Fluid-Model-Based Car Routing for Modern Ridesharing Systems

Anton Braverman, J. G. Dai, Xin Liu, Lei Ying

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


We consider a closed queueing network model of ridesharing systems such as Didi Chuxing, Lyft, and Uber. We focus on empty-car routing, a mechanism by which we control car flow in the network to optimize system-wide utility functions, e.g. the availability of empty cars when a passenger arrives. We establish both process-level and steady-state convergence of the queueing network to a fluid limit in a large market regime where demand for rides and supply of cars tend to infinity, and use this limit to study a fluid-based optimization problem. We prove that the optimal network utility obtained from the fluid-based optimization is an upper bound on the utility in the finite car system for any routing policy, both static and dynamic, under which the closed queueing network has a stationary distribution. This upper bound is achieved asymptotically under the fluid-based optimal routing policy. Simulation results with real-word data released by Didi Chuxing demonstrate that the utility under the fluid-based optimal routing policy converges to the upper bound with a rate of 1 √N, where N is the number of cars in the network.

Original languageEnglish (US)
Pages (from-to)11-12
Number of pages2
JournalPerformance Evaluation Review
Issue number1
StatePublished - Jun 5 2017


  • BCMP network
  • car routing
  • closed queueing network
  • fluid limit
  • ridesharing

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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