Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and the Value of Dynamic Pricing

Published Online:https://doi.org/10.1287/opre.2022.2425

References

  • Afeche P, Liu Z, Maglaras C (2018) Ride-hailing networks with strategic drivers: The impact of platform control capabilities on performance. Working paper, Columbia Business School, Columbia University, New York.Google Scholar
  • Akturk D, Candogan O, Gupta V (2021) Network inventory management: Approximate optimality in large-scale systems. Preprint, submitted May 21, http://dx.doi.org/10.2139/ssrn.3842817.Google Scholar
  • Arlotto A, Gurvich I (2019) Uniformly bounded regret in the multisecretary problem. Stochastic Systems 9(3):231–260.LinkGoogle Scholar
  • Ata B, Barjesteh N, Kumar S (2019) Spatial pricing: An empirical analysis of taxi rides in New York City. Working paper, University of Chicago, Booth Business School, Chicago.Google Scholar
  • Bai J, So KC, Tang CS, Chen X, Wang H (2019) Coordinating supply and demand on an on-demand service platform with impatient customers. Manufacturing Service Oper. Management 21(3):556–570.LinkGoogle Scholar
  • Balseiro SR, Brown DB, Chen C (2021) Dynamic pricing of relocating resources in large networks. Management Sci. 67(7):4075–4094.LinkGoogle Scholar
  • Banerjee S, Freund D, Lykouris T (2021) Pricing and optimization in shared vehicle systems: An approximation framework. Oper. Res. 70(3):1783–1805.LinkGoogle Scholar
  • Banerjee S, Kanoria Y, Qian P (2018) State dependent control of closed queueing networks with application to ride-hailing. Preprint, submitted March 13, https://arxiv.org/abs/1803.04959.Google Scholar
  • Benjaafar S, Wang Z, Yang X (2021) Autonomous vehicles for ride-hailing. Preprint, submitted September 10, http://dx.doi.org/10.2139/ssrn.3919411.Google Scholar
  • Besbes O, Castro F, Lobel I (2021a) Spatial capacity planning. Oper. Res. 70(2):1271–1291.LinkGoogle Scholar
  • Besbes O, Castro F, Lobel I (2021b) Surge pricing and its spatial supply response. Management Sci. 67(3):1350–1367.LinkGoogle Scholar
  • Besbes O, Elmachtoub AN, Sun Y (2019) Static pricing: Universal guarantees for reusable resources. Proc. 2019 ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 393–394.Google Scholar
  • Bimpikis K, Candogan O, Saban D (2019) Spatial pricing in ride-sharing networks. Oper. Res. 67(3):744–769.LinkGoogle Scholar
  • Braverman A, Dai JG, Liu X, Ying L (2019) Empty-car routing in ridesharing systems. Oper. Res. 67(5):1437–1452.LinkGoogle Scholar
  • Buchholz N (2022) Spatial equilibrium, search frictions, and dynamic efficiency in the taxi industry. Rev. Econom. Stud. 89(2):556–591.CrossrefGoogle Scholar
  • Bumpensanti P, Wang H (2020) A re-solving heuristic with uniformly bounded loss for network revenue management. Management Sci. 66(7):2993–3009.LinkGoogle 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 D, Weyl G (2017) Surge pricing solves the wild goose chase. Proc. 2017 ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 241–242.Google Scholar
  • Chen Q, Jasin S, Duenyas I (2015) Real-time dynamic pricing with minimal and flexible price adjustment. Management Sci. 62(8):2437–2455.LinkGoogle Scholar
  • Chen Y, Hu M (2019) Pricing and matching with forward-looking buyers and sellers. Manufacturing Service Oper. Management 22(4):717–734.LinkGoogle Scholar
  • Chen Y, Levi R, Shi C (2017) Revenue management of reusable resources with advanced reservations. Production Oper. Management 26(5):836–859.CrossrefGoogle Scholar
  • City of New York (2020) TLC trip record data. https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page.Google Scholar
  • Fang Z, Huang L, Wierman A (2019) Prices and subsidies in the sharing economy. Performance Evaluation 136:102037.CrossrefGoogle Scholar
  • Feng Y, Niazadeh R, Saberi A (2019) Linear programming based online policies for real-time assortment of reusable resources. Preprint, submitted July 17, http://dx.doi.org/10.2139/ssrn.3421227.Google Scholar
  • Gallego G, van Ryzin G (1994) Optimal dynamic pricing of inventory with stochastic demand over finite horizons. Management Sci. 40(8):999–1020.LinkGoogle Scholar
