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Simulation-based estimation of the real demand in bike-sharing systems in the presence of censoring

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  • Negahban, Ashkan

Abstract

Data on successful bike pickups/drop-offs censor the demand from customers/riders that were unable to pickup/drop-off a bike due to bike/dock unavailability (i.e., balks). The objective of this paper is two-fold: (1) provide a formal comparison between the distribution of satisfied bike/dock demand and the true (latent) demand in bike-sharing systems through simulation experiments and nonparametric bootstrap tests to show when and how the two may differ; and, (2) propose a novel methodology combining simulation, bootstrapping, and subset selection that harnesses the useful partial information in every bike pickup/drop-off observation (even if it is subject to censoring) to estimate the true demand in situations where data filtering/cleaning approaches commonly used in the bike-sharing literature fail due to lack of valid data. The results reveal that the distribution of inter-pickup/drop-off times may differ (statistically) from the distribution of the actual inter-arrival time of customers/bikes primarily for higher percentile values and even if the demand rate is slower than the supply rate, especially if customer/bike inter-arrival times follow a heavy-tailed distribution. The statistical power of the proposed demand estimation approach in identifying an appropriate model for the underlying demand distribution is tested through simulation experiments as well as a real-world application. The paper has important academic and practical impacts by providing additional means to obtain and use statistically valid demand estimates, enhancing decision-making related to the design and operation of bike-sharing systems.

Suggested Citation

  • Negahban, Ashkan, 2019. "Simulation-based estimation of the real demand in bike-sharing systems in the presence of censoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 317-332.
  • Handle: RePEc:eee:ejores:v:277:y:2019:i:1:p:317-332
    DOI: 10.1016/j.ejor.2019.02.013
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    References listed on IDEAS

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    1. Tal Raviv & Ofer Kolka, 2013. "Optimal inventory management of a bike-sharing station," IISE Transactions, Taylor & Francis Journals, vol. 45(10), pages 1077-1093.
    2. Chao Wang & Kung-Sik Chan, 2018. "Quasi-Likelihood Estimation of a Censored Autoregressive Model With Exogenous Variables," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1135-1145, July.
    3. Barry L. Nelson, 2013. "Foundations and Methods of Stochastic Simulation," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-6160-9, December.
    4. Legros, Benjamin, 2019. "Dynamic repositioning strategy in a bike-sharing system; how to prioritize and how to rebalance a bike station," European Journal of Operational Research, Elsevier, vol. 272(2), pages 740-753.
    5. Christine Fricker & Nicolas Gast, 2016. "Incentives and redistribution in homogeneous bike-sharing systems with stations of finite capacity," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(3), pages 261-291, August.
    6. Mor Kaspi & Tal Raviv & Michal Tzur, 2017. "Bike-sharing systems: User dissatisfaction in the presence of unusable bicycles," IISE Transactions, Taylor & Francis Journals, vol. 49(2), pages 144-158, February.
    7. A. Negahban & J.S. Smith, 2016. "The effect of supply and demand uncertainties on the optimal production and sales plans for new products," International Journal of Production Research, Taylor & Francis Journals, vol. 54(13), pages 3852-3869, July.
    8. Van Zuylen, Henk J. & Willumsen, Luis G., 1980. "The most likely trip matrix estimated from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 281-293, September.
    9. Marie Laure Delignette-Muller & Christophe Dutang, 2015. "fitdistrplus : An R Package for Fitting Distributions," Post-Print hal-01616147, HAL.
    10. Bulhões, Teobaldo & Subramanian, Anand & Erdoğan, Güneş & Laporte, Gilbert, 2018. "The static bike relocation problem with multiple vehicles and visits," European Journal of Operational Research, Elsevier, vol. 264(2), pages 508-523.
    11. Schuijbroek, J. & Hampshire, R.C. & van Hoeve, W.-J., 2017. "Inventory rebalancing and vehicle routing in bike sharing systems," European Journal of Operational Research, Elsevier, vol. 257(3), pages 992-1004.
    12. Jia Shu & Mabel C. Chou & Qizhang Liu & Chung-Piaw Teo & I-Lin Wang, 2013. "Models for Effective Deployment and Redistribution of Bicycles Within Public Bicycle-Sharing Systems," Operations Research, INFORMS, vol. 61(6), pages 1346-1359, December.
    13. Justin Boesel & Barry L. Nelson & Seong-Hee Kim, 2003. "Using Ranking and Selection to “Clean Up” after Simulation Optimization," Operations Research, INFORMS, vol. 51(5), pages 814-825, October.
    14. Samarth J. Patel & Robin Qiu & Ashkan Negahban, 2019. "Incentive-Based Rebalancing of Bike-Sharing Systems," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu (ed.), Advances in Service Science, pages 21-30, Springer.
    15. Haider, Zulqarnain & Nikolaev, Alexander & Kang, Jee Eun & Kwon, Changhyun, 2018. "Inventory rebalancing through pricing in public bike sharing systems," European Journal of Operational Research, Elsevier, vol. 270(1), pages 103-117.
    16. Elliot Fishman & Simon Washington & Narelle Haworth, 2013. "Bike Share: A Synthesis of the Literature," Transport Reviews, Taylor & Francis Journals, vol. 33(2), pages 148-165, March.
    17. Delignette-Muller, Marie Laure & Dutang, Christophe, 2015. "fitdistrplus: An R Package for Fitting Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i04).
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