An integrated car-and-ride sharing system for mobilizing heterogeneous travelers with application in underserved communities

Abstract

The fast-growing carsharing and ride-hailing businesses are generating economic benefits and societal impacts in modern society, while both have limitations to satisfy diverse users, e.g., travelers in low-income, underserved communities. In this article, we consider two types of users: Type 1 drivers who rent shared cars and Type 2 passengers who need shared rides. We propose an integrated car-and-ride sharing (CRS) system to enable community-based shared transportation. To compute solutions, we propose a two-phase approach where in Phase I we determine initial car allocation and Type 1 drivers to accept; in Phase II we solve a stochastic mixed-integer program to match the accepted Type 1 drivers with Type 2 users, and optimize their pick-up routes under a random travel time. The goal is to minimize the total travel cost plus expected penalty cost of users’ waiting and system overtime. We demonstrate the performance of a CRS system in Washtenaw County, Michigan by testing instances generated based on census data and different demand patterns. We also demonstrate the computational efficacy of our decomposition algorithm benchmarked with the traditional Benders decomposition for solving the stochastic model in Phase II. Our results show high demand fulfillment rates and effective matching and scheduling with low risk of waiting and overtime.

Keywords:

• Carsharing
• ride-hailing
• stochastic vehicle routing
• two-stage stochastic integer programming
• decomposition algorithm

Notes

Notes

1. https://developers.google.com/maps/documentation/distance-matrix/

2. https://support.zipcar.com/hc/en-us/articles/220333808-Eligibility-Requirements

3. https://www.uber.com/fare-estimate/

Additional information

Funding

The authors are grateful for the support from the National Science Foundation under grant CMMI-1636876 and CMMI-1727618.

Notes on contributors

Miao Yu

Miao Yu received a B.S. in mathematics and B.S. in statistics from the University of Minnesota, Twin Cities in 2014. He is currently a Ph.D. candidate in the Department of Industrial and Operations Engineering at the University of Michigan, Ann Arbor. His research interests include stochastic programming and combinatorial optimization with applications in routing and shared mobility problems.

Siqian Shen

Siqian Shen received a B.S. degree in industrial engineering from Tsinghua University, China, in 2007, and M.S. and Ph.D. degrees in industrial and systems engineering from the University of Florida, USA, in 2009 and 2011, respectively. She is an associate professor in the Department of Industrial and Operations Engineering, University of Michigan at Ann Arbor, and also an Associate Director for the Michigan Institute for Computational Discovery & Engineering. Her research interests include stochastic programming, network optimization, and integer programming. Applications of her work include healthcare, transportation, and energy.