A Locational Demand Model for Bike-Sharing

References

• Abdallah T, Vulcano G (2020) Demand estimation under the multinomial logit model from sales transaction data. Manufacturing Service Oper. Management 23(5):1196–1216.Link, Google Scholar
• Anupindi R, Dada M, Gupta S (1998) Estimation of consumer demand with stock-out based substitution: An application to vending machine products. Marketing Sci. 17(4):406–423.Link, Google Scholar
• Banerjee S, Freund D, Lykouris T (2022) Pricing and optimization in shared vehicle systems: An approximation framework. Oper. Res. 70(3):1783–1805.Link, Google Scholar
• Bhat CR (1997) An endogenous segmentation mode choice model with an application to intercity travel. Transportation Sci. 31(1):34–48.Link, Google Scholar
• Bureau of Transportation Statistics (2024) Bikeshare and E-scooter systems in the U.S. Accessed August 28, https://data.bts.gov/stories/s/Bikeshare-and-e-scooters-in-the-U-S-/fwcs-jprj/.Google Scholar
• Cho S, Ferguson M, Pekgün P, Vakhutinsky A (2023) Estimating personalized demand with unobserved no-purchases using a mixture model: An application in the hotel industry. Manufacturing Service Oper. Management 25(4):1245–1262.Link, Google Scholar
• Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. Ser. B (Methodological) 39(1):1–38.Crossref, Google Scholar
• El-Assi W, Mahmoud MS, Habib KN (2017) Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation 44(3):589–613.Crossref, Google Scholar
• Frank M, Wolfe P (1956) An algorithm for quadratic programming. Naval Res. Logist. Quart. 3(1–2):95–110.Crossref, Google Scholar
• Freund D, Henderson SG, Shmoys DB (2019a) Bike sharing. Hu M, ed. Sharing Economy: Making Supply Meet Demand. Springer Series in Supply Chain Management, vol. 6 (Springer, Cham, Switzerland), 435–459.Crossref, Google Scholar
• Freund D, Henderson SG, O’Mahony E, Shmoys DB (2019b) Analytics and bikes: Riding tandem with motivate to improve mobility. INFORMS J. Appl. Analytics 49(5):310–323.Link, Google Scholar
• Gaur V, Honhon D (2006) Assortment planning and inventory decisions under a locational choice model. Management Sci. 52(10):1528–1543.Google Scholar
• Greene WH, Hensher DA (2003) A latent class model for discrete choice analysis: Contrasts with mixed logit. Transportation Res. Part B Methodological 37(8):681–698.Crossref, Google Scholar
• Grün B, Leisch F (2008) Identifiability of finite mixtures of multinomial logit models with varying and fixed effects. J. Classification 25(2):225–247.Crossref, Google Scholar
• He P, Zheng F, Belavina E, Girotra K (2021) Customer preference and station network in the London bike-share system. Management Sci. 67(3):1392–1412.Link, Google Scholar
• Hotelling H (1929) Stability in competition. Econom. J. 39(153):41–57.Google Scholar
• Hunter DR, Lange K (2004) A tutorial on MM algorithms. Amer. Statist. 58(1):30–37.Crossref, Google Scholar
• Jagabathula S, Subramanian L, Venkataraman A (2020) A conditional gradient approach for nonparametric estimation of mixing distributions. Management Sci. 66(8):3635–3656.Link, Google Scholar
• Kabra A, Belavina E, Girotra K (2019) Bike-share systems: Accessibility and availability. Management Sci. 66(9):3803–3824.Link, Google Scholar
• Lancaster KJ (1966) A new approach to consumer theory. J. Political Econom. 74(2):132–157.Google Scholar
• Lancaster K (1975) Socially optimal product differentiation. Amer. Econom. Rev. 65(4):567–585.Google Scholar
• Luo X, Li L, Zhao L, Lin J (2022) Dynamic intra-cell repositioning in free-floating bike-sharing systems using approximate dynamic programming. Transportation Sci. 56(4):799–826.Link, Google Scholar
• McFadden D, Train K (2000) Mixed MNL models for discrete response. J. Appl. Econometrics 15(5):447–470.Crossref, Google Scholar
• Newman JP, Ferguson ME, Garrow LA, Jacobs TL (2014) Estimation of choice-based models using sales data from a single firm. Manufacturing Service Oper. Management 16(2):184–197.Link, Google Scholar
• O’Mahony E (2015) Smarter tools for (Citi) bike sharing. PhD thesis, Cornell University, Ithaca, NY.Google Scholar
• O’Mahony E, Shmoys DB (2015) Data analysis and optimization for (Citi)bike sharing. Proc. 29th AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 687–694.Google Scholar
• Open Mobility Foundation (2022) Mobility-data-specification: A data standard to enable right-of-way regulation and two-way communication between mobility companies and local governments. Accessed November 29, https://github.com/openmobilityfoundation/mobility-data-specification/.Google Scholar
• Paul A, Flynn K, Overney C (2023) Estimating censored spatial-temporal demand with applications to shared micromobility. Preprint, submitted March 17, https://arxiv.org/abs/2303.09971.Google Scholar
• Rixey RA (2013) Station-level forecasting of bikesharing ridership: Station network effects in three U.S. systems. Transportation Res. Record 2387(1):46–55.Crossref, Google Scholar
• Seattle Department of Transportation (2022) Scooter share data & permitting. Accessed May 14, 2023, https://www.seattle.gov/transportation/projects-and-programs/programs/new-mobility-program/scooter-share.Google Scholar
• Singhvi D, Singhvi S, Frazier PI, Henderson SG, O’Mahony E, Shmoys DB, Woodard DB (2015) Predicting bike usage for New York City’s bike sharing system. AAAI Workshop Comput. Sustainability (AAAI, Washington, DC), 110–114.Google Scholar
• Steeneck D, Eng-Larsson F, Jauffred F (2022) Estimating lost sales for substitutable products with uncertain on-shelf availability. Manufacturing Service Oper. Management 24(3):1578–1594.Link, Google Scholar
• Talluri K, van Ryzin G (2004) Revenue management under a general discrete choice model of consumer behavior. Management Sci. 50(1):15–33.Link, Google Scholar
• van der Vaart AW (2000) Asymptotic Statistics, vol. 3 (Cambridge University Press, Cambridge, UK).Google Scholar
• van Ryzin G, Vulcano G (2014) A market discovery algorithm to estimate a general class of nonparametric choice models. Management Sci. 61(2):281–300.Link, Google Scholar
• Vulcano G, Van Ryzin G, Ratliff R (2012) Estimating primary demand for substitutable products from sales transaction data. Oper. Res. 60(2):313–334.Link, Google Scholar
• Wu CF (1983) On the convergence properties of the EM algorithm. Ann. Statist. 11(1):95–103.Crossref, Google Scholar
• Yuille AL, Rangarajan A (2003) The concave-convex procedure. Neural Comput. 15(4):915–936.Crossref, Google Scholar