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Modeling Bike Sharing System using Built Environment Factors

Author

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  • Tien Dung Tran

    (LET - Laboratoire d'économie des transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique)

  • Nicolas Ovtracht

    (LET - Laboratoire d'économie des transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique)

  • Bruno Faivre D’arcier

    (LET - Laboratoire d'économie des transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper aims to present a modeling of bike sharing demand at station level in the city of Lyon. Robust linear regression models were used in order to predict the flows of each station. The data used in this project consists of over 6 million bike sharing trips recorded in 2011. The built environment variables used in the model are determined in a buffer zone of 300 meters around each bike sharing station. In order to estimate the bike sharing flow, we use the method of linear regression during the peak periods of a weekday. The results show that bike sharing is principally used for commuting purposes by long term subscribers while short term subscriber's trips purposes are more varied. The combination between bike sharing and train seems to be an important inter-modality. An interesting finding is that student is an important user of bike sharing. We found that there were different types of bikesharing usage which are influenced by socio-economic factors depending on the period within the day and type of subscribers. The present findings could be useful for others cities which want to adopt a bikesharing system and also for a better planning and operation of existing systems. Further, the solutions to encourage the use of bikesharing will be various depending on type of subscribers. The approach in this paper can be useful for estimating car-sharing demand.

Suggested Citation

  • Tien Dung Tran & Nicolas Ovtracht & Bruno Faivre D’arcier, 2015. "Modeling Bike Sharing System using Built Environment Factors," Post-Print halshs-01474166, HAL.
    • Handle: RePEc:hal:journl:halshs-01474166
    DOI: 10.1016/j.procir.2015.02.156
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01474166
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    References listed on IDEAS

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    Cited by:

    1. Mehzabin Tuli, Farzana & Mitra, Suman & Crews, Mariah B., 2021. "Factors influencing the usage of shared E-scooters in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 164-185.
    2. Todd, James & O'Brien, Oliver & Cheshire, James, 2021. "A global comparison of bicycle sharing systems," Journal of Transport Geography, Elsevier, vol. 94(C).
    3. Mingyang Du & Lin Cheng, 2018. "Better Understanding the Characteristics and Influential Factors of Different Travel Patterns in Free-Floating Bike Sharing: Evidence from Nanjing, China," Sustainability, MDPI, vol. 10(4), pages 1-14, April.
    4. Hyland, Michael & Hong, Zihan & Pinto, Helen Karla Ramalho de Farias & Chen, Ying, 2018. "Hybrid cluster-regression approach to model bikeshare station usage," Transportation Research Part A: Policy and Practice, Elsevier, vol. 115(C), pages 71-89.
    5. Liu, Hung-Chi & Lin, Jen-Jia, 2019. "Associations of built environments with spatiotemporal patterns of public bicycle use," Journal of Transport Geography, Elsevier, vol. 74(C), pages 299-312.
    6. Yoo, Sunbin & Hong, Sungwan & Park, Yeongkyung & Okuyama, Akihiro & Zhang, Zhaozhe & Yoshida, Yoshikuni & Managi, Shunsuke, 2021. "Danger, Respect, and Indifference: Bike-Sharing Choices in Shanghai and Tokyo using Latent Choice Models," MPRA Paper 108312, University Library of Munich, Germany.
    7. Wang, Kailai & Akar, Gulsah, 2019. "Gender gap generators for bike share ridership: Evidence from Citi Bike system in New York City," Journal of Transport Geography, Elsevier, vol. 76(C), pages 1-9.
    8. Chen, Qun & Pan, Xiaoyi & Liu, Fang & Xiong, Yong & Li, Zhitao & Tang, Jinjun, 2022. "Reposition optimization in free-floating bike-sharing system: A case study in Shenzhen City," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    9. Kutela, Boniphace & Teng, Hualiang, 2019. "The influence of campus characteristics, temporal factors, and weather events on campuses-related daily bike-share trips," Journal of Transport Geography, Elsevier, vol. 78(C), pages 160-169.
    10. Xuefeng Li & Yong Zhang & Li Sun & Qiyang Liu, 2018. "Free-Floating Bike Sharing in Jiangsu: Users’ Behaviors and Influencing Factors," Energies, MDPI, vol. 11(7), pages 1-18, June.
    11. Yi-Wen Kuo & Cheng-Hsien Hsieh & Yu-Chen Hung, 2021. "Non-linear characteristics in switching intention to use a docked bike-sharing system," Transportation, Springer, vol. 48(3), pages 1459-1479, June.
    12. Wu, Xueying & Lu, Yi & Gong, Yongxi & Kang, Yuhao & Yang, Linchuan & Gou, Zhonghua, 2021. "The impacts of the built environment on bicycle-metro transfer trips: A new method to delineate metro catchment area based on people's actual cycling space," Journal of Transport Geography, Elsevier, vol. 97(C).
    13. Willberg, Elias & Salonen, Maria & Toivonen, Tuuli, 2021. "What do trip data reveal about bike-sharing system users?," Journal of Transport Geography, Elsevier, vol. 91(C).
    14. Smith, C. Scott & Schwieterman, Joseph P., 2021. "Using multivariate adaptive regression splining (MARS) to identify factors affecting the performance of dock-based bikesharing: The case of Chicago’s Divvy system," Research in Transportation Economics, Elsevier, vol. 89(C).
    15. Namkung, Ok Stella & Park, Jonghan & Ko, Joonho, 2023. "Public bike users’ annual travel distance: Findings from combined data of user survey and annual rental records," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    16. Liu, Hung-Chi & Lin, Jen-Jia, 2022. "Associations of built environments with spatiotemporal patterns of shared scooter use: A comparison with shared bike use," Transport Policy, Elsevier, vol. 126(C), pages 107-119.
    17. Narayanan, Santhanakrishnan & Antoniou, Constantinos, 2022. "Expansion of a small-scale car-sharing service: A multi-method framework for demand characterization and derivation of policy insights," Journal of Transport Geography, Elsevier, vol. 104(C).
    18. Lin, Jen-Jia & Wei, Yi-Hsuan, 2018. "Assessing area-wide bikeability: A grey analytic network process," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 381-396.
    19. Wang, Kailai & Akar, Gulsah & Chen, Yu-Jen, 2018. "Bike sharing differences among Millennials, Gen Xers, and Baby Boomers: Lessons learnt from New York City’s bike share," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 1-14.
    20. Song, Jie & Zhang, Liye & Qin, Zheng & Ramli, Muhamad Azfar, 2021. "Where are public bikes? The decline of dockless bike-sharing supply in Singapore and its resulting impact on ridership activities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 146(C), pages 72-90.
    21. Minutillo, M. & Forcina, A. & Jannelli, N. & Lubrano Lavadera, A., 2018. "Assessment of a sustainable energy chain designed for promoting the hydrogen mobility by means of fuel-cell powered bicycles," Energy, Elsevier, vol. 153(C), pages 200-210.
    22. 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.
    23. Narayanan, Santhanakrishnan & Antoniou, Constantinos, 2023. "Shared mobility services towards Mobility as a Service (MaaS): What, who and when?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).
    24. Zijia Wang & Lei Cheng & Yongxing Li & Zhiqiang Li, 2020. "Spatiotemporal Characteristics of Bike-Sharing Usage around Rail Transit Stations: Evidence from Beijing, China," Sustainability, MDPI, vol. 12(4), pages 1-19, February.
    25. Yi-Wen Kuo & Cheng-Hsien Hsieh & Yu-Chen Hung, 0. "Non-linear characteristics in switching intention to use a docked bike-sharing system," Transportation, Springer, vol. 0, pages 1-21.

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