Skip to content Skip to main navigation Report an accessibility issue

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

Dr. Guiyun Feng
University of Minnesota
Industrial & Systems Engineering Dept.
Friday, February 9, 2018   2:30-3:30pm
JDT 410


Recently, there has been a rapid rise of on-demand ride-hailing platforms, such as Uber and Lyft, 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 work, 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.


Guiyun Feng is a doctoral candidate in the Department of Industrial and Systems Engineering at the University of Minnesota. Prior to joining the University of Minnesota, she received her Ph.D. degree in Management Sciences from the City University of Hong Kong in 2014 and her Bachelor’s degree in Physics at the University of Science and Technology of China in 2010. Her research interests include the service operations in sharing economy, customer choice modelling in ​revenue management and stochastic simulation in financial engineering. ​