Reinforcement learning and mechanism design for routing of connected and autonomous vehicles
Reinforcement learning and mechanism design for routing of connected and autonomous vehicles
The data provided by Connected and Autonomous Vehicles (CAVs) is a powerful tool, providing insight into user incentives and preferences, and combined with existing road data sources, provides a number of new research avenues for intelligent traffic systems. In this paper, we propose the use of Reinforcement Learning (RL) for adaptive pricing of travel systems such as trains, buses and toll-road, in simulations which consider multiple transport providers and traffic management systems, known as the multi-market pricing problem. We also propose two research directions for this problem, the use of incentives when user preferences are included and development of detection and prevention of unintentional collusion between RL pricing agents.
Connected Automated Vehicles, Vehicle routing, Reinforcement learning, adaptive pricing
Koohy, Behrad
1d8bf838-48c3-46ec-b2d3-a1c5001ccaaf
29 May 2023
Koohy, Behrad
1d8bf838-48c3-46ec-b2d3-a1c5001ccaaf
Koohy, Behrad
(2023)
Reinforcement learning and mechanism design for routing of connected and autonomous vehicles.
The 22nd International Conference on Autonomous Agents and Multiagent Systems, Excel, London, United Kingdom.
29 May - 02 Jun 2023.
2 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
The data provided by Connected and Autonomous Vehicles (CAVs) is a powerful tool, providing insight into user incentives and preferences, and combined with existing road data sources, provides a number of new research avenues for intelligent traffic systems. In this paper, we propose the use of Reinforcement Learning (RL) for adaptive pricing of travel systems such as trains, buses and toll-road, in simulations which consider multiple transport providers and traffic management systems, known as the multi-market pricing problem. We also propose two research directions for this problem, the use of incentives when user preferences are included and development of detection and prevention of unintentional collusion between RL pricing agents.
Text
Behrad_AAMAS_DC_Research_Proposal-5
- Accepted Manuscript
More information
Accepted/In Press date: 10 March 2023
Published date: 29 May 2023
Venue - Dates:
The 22nd International Conference on Autonomous Agents and Multiagent Systems, Excel, London, United Kingdom, 2023-05-29 - 2023-06-02
Keywords:
Connected Automated Vehicles, Vehicle routing, Reinforcement learning, adaptive pricing
Identifiers
Local EPrints ID: 476985
URI: http://eprints.soton.ac.uk/id/eprint/476985
PURE UUID: e7e5b032-0153-42b3-b3ea-6159f7c9463d
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Date deposited: 23 May 2023 16:33
Last modified: 17 Mar 2024 01:30
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Contributors
Author:
Behrad Koohy
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