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We are on the Way: Analysis of On-Demand Ride-Hailing Systems

36 Pages Posted: 1 May 2017  

Guiyun Feng

University of Minnesota - Industrial & System Engineering

Guangwen Kong

University of Minnesota - Industrial & System Engineering

Zizhuo Wang

University of Minnesota - Industrial & System Engineering

Date Written: February 28, 2017

Abstract

Recently, there has been a rapid rise of on-demand ride-hailing platforms, such as Uber and Didi, which allow passengers with smartphones to submit trip requests and match them to drivers based on their locations and drivers' availability. This increased demand has raised questions about how such a new matching mechanism will affect the efficiency of the transportation system, in particular, whether it will help reduce passengers' average waiting time compared to traditional street-hailing systems.

In this paper, we address this question by building a stylized model of a circular road and comparing the average waiting times of passengers under various matching mechanisms. After identifying key trade-offs between different mechanisms, we find that surprisingly, the on-demand matching mechanism could result in higher or lower efficiency than the traditional street-hailing mechanism, depending on the parameters of the system. To overcome the disadvantage of both systems, we further propose adding response caps to the on-demand hailing mechanism and develop a heuristic method to calculate a near-optimal cap. We also test our model using more complex road networks to show that our key observations still exist.

Keywords: on-demand ride-hailing; queueing systems; matching mechanism

Suggested Citation

Feng, Guiyun and Kong, Guangwen and Wang, Zizhuo, We are on the Way: Analysis of On-Demand Ride-Hailing Systems (February 28, 2017). Available at SSRN: https://ssrn.com/abstract=2960991 or http://dx.doi.org/10.2139/ssrn.2960991

Guiyun Feng

University of Minnesota - Industrial & System Engineering ( email )

111 Church Street S.E.
Minneapolis, MN 55455
United States

Guangwen Kong

University of Minnesota - Industrial & System Engineering ( email )

111 Church Street S.E.
Minneapolis, MN 55455
United States

Zizhuo Wang (Contact Author)

University of Minnesota - Industrial & System Engineering ( email )

111 Church Street S.E.
Minneapolis, MN 55455
United States

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