Ride Sharing

  • Siddhartha BanerjeeEmail author
  • Ramesh Johari
Part of the Springer Series in Supply Chain Management book series (SSSCM, volume 6)


Ridesharing platforms such as Didi, Lyft, Ola and Uber are increasingly important components of the transportation infrastructure. However, our understanding of their design and operations, and their effect on society at large, is not yet well understood. From an academic perspective, these platforms present challenges in large-scale learning, real-time stochastic control, and market design. Their popularity has led to a growing body of academic work across several disciplines, with researchers addressing similar questions with vastly different tools and models. Our aim in this chapter is to outline the main challenges in ridesharing, and to present an approach to modeling, optimizing, and reasoning about such platforms. We describe how rigorous analysis has been used with great success in designing efficient algorithms for real-time decision making, in informing the market design aspects of these platforms, and in understanding the impact of these platforms in their larger societal context.



The authors would like to thank the data science team at Lyft, particularly Chris Sholley; part of this work was carried out when SB was a technical consultant at Lyft. We gratefully acknowledge support from the National Science Foundation via grants CMMI-1234955 and CNS-1343253, the DARPA GRAPHS program, and Army Research Office grant W911NF-17-1-0094.


  1. Adan I, Weiss G (2012) Exact fcfs matching rates for two infinite muti-type sequences. Oper Res 60:475–489CrossRefGoogle Scholar
  2. Adelman D (2007) Price-directed control of a closed logistics queueing network. Oper Res 55(6):1022–1038CrossRefGoogle Scholar
  3. Alonso-Mora J, Samaranayake S, Wallar A, Frazzoli E, Rus D (2017) On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc Natl Acad Sci 114(3):462–467CrossRefGoogle Scholar
  4. Armstrong M (2006) Competition in two-sided markets. RAND J Econ 37(3):668–691CrossRefGoogle Scholar
  5. Armstrong M, Wright J (2007) Two-sided markets, competitive bottlenecks and exclusive contracts. Econ Theory 32(2):353–380CrossRefGoogle Scholar
  6. Arnosti N, Johari R, Kanoria Y (2014) Managing congestion in decentralized matching markets. Available at SSRN 2427960Google Scholar
  7. Azevedo EM, Budish E (2012) Strategy-proofness in the large as a desideratum for market design. In: Proceedings of the 13th ACM conference on electronic commerce. ACM, New York, p 55Google Scholar
  8. Banerjee S, Johari R, Riquelme C (2015) Pricing in ride-sharing platforms: a queueing-theoretic approach. In: Proceedings of the 2015 ACM conference on economics and computation. ACM, New York, p 517Google Scholar
  9. Banerjee S, Freund D, Lykouris T (2017) Pricing and optimization in shared vehicle systems: an approximation framework. In: Proceedings of the 2017 ACM conference on economics and computation. ACM, New York, p 517Google Scholar
  10. Banerjee S, Kanoria Y, Qian P (2018) The value of state dependent control in ride-sharing systems. arXiv preprint, arXiv:180304959Google Scholar
  11. Baskett F, Chandy KM, Muntz RR, Palacios FG (1975) Open, closed, and mixed networks of queues with different classes of customers. J ACM (JACM) 22(2):248–260CrossRefGoogle Scholar
  12. Bimpikis K, Candogan O, Daniela S (2016) Spatial pricing in ride-sharing networks. Available at SSRN 2868080Google Scholar
  13. Bitran G, Caldentey R (2003) An overview of pricing models for revenue management. Manuf Serv Oper Manag 5(3):203–229CrossRefGoogle Scholar
  14. Braverman A, Dai J, Liu X, Ying L (2016) Empty-car routing in ridesharing systems. arXiv preprint arXiv:160907219Google Scholar
  15. Bušić A, Meyn S (2014) Optimization of dynamic matching models. arXiv preprint, arXiv:14111044Google Scholar
  16. Buzen JP (1973) Computational algorithms for closed queueing networks with exponential servers. Commun ACM 16(9):527–531CrossRefGoogle Scholar
  17. Caillaud B, Jullien B (2003) Chicken & egg: competition among intermediation service providers. RAND J Econ 34(2):309–328CrossRefGoogle Scholar
  18. Castillo JC, Knoepfle D, Weyl G (2017) Surge pricing solves the wild goose chase. In: Proceedings of the 2017 ACM conference on economics and computation. ACM, New York, pp 241–242Google Scholar
  19. Chen MK, Sheldon M (2016) Dynamic pricing in a labor market: surge pricing and flexible work on the Uber platform. In: Proceedings of the 2016 ACM conference on economics and computation. ACM, New York, p 455Google Scholar
  20. Gallego G, Van Ryzin G (1994) Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Manag Sci 40(8):999–1020CrossRefGoogle Scholar
  21. George DK (2012) Stochastic modeling and decentralized control policies for large-scale vehicle sharing systems via closed queueing networks. PhD thesis, The Ohio State UniversityGoogle Scholar
