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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
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    References listed on IDEAS

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    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. Zhang, Yongping & Mi, Zhifu, 2018. "Environmental benefits of bike sharing: A big data-based analysis," Applied Energy, Elsevier, vol. 220(C), pages 296-301.
    3. 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).
    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. 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.
    6. 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.
    7. Buehler, Ralph & Pucher, John, 2011. "Making public transport financially sustainable," Transport Policy, Elsevier, vol. 18(1), pages 126-138, January.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
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