  • Garg N, Nazerzadeh H (2021) Driver surge pricing. Management Sci. 68(5):3219–3235.LinkGoogle Scholar
  • Gong XY, Goyal V, Iyengar G, Simchi-Levi D, Udwani R, Wang S (2019) Online assortment optimization with reusable resources. Preprint, submitted March 7, http://dx.doi.org/10.2139/ssrn.3334789.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
  • Hosseini M, Milner J, Romero G (2021) Dynamic relocations in car-sharing networks. Preprint, submitted February 18, http://dx.doi.org/10.2139/ssrn.3774324.Google Scholar
  • Jasin S (2014) Reoptimization and self-adjusting price control for network revenue management. Oper. Res. 62:1168–1178.LinkGoogle Scholar
  • Kanoria Y, Qian P (2019) Near optimal control of a ride-hailing platform via mirror backpressure. Preprint, submitted March 7, https://arxiv.org/abs/1903.02764.Google Scholar
  • Kim J, Randhawa RS (2018) The value of dynamic pricing in large queueing systems. Oper. Res. 66(2):409–425.LinkGoogle Scholar
  • Kim J, Randhawa S, Ward AR (2018) Dynamic scheduling in a many-server, multiclass system: The role of customer impatience in large systems. Manufacturing Service Oper. Management 20(2):285–301.LinkGoogle Scholar
  • Lei Y, Jasin S (2020) Real-time dynamic pricing for revenue management with reusable resources, advance reservation, and deterministic service time requirements. Oper. Res. 68(3):676–685.LinkGoogle Scholar
  • Lei Y, Jasin S, Sinha A (2018) Joint dynamic pricing and order fulfillment for e-commerce retailers. Manufacturing Service Oper. Management 20(2):269–284.LinkGoogle Scholar
  • Levi R, Radovanovic A (2010) Provably near-optimal LP-based policies for revenue management in systems with reusable resources. Oper. Res. 58(2):503–507.LinkGoogle Scholar
  • Lim A (2017) Taxi companies get green light to implement dynamic pricing system. Straits Times (March 17), https://www.straitstimes.com/singapore/transport/authorities-give-taxi-companies-green-light-to-implement-surge-pricing-system.Google Scholar
  • Owen Z, Simchi-Levi D (2017) Price and assortment optimization for reusable resources. Preprint, submitted November 16, http://dx.doi.org/10.2139/ssrn.3070625.Google Scholar
  • Özkan E (2018) Joint pricing and matching in ride-sharing systems. Preprint, submitted August 8, http://dx.doi.org/10.2139/ssrn.3217642.Google Scholar
  • Özkan E, Ward AR (2020) Dynamic matching for real-time ride sharing. Stochastic Systems 10(1):29–70.LinkGoogle Scholar
  • Rusmevichientong P, Sumida M, Topaloglu H (2020) Dynamic assortment optimization for reusable products with random usage durations. Management Sci. 66(7):2820–2844.LinkGoogle Scholar
  • Taylor TA (2018) On-demand service platforms. Manufacturing Service Oper. Management 20(4):704–720.LinkGoogle Scholar
  • Varma SM, Bumpensanti P, Maguluri ST, Wang H (2022) Dynamic pricing and matching for two-sided queues. Oper. Res. 71(1):83–100.Google Scholar
  • Vera A, Banerjee S, Gurvich I (2021) Online allocation and pricing: Constant regret via Bellman inequalities. Oper. Res. 60(30):821–840.LinkGoogle Scholar
  • Vera A, Arlotto A, Gurvich I, Levin E (2020) Dynamic resource allocation: The geometry and robustness of constant regret. Working paper, Cornell University, ORIE, Ithaca, NY.Google Scholar
  • Wang Y, Wang H (2022) Constant regret resolving heuristics for price-based revenue management. Oper. Res. 70(6):3538–3557.LinkGoogle Scholar
  • Yan C, Zhu H, Korolko N, Woodard D (2019) Dynamic pricing and matching in ride-hailing platforms. Naval Res. Logist 67(8):705–724.CrossrefGoogle Scholar
  • Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Ye J, Li Z (2018) Deep multi-view spatial-temporal network for taxi demand prediction. Proc. 32nd AAAI Conf. Artificial Intelligence (Association for the Advancement of Artificial Intelligence, Palo Alto, CA), 2588–2595.Google Scholar
  • Yong C (2022) More drivers wanted as taxi and private-hire ridership rebounds. Straits Times (January 15), https://www.straitstimes.com/singapore/more-drivers-wanted-as-taxi-and-private-hire-ridership-rebounds.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.