyun & Fritz Akhmad Nuzir & Bart Julien Dewancker, 2017. "Dynamic Land-Use Map Based on Twitter Data," Sustainability, MDPI, vol. 9(12), pages 1-20, November.
    7. Xianyuan Zhan & Satish Ukkusuri & Feng Zhu, 2014. "Inferring Urban Land Use Using Large-Scale Social Media Check-in Data," Networks and Spatial Economics, Springer, vol. 14(3), pages 647-667, December.
    8. James P. LeSage, 2004. "A Family of Geographically Weighted Regression Models," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 11, pages 241-264, Springer.
    9. Gilbert Laporte & Frédéric Meunier & Roberto Wolfler Calvo, 2018. "Shared mobility systems: an updated survey," Annals of Operations Research, Springer, vol. 271(1), pages 105-126, December.
    10. Yang Liu & Yanjie Ji & Zhuangbin Shi & Liangpeng Gao, 2018. "The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    11. Yang, Hongtai & Lu, Xiaozhao & Cherry, Christopher & Liu, Xiaohan & Li, Yanlai, 2017. "Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 64(C), pages 184-194.
    12. Yaxiong Ma & Sucharita Gopal, 2018. "Geographically Weighted Regression Models in Estimating Median Home Prices in Towns of Massachusetts Based on an Urban Sustainability Framework," Sustainability, MDPI, vol. 10(4), pages 1-27, March.
    13. Gu, Tianqi & Kim, Inhi & Currie, Graham, 2019. "To be or not to be dockless: Empirical analysis of dockless bikeshare development in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 122-147.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Franklin Oliveira & Dilan Nery & Daniel G. Costa & Ivanovitch Silva & Luciana Lima, 2021. "A Survey of Technologies and Recent Developments for Sustainable Smart Cycling," Sustainability, MDPI, vol. 13(6), pages 1-28, March.
    2. Wang, Xudong & Cheng, Zhanhong & Trépanier, Martin & Sun, Lijun, 2021. "Modeling bike-sharing demand using a regression model with spatially varying coefficients," Journal of Transport Geography, Elsevier, vol. 93(C).
    3. Yanyan Chen & Hanqiang Qian & Yang Wang, 2020. "Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    4. Jialing Zhao & Hongwei Wang & Yuxin Huang & Yuan Meng, 2020. "Does Massive Placement of Bicycles Win the Market for the Bicycle-Sharing Company in China?," Sustainability, MDPI, vol. 12(13), pages 1-14, June.
    5. Shuo Zhang & Li Chen & Yingzi Li, 2021. "Shared Bicycle Distribution Connected to Subway Line Considering Citizens’ Morning Peak Social Characteristics for Urban Low-Carbon Development," Sustainability, MDPI, vol. 13(16), pages 1-19, August.
    6. Fan Yang & Linchao Li & Fan Ding & Huachun Tan & Bin Ran, 2020. "A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    7. Daozhi Zhao & Di Wang, 2019. "The Research of Tripartite Collaborative Governance on Disorderly Parking of Shared Bicycles Based on the Theory of Planned Behavior and Motivation Theories—A Case of Beijing, China," Sustainability, MDPI, vol. 11(19), pages 1-21, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mix, Richard & Hurtubia, Ricardo & Raveau, Sebastián, 2022. "Optimal location of bike-sharing stations: A built environment and accessibility approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 126-142.
    2. Cheng, Long & Shi, Kunbo & De Vos, Jonas & Cao, Mengqiu & Witlox, Frank, 2021. "Examining the spatially heterogeneous effects of the built environment on walking among older adults," Transport Policy, Elsevier, vol. 100(C), pages 21-30.
    3. Liu, Yang & Min, Shisheng & Shi, Zhuangbin & He, Mingwei, 2024. "Exploring students' choice of active travel to school in different spatial environments: A case study in a mountain city," Journal of Transport Geography, Elsevier, vol. 115(C).
