Losapio, G 
ORCID: 0000-0002-1024-7512, Minutoli, F ORCID: 0000-0002-5472-7673, Mascardi, V and Ferrando, A ORCID: 0000-0002-8711-4670
(2021)
Smart balancing of E-scooter sharing systems via deep reinforcement learning.
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Abstract
Nowadays, micro-mobility sharing systems have become extremely popular. Such systems consist in fleets of electric vehicles which are deployed in cities, and used by citizens to move in a more ecological and flexible way. Unfortunately, one of the issues related to such technologies is its intrinsic load imbalance; since the users can pick up and drop off the electric vehicles where they prefer. We present ESB-DQN, a multi-agent system based on Deep Reinforcement Learning that offers suggestions to pick or return e-scooters in order to make the fleet usage and sharing as balanced as possible.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 06 Mar 2023 11:33 |
| Last Modified: | 06 Mar 2023 11:33 |
| Open Access URL: | https://ceur-ws.org/Vol-2963/paper16.pdf |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3168787 |
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