IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i8p3230-d1374607.html
   My bibliography  Save this article

Interpretable Bike-Sharing Activity Prediction with a Temporal Fusion Transformer to Unveil Influential Factors: A Case Study in Hamburg, Germany

Author

Listed:
  • Sebastian Rühmann

    (Department of Computer Science, Human-Computer Interaction, University of Hamburg, 22527 Hamburg, Germany)

  • Stephan Leible

    (Department of Computer Science, IT-Management and -Consulting, University of Hamburg, 22527 Hamburg, Germany)

  • Tom Lewandowski

    (Department of Computer Science, IT-Management and -Consulting, University of Hamburg, 22527 Hamburg, Germany)

Abstract

Bike-sharing systems (BSS) have emerged as an increasingly important form of transportation in smart cities, playing a pivotal role in the evolving landscape of urban mobility. As cities worldwide strive to promote sustainable and efficient transportation options, BSS offer a flexible, eco-friendly alternative that complements traditional public transport systems. These systems, however, are complex and influenced by a myriad of endogenous and exogenous factors. This complexity poses challenges in predicting BSS activity and optimizing its usage and effectiveness. This study delves into the dynamics of the BSS in Hamburg, Germany, focusing on system stability and activity prediction. We propose an interpretable attention-based Temporal Fusion Transformer (TFT) model and compare its performance with the state-of-the-art Long Short-Term Memory (LSTM) model. The proposed TFT model outperforms the LSTM model with a 36.8% improvement in RMSE and overcomes current black-box models via interpretability. Via detailed analysis, key factors influencing bike-sharing activity, especially in terms of temporal and spatial contexts, are identified, examined, and evaluated. Based on the results, we propose interventions and a deployed TFT model that can improve the effectiveness of BSS. This research contributes to the evolving field of sustainable urban mobility via data analysis for data-informed decision-making.

Suggested Citation

  • Sebastian Rühmann & Stephan Leible & Tom Lewandowski, 2024. "Interpretable Bike-Sharing Activity Prediction with a Temporal Fusion Transformer to Unveil Influential Factors: A Case Study in Hamburg, Germany," Sustainability, MDPI, vol. 16(8), pages 1-32, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3230-:d:1374607
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/8/3230/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/8/3230/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Böcker, Lars & Anderson, Ellinor & Uteng, Tanu Priya & Throndsen, Torstein, 2020. "Bike sharing use in conjunction to public transport: Exploring spatiotemporal, age and gender dimensions in Oslo, Norway," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 389-401.
    2. Giuffrida, Nadia & Pilla, Francesco & Carroll, Páraic, 2023. "The social sustainability of cycling: Assessing equity in the accessibility of bike-sharing services," Journal of Transport Geography, Elsevier, vol. 106(C).
    3. Hamilton, Timothy L. & Wichman, Casey J., 2018. "Bicycle infrastructure and traffic congestion: Evidence from DC's Capital Bikeshare," Journal of Environmental Economics and Management, Elsevier, vol. 87(C), pages 72-93.
    4. Jessica Schoner & David Levinson, 2014. "The missing link: bicycle infrastructure networks and ridership in 74 US cities," Transportation, Springer, vol. 41(6), pages 1187-1204, November.
    5. Martin, Elliot PhD & Shaheen, Susan PhD, 2014. "Evaluating Public Transit Modal Shift Dynamics In Response to Bikesharing: A Tale of Two U.S. Cities," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6x29n876, Institute of Transportation Studies, UC Berkeley.
    6. Wang, Mingshu & Zhou, Xiaolu, 2017. "Bike-sharing systems and congestion: Evidence from US cities," Journal of Transport Geography, Elsevier, vol. 65(C), pages 147-154.
    7. Kim, Kyoungok, 2018. "Investigation on the effects of weather and calendar events on bike-sharing according to the trip patterns of bike rentals of stations," Journal of Transport Geography, Elsevier, vol. 66(C), pages 309-320.
    8. Kyle Gebhart & Robert Noland, 2014. "The impact of weather conditions on bikeshare trips in Washington, DC," Transportation, Springer, vol. 41(6), pages 1205-1225, November.
    9. Buehler, Ralph & Pucher, John, 2011. "Making public transport financially sustainable," Transport Policy, Elsevier, vol. 18(1), pages 126-138, January.
    10. Lu Cheng & Zhifu Mi & D’Maris Coffman & Jing Meng & Dining Liu & Dongfeng Chang, 2022. "The Role of Bike Sharing in Promoting Transport Resilience," Networks and Spatial Economics, Springer, vol. 22(3), pages 567-585, September.
    11. Zhang, Yongping & Mi, Zhifu, 2018. "Environmental benefits of bike sharing: A big data-based analysis," Applied Energy, Elsevier, vol. 220(C), pages 296-301.
    Full references (including those not matched with items on IDEAS)

