Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 141-145). / In the design and operation of urban mobility systems, it is often desirable to understand patterns in traveler demand. However, demand is typically unobserved and must be estimated from available data. To address this disconnect, we begin by proposing a method for recovering an unknown probability distribution given a censored or truncated sample from that distribution. The proposed method is a novel and conceptually simple detruncation technique 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 about the unknown distribution is available but the truncation process can be simulated. / The proposed method is compared to the ubiquitous maximum likelihood (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 slight misspecified MLE. We then describe a novel car-sharing simulator which captures many of the important interactions between supply, demand, and system utilization while remaining simple and computationally efficient. In collaboration with Zipcar, a leading car-sharing operator in the United States, we demonstrate the usefulness of our detruncation method combined with our simulator via a pair of case studies. These tools allow us to estimate demand for round trip car-sharing services in the Boston and New York metropolitan areas, and the inferred demand distributions contain actionable insights. / Finally, we extend the detruncation method to cover cases where data is noisy, missing, or must be combined from different sources such as web or mobile applications. In synthetic validation experiments, the extended method is benchmarked against kernel density estimation (KDE) with Gaussian kernels. We find that the proposed method typically outperforms KDE, especially when the distribution to be estimated is not unimodal. With this extended method we consider the added utility of search data when estimating demand for car-sharing. / by Evan Fields. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/121825 |
| Date | January 2019 |
| Creators | Fields, Evan(Evan Jerome) |
| Contributors | Carolina Osorio., Massachusetts Institute of Technology. Operations Research Center., Massachusetts Institute of Technology. Operations Research Center |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 145 pages, application/pdf |
| Rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission., http://dspace.mit.edu/handle/1721.1/7582 |
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