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Ride Sharing

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

Abstract

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.

Notes

Acknowledgements

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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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