  22. Gopalakrishnan R, Doroudi S, Ward AR, Wierman A (2016) Routing and staffing when servers are strategic. Oper Res 64(4):1033–1050CrossRefGoogle Scholar
  23. Gordon WJ, Newell GF (1967) Closed queuing systems with exponential servers. Oper Res 15(2):254–265CrossRefGoogle Scholar
  24. Gurvich I, Lariviere M, Moreno A (2014) Staffing service systems when capacity has a mind of its own. Available at SSRN 2336514Google Scholar
  25. Hall J, Kendrick C, Nosko C (2015) The effects of Uber’s surge pricing: a case study. The University of Chicago Booth School of BusinessGoogle Scholar
  26. Hall JV, Horton JJ, Knoepfle DT (2017) Labor market equilibration: evidence from Uber. Technical report, Working Paper, 1–42Google Scholar
  27. Hampshire RC, Massey WA, Wang Q (2009) Dynamic pricing to control loss systems with quality of service targets. Probab Eng Inf Sci 23(02):357–383CrossRefGoogle Scholar
  28. Harchol-Balter M (2013) Performance modeling and design of computer systems: queueing theory in action. Cambridge University Press, CambridgeGoogle Scholar
  29. Hartline JD (2013) Mechanism design and approximation. Book draft October, p 122Google Scholar
  30. Hassin R, Haviv M (2003) To queue or not to queue: equilibrium behavior in queueing systems, vol 59. Springer, BostonCrossRefGoogle Scholar
  31. Jackson JR (1963) Jobshop-like queueing systems. Manag Sci 10(1):131–142CrossRefGoogle Scholar
  32. Kallenberg O (2006) Foundations of modern probability. Springer, New YorkGoogle Scholar
  33. Kelly FP (1979) Reversibility and stochastic networks. Cambridge University Press, CambridgeGoogle Scholar
  34. Kelly F, Yudovina E (2014) Stochastic networks, vol 2. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  35. Kojima F, Pathak PA (2009) Incentives and stability in large two-sided matching markets. Am Econ Rev 99(3):608–627CrossRefGoogle Scholar
  36. Levi R, Radovanović A (2010) Provably near-optimal LP-based policies for revenue management in systems with reusable resources. Oper Res 58(2):503–507CrossRefGoogle Scholar
  37. Moyal P, Busic A, Mairesse J (2017) A product form and a sub-additive theorem for the general stochastic matching model. arXiv preprint, arXiv:171102620Google Scholar
  38. Naor P (1969) The regulation of queue size by levying tolls. Econometrica 37(1):15–24CrossRefGoogle Scholar
  39. Nazari M, Stolyar AL (2016) Optimal control of general dynamic matching systems. arXiv preprint, arXiv:160801646Google Scholar
  40. Ozkan E, Ward AR (2016) Dynamic matching for real-time ridesharing. Available at SSRN 2844451Google Scholar
  41. Ramsey FP (1927) A contribution to the theory of taxation. Econ J 37(145):47–61CrossRefGoogle Scholar
  42. Reiser M, Lavenberg SS (1980) Mean-value analysis of closed multichain queuing networks. J ACM (JACM) 27(2):313–322CrossRefGoogle Scholar
  43. Rochet JC, Tirole J (2006) Two-sided markets: a progress report. RAND J Econ 37(3):645–667CrossRefGoogle Scholar
  44. Rysman M (2009) The economics of two-sided markets. J Econ Perspect 23(3):125–143CrossRefGoogle Scholar
  45. Santi P, Resta G, Szell M, Sobolevsky S, Strogatz SH, Ratti C (2014) Quantifying the benefits of vehicle pooling with shareability networks. Proc Natl Acad Sci 111(37):13290–13294CrossRefGoogle Scholar
  46. Séjourné T, Samaranayake S, Banerjee S (2017) The price of fragmentation in mobility-on-demand services. arXiv preprint, arXiv:171110963Google Scholar
  47. Serfozo R (1999) Introduction to stochastic networks, vol 44. Springer, New YorkCrossRefGoogle Scholar
  48. Spieser K, Samaranayake S, Gruel W, Frazzoli E (2016) Shared-vehicle mobility-on-demand systems: a fleet operator’s guide to rebalancing empty vehicles. In: Transportation Research Board 95th annual meeting, Washington, DC, 16–5987Google Scholar
  49. Srikant R, Ying L (2013) Communication networks: an optimization, control, and stochastic networks perspective. Cambridge University Press, CambridgeGoogle Scholar
  50. Talluri KT, Van Ryzin GJ (2006) The theory and practice of revenue management, vol 68. SpringerGoogle Scholar
  51. Visschers J, Adan I, Weiss G (2012) A product form solution to a system with multi-type jobs and multi-type servers. Queueing Syst 70(3):269–298CrossRefGoogle Scholar
  52. Waserhole A, Jost V (2016) Pricing in vehicle sharing systems: optimization in queuing networks with product forms. EURO J Transp Logist 5(3):293–320CrossRefGoogle Scholar
  53. Weyl EG (2010) A price theory of multi-sided platforms. Am Econ Rev 100(4):1642–1672CrossRefGoogle Scholar
  54. Whittle P (1985) Scheduling and characterization problems for stochastic networks. J R Stat Soc Ser B (Methodol) 47(3):407–428Google Scholar
  55. Zhang R, Pavone M (2016) Control of robotic mobility-on-demand systems: a queueing-theoretical perspective. Int J Robot Res 35(1–3):186–203CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Cornell UniversityIthacaUSA
  2. 2.Stanford UniversityStanfordUSA

Personalised recommendations