Dynamic Matching for Real-Time Ride Sharing

Published Online:https://doi.org/10.1287/stsy.2019.0037

References

  • Aliprantis CD, Border KC (2006) Infinite Dimensional Analysis: A Hitchhiker’s Guide, 3rd ed. (Springer, Berlin).Google Scholar
  • Anderson EJ, Nash P, Perold AF (1983) Some properties of a class of continuous linear programs. SIAM J. Control Optim. 21(5):758–765.Google Scholar
  • Arnosti N, Johari R, Kanoria Y (2016) Managing congestion in matching markets. Working paper, Columbia University, New York. http://ssrn.com/abstract=2427960.Google Scholar
  • Ata B, Kumar S (2005) Heavy traffic analysis of open processing networks with complete resource pooling: Asymptotic optimality of discrete review policies. Ann. Appl. Probab. 15(1A):331–391.Google Scholar
  • Azevedo EM, Weyl EG (2016) Matching markets in the digital age. Science 352(6289):1056–1057.Google Scholar
  • Banerjee S, Freund D, Lykouris T (2016) Pricing and optimization in shared vehicle systems: An approximation framework. Preprint arXiv:1608.06819v3, submitted August 24, 2016, https://arxiv.org/abs/1608.06819v3.Google Scholar
  • Banerjee S, Kanoria Y, Qian P (2018) The value of state dependent control in ridesharing systems. Preprint arXiv:1803.04959v1, submitted March 13, https://arxiv.org/abs/1803.04959.Google Scholar
  • Bertsimas D, Nasrabadi E, Paschalidis IC (2015) Robust fluid processing networks. IEEE Trans. Automatic Control 60(3):715–728.Google Scholar
  • Besbes O, Castro F, Lobel I (2018) Surge pricing and its spatial supply response. Working paper, Columbia University, New York. https://ssrn.com/abstract=3124571.Google Scholar
  • Billingsley P (1999) Convergence of Probability Measures, 2nd ed. (Wiley, Hoboken, NJ).Google Scholar
  • Bimpikis K, Candoğan O, Saban D (2019) Spatial pricing in ride-sharing networks. Oper. Res. 67(3):744–769.Google Scholar
  • Braverman A, Dai JG, Liu X, Ying L (2016) Empty-car routing in ridesharing systems. Preprint arXiv:1609.07219v3, submitted September 23, https://arxiv.org/abs/1609.07219v3.Google Scholar
  • Cachon GP, Daniels KM, Lobel R (2017) The role of surge pricing on a service platform with self-scheduling capacity. Manufacturing Service Oper. Management 19(3):368–384.LinkGoogle Scholar
  • Castillo JC, Knoepfle DT, Weyl EG (2016) Surge pricing solves the wild goose chase. Working paper, Stanford University, Stanford, CA. https://ssrn.com/abstract=2890666.Google Scholar
  • Chaudhari HA, Byers JW, Terzi E (2018) Putting data in the driver’s seat: Optimizing earnings for on-demand ride-hailing. Proc. 11th ACM Internat. Conf. Web Search Data Mining (Association for Computing Machinery, New York), 90–98.Google Scholar
  • Chen K, Sheldon M (2015) Dynamic pricing in a labor market: Surge pricing and flexible work on the Uber platform. Working paper, University of California, Los Angeles, Los Angeles.Google Scholar
  • Chen L, Mislove A, Wilson C (2015) Peeking beneath the hood of Uber. Proc. 2015 Internet Measurement Conf. (Association for Computing Machinery, New York), 495–508.Google Scholar
  • Chen H, Yao DD (2001) Fundamentals of Queueing Networks: Performance, Asymptotics, and Optimization (Springer, Berlin).Google Scholar
  • Dai JG, Tezcan T (2011) State space collapse in many-server diffusion limits of parallel server systems. Math. Oper. Res. 36(2):271–320.LinkGoogle Scholar
  • de Jong RM (1996) A strong law of large numbers for triangular mixingale arrays. Statist. Probab. Lett. 27(1):1–9.Google Scholar
  • Durrett R (2010) Probability: Theory and Examples, 4th ed. (Cambridge University Press, Cambridge, UK).Google Scholar
  • Economist, The (2016) A fare shake. The Economist (May 14), http://www.economist.com/news/finance-and-economics/21698656-jacking-up-prices-may-not-be-only-way-balance-supply-and-demand-taxis.Google Scholar
  • Folland GB (1999) Real Analysis Modern Techniques and Their Applications, 2nd ed. (Wiley, Hoboken, NJ).Google Scholar
  • Gopalakrishnan R, Mukherjee K, Tulabandhula T (2016) The costs and benefits of ridesharing: Sequential individual rationality and sequential fairness. Preprint, arXiv:1607.07306v2, submitted July 25, https://arxiv.org/abs/1607.07306v2.Google Scholar
