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

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2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)317-332
Number of pages16
JournalEuropean Journal of Operational Research
Volume277
Issue number1
DOIs
StatePublished - Aug 16 2019

Fingerprint

Pickups
Censoring
Sharing
Docks
Simulation
Customers
Simulation Experiment
Cleaning
Decision making
Experiments
Valid
Nonparametric Bootstrap
Bootstrap Test
Subset Selection
Statistical Power
Heavy-tailed Distribution
Time of Arrival
Demand
Simulation-based estimation
Arrival Time

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Cite this

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title = "Simulation-based estimation of the real demand in bike-sharing systems in the presence of censoring",
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.",
author = "Ashkan Negahban",
year = "2019",
month = "8",
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doi = "10.1016/j.ejor.2019.02.013",
language = "English (US)",
volume = "277",
pages = "317--332",
journal = "European Journal of Operational Research",
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