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A Data-Driven Method for Reconstructing a Distribution from a Truncated Sample with an Application to Inferring Car-Sharing Demand

Author

Listed:
  • Evan Fields

    (Zoba Inc., Boston, Massachusetts 02118)

  • Carolina Osorio

    (Department of Decision Sciences, HEC Montreal, Montreal, Quebec H3T 2A7, Canada)

  • Tianli Zhou

    (Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

This paper proposes a method to recover an unknown probability distribution given a censored or truncated sample from that distribution. The proposed method is a novel and conceptually simple detruncation method based on sampling the observed data according to weights learned by solving a simulation-based optimization problem; this method is especially appropriate in cases where little analytic information is available but the truncation process can be simulated. The proposed method is compared with the ubiquitous maximum likelihood estimation (MLE) method in a variety of synthetic validation experiments, where it is found that the proposed method performs slightly worse than perfectly specified MLE and competitively with slightly misspecified MLE. The practical application of this method is then demonstrated via a pair of case studies in which the proposed detruncation method is used alongside a car-sharing service simulator to estimate demand for round-trip car-sharing services in the Boston and New York metropolitan areas.

Suggested Citation

  • Evan Fields & Carolina Osorio & Tianli Zhou, 2021. "A Data-Driven Method for Reconstructing a Distribution from a Truncated Sample with an Application to Inferring Car-Sharing Demand," Transportation Science, INFORMS, vol. 55(3), pages 616-636, May.
    • Handle: RePEc:inm:ortrsc:v:55:y:2021:i:3:p:616-636
    DOI: 10.1287/trsc.2020.1028
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    References listed on IDEAS

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    Cited by:

    1. Miwa, Tomio & Wang, Jianbiao & Morikawa, Takayuki, 2023. "Are seniors in mountainous areas able to realize their desired trips? A novel approach to estimate trip demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    2. Zhou, Tianli & Fields, Evan & Osorio, Carolina, 2023. "A data-driven discrete simulation-based optimization algorithm for car-sharing service design," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    3. Er-Rahmadi, Btissam & Ma, Tiejun, 2022. "Data-driven mixed-Integer linear programming-based optimisation for efficient failure detection in large-scale distributed systems," European Journal of Operational Research, Elsevier, vol. 303(1), pages 337-353.
    4. Wang, Jianbiao & Miwa, Tomio & Morikawa, Takayuki, 2023. "Recursive decomposition probability model for demand estimation of street-hailing taxis utilizing GPS trajectory data," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 171-195.

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