    4. Li, Shaoying & Zhuang, Caigang & Tan, Zhangzhi & Gao, Feng & Lai, Zhipeng & Wu, Zhifeng, 2021. "Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China," Journal of Transport Geography, Elsevier, vol. 91(C).
    5. Mingyang Du & Lin Cheng & Xuefeng Li & Jingzong Yang, 2019. "Investigating the Influential Factors of Shared Travel Behavior: Comparison between App-Based Third Taxi Service and Free-Floating Bike Sharing in Nanjing, China," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    6. Eldeeb, Gamal & Mohamed, Moataz & Páez, Antonio, 2021. "Built for active travel? Investigating the contextual effects of the built environment on transportation mode choice," Journal of Transport Geography, Elsevier, vol. 96(C).
    7. Guangnian Xiao & Zihao Wang, 2020. "Empirical Study on Bikesharing Brand Selection in China in the Post-Sharing Era," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    8. Fan Yang & Linchao Li & Fan Ding & Huachun Tan & Bin Ran, 2020. "A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    9. Xinwei Ma & Ruiming Cao & Jianbiao Wang, 2019. "Effects of Psychological Factors on Modal Shift from Car to Dockless Bike Sharing: A Case Study of Nanjing, China," IJERPH, MDPI, vol. 16(18), pages 1-16, September.
    10. Longzhu Xiao & Linchuan Yang & Jixiang Liu & Hongtai Yang, 2020. "Built Environment Correlates of the Propensity of Walking and Cycling," Sustainability, MDPI, vol. 12(20), pages 1-16, October.
    11. Yanyan Chen & Hanqiang Qian & Yang Wang, 2020. "Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    12. Pulugurtha, Srinivas S. & Mathew, Sonu, 2021. "Modeling AADT on local functionally classified roads using land use, road density, and nearest nonlocal road data," Journal of Transport Geography, Elsevier, vol. 93(C).
    13. Junming Li & Meijun Jin & Honglin Li, 2019. "Exploring Spatial Influence of Remotely Sensed PM 2.5 Concentration Using a Developed Deep Convolutional Neural Network Model," IJERPH, MDPI, vol. 16(3), pages 1-11, February.
    14. Gu, Tianqi & Xu, Weiping & Liang, Hua & He, Qing & Zheng, Nan, 2024. "School bus transport service strategies’ policy-making mechanism – An evolutionary game approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    15. Pede, Valerien O. & Florax, Raymond J.G.M. & Holt, Matthew T., 2009. "A Spatial Econometric Star Model With An Application To U.S. County Economic Growth, 1969–2003," Working papers 48117, Purdue University, Department of Agricultural Economics.
    16. Boyacı, Burak & Zografos, Konstantinos G., 2019. "Investigating the effect of temporal and spatial flexibility on the performance of one-way electric carsharing systems," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 244-272.
    17. Shafida Azwina Mohd Shafie & Lee Vien Leong & Ahmad Farhan Mohd Sadullah, 2021. "A Trip Generation Model for a Petrol Station with a Convenience Store and a Fast-Food Restaurant," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    18. Su, Rongxiang & Xiao, Jingyi & McBride, Elizabeth C. & Goulias, Konstadinos G., 2021. "Understanding senior's daily mobility patterns in California using human mobility motifs," Journal of Transport Geography, Elsevier, vol. 94(C).
    19. Mur, Jesús & Angulo, Ana, 2009. "Model selection strategies in a spatial setting: Some additional results," Regional Science and Urban Economics, Elsevier, vol. 39(2), pages 200-213, March.
    20. Jin, Peizhen & Mangla, Sachin Kumar & Song, Malin, 2021. "Moving towards a sustainable and innovative city: Internal urban traffic accessibility and high-level innovation based on platform monitoring data," International Journal of Production Economics, Elsevier, vol. 235(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3220-:d:238709. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.