    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. Hyungkyoo Kim, 2020. "Seasonal Impacts of Particulate Matter Levels on Bike Sharing in Seoul, South Korea," IJERPH, MDPI, vol. 17(11), pages 1-17, June.
    2. Xie, Xiao-Feng & Wang, Zunjing Jenipher, 2018. "Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the Washington DC area," Journal of Transport Geography, Elsevier, vol. 71(C), pages 84-102.
    3. Zhang, Xiang & Li, Wence, 2023. "Effects of a bike sharing system and COVID-19 on low-carbon traffic modal shift and emission reduction," Transport Policy, Elsevier, vol. 132(C), pages 42-64.
    4. 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.
    5. Yi Zhu, 2022. "Can bicycle sharing mitigate vehicle emission in Chinese large cities? Estimation based on mode shift analysis," Transportation, Springer, vol. 49(6), pages 1627-1648, December.
    6. Zhou, Xiaolu & Wang, Mingshu & Li, Dongying, 2019. "Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    7. Shang, Wen-Long & Chen, Jinyu & Bi, Huibo & Sui, Yi & Chen, Yanyan & Yu, Haitao, 2021. "Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis," Applied Energy, Elsevier, vol. 285(C).
    8. Kwiatkowski Michał Adam, 2018. "Urban Cycling as an Indicator of Socio-Economic Innovation and Sustainable Transport," Quaestiones Geographicae, Sciendo, vol. 37(4), pages 23-32, December.
    9. Kim, Kyoungok, 2023. "Investigation of modal integration of bike-sharing and public transit in Seoul for the holders of 365-day passes," Journal of Transport Geography, Elsevier, vol. 106(C).
    10. Yuanyuan Zhang & Yuming Zhang, 2018. "Associations between Public Transit Usage and Bikesharing Behaviors in The United States," Sustainability, MDPI, vol. 10(6), pages 1-20, June.
    11. Hu, Beibei & Zhong, Zhenfang & Zhang, Yanli & Sun, Yue & Jiang, Li & Dong, Xianlei & Sun, Huijun, 2022. "Understanding the influencing factors of bicycle-sharing demand based on residents’ trips," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    12. Younes, Hannah & Zou, Zhenpeng & Wu, Jiahui & Baiocchi, Giovanni, 2020. "Comparing the Temporal Determinants of Dockless Scooter-share and Station-based Bike-share in Washington, D.C," Transportation Research Part A: Policy and Practice, Elsevier, vol. 134(C), pages 308-320.
    13. Gu, Tianqi & Kim, Inhi & Currie, Graham, 2019. "Measuring immediate impacts of a new mass transit system on an existing bike-share system in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 20-39.
    14. Shr, Yau-Huo (Jimmy) & Yang, Feng-An & Chen, Yi-Syun, 2023. "The housing market impacts of bicycle-sharing systems," Regional Science and Urban Economics, Elsevier, vol. 98(C).
    15. 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).
    16. Gao, Kun & Yang, Ying & Li, Aoyong & Li, Junhong & Yu, Bo, 2021. "Quantifying economic benefits from free-floating bike-sharing systems: A trip-level inference approach and city-scale analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 89-103.
    17. Li, Chunzhi & Xiao, Wei & Zhang, Dayong & Ji, Qiang, 2021. "Low-carbon transformation of cities: Understanding the demand for dockless bike sharing in China," Energy Policy, Elsevier, vol. 159(C).
    18. Choi, Seung Jun & Jiao, Junfeng & Lee, Hye Kyung & Farahi, Arya, 2023. "Combatting the mismatch: Modeling bike-sharing rental and return machine learning classification forecast in Seoul, South Korea," Journal of Transport Geography, Elsevier, vol. 109(C).
    19. Yong Lei & Jun Zhang & Zhihua Ren, 2023. "A Study on Bicycle-Sharing Dispatching Station Site Selection and Planning Based on Multivariate Data," Sustainability, MDPI, vol. 15(17), pages 1-25, August.
    20. Ma, Xinwei & Ji, Yanjie & Yuan, Yufei & Van Oort, Niels & Jin, Yuchuan & Hoogendoorn, Serge, 2020. "A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 148-173.

    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:16:y:2024:i:8:p:3230-:d:1374607. 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.