### Abstract

This paper considers 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.

Original language | English (US) |
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Title of host publication | SIGMETRICS 2017 Abstracts - Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems |

Publisher | Association for Computing Machinery, Inc |

Pages | 11-12 |

Number of pages | 2 |

ISBN (Electronic) | 9781450350327 |

DOIs | |

State | Published - Jun 5 2017 |

Event | 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2017 - Urbana-Champaign, United States Duration: Jun 5 2017 → Jun 9 2017 |

### Other

Other | 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2017 |
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Country | United States |

City | Urbana-Champaign |

Period | 6/5/17 → 6/9/17 |

### Fingerprint

### ASJC Scopus subject areas

- Software
- Hardware and Architecture
- Computer Networks and Communications
- Computational Theory and Mathematics

### Cite this

*SIGMETRICS 2017 Abstracts - Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems*(pp. 11-12). Association for Computing Machinery, Inc. https://doi.org/10.1145/3078505.3078595

**Fluid-model-based car routing for modern ridesharing systems.** / Braverman, Anton; Dai, J. G.; Liu, Xin; Ying, Lei.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*SIGMETRICS 2017 Abstracts - Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems.*Association for Computing Machinery, Inc, pp. 11-12, 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2017, Urbana-Champaign, United States, 6/5/17. https://doi.org/10.1145/3078505.3078595

}

TY - GEN

T1 - Fluid-model-based car routing for modern ridesharing systems

AU - Braverman, Anton

AU - Dai, J. G.

AU - Liu, Xin

AU - Ying, Lei

PY - 2017/6/5

Y1 - 2017/6/5

N2 - This paper considers 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.

AB - This paper considers 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.

UR - http://www.scopus.com/inward/record.url?scp=85021795559&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85021795559&partnerID=8YFLogxK

U2 - 10.1145/3078505.3078595

DO - 10.1145/3078505.3078595

M3 - Conference contribution

AN - SCOPUS:85021795559

SP - 11

EP - 12

BT - SIGMETRICS 2017 Abstracts - Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems

PB - Association for Computing Machinery, Inc

ER -