Optimal Operations Management of Mobility-on-Demand Systems
Abstract
The emergence of the sharing economy in urban transportation networks has enabled new fast, convenient and accessible mobility services referred to as Mobilty-on-Demand systems (e.g., Uber, Lyft, DiDi). These platforms have flourished in the last decade around the globe and face many operational challenges in order to be competitive and provide good quality of service. A crucial step in the effective operation of these systems is to reduce customers' waiting time while properly selecting the optimal fleet size and pricing policy. In this paper, we jointly tackle three operational decisions: (i) fleet size, (ii) pricing, and (iii) rebalancing, in order to maximize the platform's profit or its customers' welfare. To accomplish this, we first devise an optimization framework which gives rise to a static policy. Then, we elaborate and propose dynamic policies that are more responsive to perturbations such as unexpected increases in demand. We test this framework in a simulation environment using three case studies and leveraging traffic flow and taxi data from Eastern Massachusetts, New York City, and Chicago. Our results show that solving the problem jointly could increase profits between 1% and up to 50%, depending on the benchmark. Moreover, we observe that the proposed fleet sizemore »
- Authors:
- Publication Date:
- Sponsoring Org.:
- USDOE Advanced Research Projects Agency - Energy (ARPA-E)
- OSTI Identifier:
- 1806341
- Resource Type:
- Published Article
- Journal Name:
- Frontiers in Sustainable Cities
- Additional Journal Information:
- Journal Name: Frontiers in Sustainable Cities Journal Volume: 3; Journal ID: ISSN 2624-9634
- Publisher:
- Frontiers Media SA
- Country of Publication:
- Country unknown/Code not available
- Language:
- English
Citation Formats
Wollenstein-Betech, Salomón, Paschalidis, Ioannis Ch., and Cassandras, Christos G. Optimal Operations Management of Mobility-on-Demand Systems. Country unknown/Code not available: N. p., 2021.
Web. doi:10.3389/frsc.2021.681096.
Wollenstein-Betech, Salomón, Paschalidis, Ioannis Ch., & Cassandras, Christos G. Optimal Operations Management of Mobility-on-Demand Systems. Country unknown/Code not available. https://doi.org/10.3389/frsc.2021.681096
Wollenstein-Betech, Salomón, Paschalidis, Ioannis Ch., and Cassandras, Christos G. Thu .
"Optimal Operations Management of Mobility-on-Demand Systems". Country unknown/Code not available. https://doi.org/10.3389/frsc.2021.681096.
@article{osti_1806341,
title = {Optimal Operations Management of Mobility-on-Demand Systems},
author = {Wollenstein-Betech, Salomón and Paschalidis, Ioannis Ch. and Cassandras, Christos G.},
abstractNote = {The emergence of the sharing economy in urban transportation networks has enabled new fast, convenient and accessible mobility services referred to as Mobilty-on-Demand systems (e.g., Uber, Lyft, DiDi). These platforms have flourished in the last decade around the globe and face many operational challenges in order to be competitive and provide good quality of service. A crucial step in the effective operation of these systems is to reduce customers' waiting time while properly selecting the optimal fleet size and pricing policy. In this paper, we jointly tackle three operational decisions: (i) fleet size, (ii) pricing, and (iii) rebalancing, in order to maximize the platform's profit or its customers' welfare. To accomplish this, we first devise an optimization framework which gives rise to a static policy. Then, we elaborate and propose dynamic policies that are more responsive to perturbations such as unexpected increases in demand. We test this framework in a simulation environment using three case studies and leveraging traffic flow and taxi data from Eastern Massachusetts, New York City, and Chicago. Our results show that solving the problem jointly could increase profits between 1% and up to 50%, depending on the benchmark. Moreover, we observe that the proposed fleet size yield utilization of the vehicles in the fleet is around 75% compared to private vehicle utilization of 5%.},
doi = {10.3389/frsc.2021.681096},
journal = {Frontiers in Sustainable Cities},
number = ,
volume = 3,
place = {Country unknown/Code not available},
year = {Thu Jul 08 00:00:00 EDT 2021},
month = {Thu Jul 08 00:00:00 EDT 2021}
}
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.3389/frsc.2021.681096
https://doi.org/10.3389/frsc.2021.681096
Other availability
Search WorldCat to find libraries that may hold this journal
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referenced in this record:
An $n^{5/2} $ Algorithm for Maximum Matchings in Bipartite Graphs
journal, December 1973
- Hopcroft, John E.; Karp, Richard M.
- SIAM Journal on Computing, Vol. 2, Issue 4
Joint Estimation of OD Demands and Cost Functions in Transportation Networks from Data *
conference, December 2019
- Wollenstein-Betech, Salomon; Sun, Chuangchuang; Zhang, Jing
- 2019 IEEE 58th Conference on Decision and Control (CDC)
Addressing the minimum fleet problem in on-demand urban mobility
journal, May 2018
- Vazifeh, M. M.; Santi, P.; Resta, G.
- Nature, Vol. 557, Issue 7706
Cost-based analysis of autonomous mobility services
journal, May 2018
- Bösch, Patrick M.; Becker, Felix; Becker, Henrik
- Transport Policy, Vol. 64
A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management
journal, February 1997
- Gallego, Guillermo; van Ryzin, Garrett
- Operations Research, Vol. 45, Issue 1
Automating mobility in smart cities
journal, January 2017
- Cassandras, Christos G.
- Annual Reviews in Control, Vol. 44
Make cities and human settlements inclusive, safe, resilient and sustainable
journal, June 2015
- Daniel, Kristie
- UN Chronicle, Vol. 51, Issue 4
Robotic load balancing for mobility-on-demand systems
journal, May 2012
- Pavone, Marco; Smith, Stephen L.; Frazzoli, Emilio
- The International Journal of Robotics Research, Vol. 31, Issue 7
Consolidated, systemic conceptualization, and definition of the “sharing economy”
journal, October 2019
- Schlagwein, Daniel; Schoder, Detlef; Spindeldreher, Kai
- Journal of the Association for Information Science and Technology, Vol. 71, Issue 7
Dynamic pricing and fleet management for electric autonomous mobility on demand systems
journal, December 2020
- Turan, Berkay; Pedarsani, Ramtin; Alizadeh, Mahnoosh
- Transportation Research Part C: Emerging Technologies, Vol. 121
Congestion-dependent pricing of network services
journal, April 2000
- Paschalidis, I. Ch.; Tsitsiklis, J. N.
- IEEE/ACM Transactions on Networking, Vol. 8, Issue 2
A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application
journal, July 2017
- Levin, Michael W.; Kockelman, Kara M.; Boyles, Stephen D.
- Computers, Environment and Urban Systems, Vol. 64
Control of robotic mobility-on-demand systems: A queueing-theoretical perspective
journal, July 2015
- Zhang, Rick; Pavone, Marco
- The International Journal of Robotics Research, Vol. 35, Issue 1-3
Peeking Beneath the Hood of Uber
conference, October 2015
- Chen, Le; Mislove, Alan; Wilson, Christo
- IMC '15: Internet Measurement Conference, Proceedings of the 2015 Internet Measurement Conference
Spatial Pricing in Ride-Sharing Networks
journal, May 2019
- Bimpikis, Kostas; Candogan, Ozan; Saban, Daniela
- Operations Research, Vol. 67, Issue 3