  • Guda H, Subramanian U (2019) Your Uber is arriving: Managing on-demand workers through surge pricing, forecast communication, and worker incentives. Management Sci. 65(5):1995–2014.AbstractGoogle Scholar
  • Gurvich I, Ward A (2014) On the dynamic control of matching queues. Stochastic Systems 4(2):479–523.LinkGoogle Scholar
  • Hall J, Kendrick C, Nosko C (2016) The effects of Uber’s surge pricing: A case study. Working paper, Uber, San Francisco.Google Scholar
  • Harrison JM (2000) Brownian models of open processing networks: Canonical representation of workload. Ann. Appl. Probab. 10(1):75–103.Google Scholar
  • Hu M, Zhou Y (2015) Dynamic type matching. Working paper, University of Toronto, Toronto. http://ssrn.com/abstract=2592622.Google Scholar
  • Kallenberg O (1997) Foundations of Modern Probability (Springer, Berlin).Google Scholar
  • Karatzas I, Shreve SE (1988) Brownian Motion and Stochastic Calculus, 1st ed. (Springer-Verlag, Berlin).Google Scholar
  • Leshno JD (2016) Dynamic matching in overloaded waiting lists. Working paper, Columbia University, New York.Google Scholar
  • Levinson N (1966) A class of continuous linear programming problems. J. Math. Anal. Appl. 16(1):7–83.Google Scholar
  • Michallon C (2016) “Shame on you”: Uber comes under fire as users complain of price surge in the aftermath of Manhattan explosion—with rides costing up to three times their usual prices. Daily Mail (September 18), http://www.dailymail.co.uk/news/article-3795474/Uber-comes-fire-users-complain-price-surge-aftermath-Manhattan-explosion.html.Google Scholar
  • Nikzad A (2017) Thickness and competition in ride-sharing markets. Working paper, University of Southern California, Los Angeles.Google Scholar
  • Özkan E (2018) Joint pricing and matching in ridesharing systems. Working paper, Koç University, Istanbul. https://ssrn.com/abstract=3217642.Google Scholar
  • Perold AF (1981) Extreme points and basic feasible solutions in continuous time linear programming. SIAM J. Control Optim. 19(1):52–63.Google Scholar
  • Plambeck EL, Ward AR (2006) Optimal control of a high-volume assemble-to-order system. Math. Oper. Res. 31(3):453–477.LinkGoogle Scholar
  • Reed JE, Ward AR (2004) A diffusion approximation for a generalized Jackson network with reneging. Proc. 42nd Annual Allerton Conf. Comm. Control Comput. (University of Illinois, Monticello), 983–995.Google Scholar
  • Reed JE, Ward AR (2008) Approximating the GI/GI/1+GI queue with a nonlinear drift diffusion: Hazard rate scaling in heavy traffic. Math. Oper. Res. 33(3):606–644.LinkGoogle Scholar
  • Reiman MI, Wang Q (2015) Asymptotically optimal inventory control for assemble-to-order systems with identical lead times. Oper. Res. 63(3):716–732.LinkGoogle Scholar
  • Riquelme C, Banerjee S, Johari R (2015) Pricing in ride-share platforms: A queueing-theoretic approach. Working paper, Google, Mountain View, CA. http://ssrn.com/abstract=2568258.Google Scholar
  • Ross SM (1996) Stochastic Processes, 2nd ed. (Wiley, Hoboken, NJ).Google Scholar
  • Royden HL, Fitzpatrick PM (2010) Real Analysis, 4th ed. (Pearson, London).Google Scholar
  • Shiryaev AN (2008) Optimal Stopping Rules [reprint of the 1978 ed.] (Springer, Berlin).Google Scholar
  • Stroock DW, Varadhan SRS (2006) Multidimensional Diffusion Processes [reprint of the 1997 ed.] (Springer, Berlin).Google Scholar
  • Ünver MU (2010) Dynamic kidney exchange. Rev. Econom. Stud. 77(1):372–414.Google Scholar
  • Ward AR, Kumar S (2008) Asymptotically optimal admission control of a queue with impatient customers. Math. Oper. Res. 33(1):167–202.LinkGoogle Scholar
  • Weiss G (2008) A simplex based algorithm to solve separated continuous linear programs. Math. Programming 115(1):151–198.Google Scholar
  • White D (2016) Uber users are complaining about pricey New Year’s Eve rides. Time (January 1), http://time.com/4165410/uber-new-years-eve-price-surge-rides/.Google Scholar
  • Whitt W (2002) Stochastic-Process Limits: An Introduction to Stochastic-Process Limits and Their Application to Queues (Springer, Berlin).Google Scholar
  • Zhong Y, Wan Z, Shen ZJM (2019) Balancing supply and demand: Queuing vs. surge pricing mechanisms. Working paper, University of Chicago, Chicago.Google Scholar
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