Abstract
We study a setting where electric vehicles (EVs) can be hired to drive from pick-up to drop-off stations in a mobility-on-demand (MoD) scheme. Each point in the MoD scheme is equipped with battery charge facility to cope with the EVs’ limited range. Customer-agents announce their trip requests over time, and the goal for the system is to maximize the number of them that are serviced. In this vein, we propose two scheduling algorithms for assigning EVs to agents. The first one is efficient for short term reservations, while the second for both short and long term ones. While evaluating our algorithms in a setting using real data on MoD locations, we observe that the long term algorithm achieves on average 2.08% higher customer satisfaction and 2.87% higher vehicle utilization compared to the short term one for 120 trip requests, but with 17.8% higher execution time. Moreover, we propose a software package that allows for efficient management of a MoD scheme from the side of a company, and easy trip requests for customers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
A flowchart for the Short mode is available at https://goo.gl/dxpfer.
- 2.
A flowchart for the Long mode is available at https://goo.gl/zFdWvh (part 1) and https://goo.gl/WZHrRc (part 2).
- 3.
A video demo is available at https://youtu.be/flyixErIE-A.
- 4.
- 5.
- 6.
Charging stations data were collected from https://goo.gl/pWXFm6.
- 7.
Distance calculation using latitude and longitude https://goo.gl/3bDKuT.
References
Tomic, J., Kempton, W.: Using fleets of electric-drive vehicles for grid support. J. Power Sources 168, 459–468 (2007)
Mitchell, W., Borroni-Bird, C., Burns, L.: Reinventing the Automobile: Personal Urban Mobility for the 21st Century. The MIT Press, Cambridge (2010)
Pavone, M., Smith, S., Frazzoli, E., Rus, D.: Robotic load balancing for mobility-on-demand systems. Int. J. Robot. Res. 31, 839–854 (2012)
Pavone, M., L. Smith, S., Frazzoli, E., Rus, D.: Load balancing for mobility-on-demand systems. In: Robotics: Science and Systems (2011)
Smith, S., Pavone, M., Schwager, M., Frazzoli, E., Rus, D.: Rebalancing the rebalancers: optimally routing vehicles and drivers in mobility-on-demand systems. In: 2013 American Control Conference, pp. 2362–2367 (2013)
Carpenter, T., Keshav, S., Wong, J.: Sizing finite-population vehicle pools. IEEE Trans. Intell. Transp. Syst. 15, 1134–1144 (2014)
Drwal, M., Gerding, E., Stein, S., Hayakawa, K., Kitaoka, H.: Adaptive pricing mechanisms for on-demand mobility. In: Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems, pp. 1017–1025 . International Foundation for Autonomous Agents and Multiagent Systems (2017)
Rigas, E., Ramchurn, S., Bassiliades, N.: Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes. Artif. Intell. 262, 248–278 (2018)
Barth, M., Shaheen, S.: Shared-use vehicle systems: framework for classifying carsharing, station cars, and combined approaches. Transp. Res. Rec. J. Transp. Res. Board 1791, 105–112 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gkourtzounis, I., Rigas, E.S., Bassiliades, N. (2019). Towards Online Electric Vehicle Scheduling for Mobility-On-Demand Schemes. In: Slavkovik, M. (eds) Multi-Agent Systems. EUMAS 2018. Lecture Notes in Computer Science(), vol 11450. Springer, Cham. https://doi.org/10.1007/978-3-030-14174-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-14174-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14173-8
Online ISBN: 978-3-030-14174-5
eBook Packages: Computer ScienceComputer Science (R0)