Adaptive microtolling in competitive online congestion games via multiagent reinforcement learning
Adaptive microtolling in competitive online congestion games via multiagent reinforcement learning
Efficient urban traffic management remains a critical challenge, yet traditional congestion games fail to capture the dynamic and competitive nature of real-world transportation systems. We introduce the Multi-Market Routing Problem (MMRP), an online and oligopolistic extension that models competition amongst route providers utilising adaptive microtolling strategies to influence driver behaviour and mitigate congestion. We formally define the MMRP, highlighting the computational complexity of solving the MMRP, and use an adapted version of Proximal Policy Optimisation (PPO) to improve update stability in multiagent environments to address this problem in online settings. Our empirical analysis demonstrates that our PPO-based approach not only matches the performance of existing benchmarks but also significantly enhances equity, reduces travel times for users, and increases profitability for providers.
Koohy, Behrad
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Stein, Sebastian
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Gerding, Enrico
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19 May 2025
Koohy, Behrad
1d8bf838-48c3-46ec-b2d3-a1c5001ccaaf
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Koohy, Behrad, Stein, Sebastian and Gerding, Enrico
(2025)
Adaptive microtolling in competitive online congestion games via multiagent reinforcement learning.
In Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025).
3 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Efficient urban traffic management remains a critical challenge, yet traditional congestion games fail to capture the dynamic and competitive nature of real-world transportation systems. We introduce the Multi-Market Routing Problem (MMRP), an online and oligopolistic extension that models competition amongst route providers utilising adaptive microtolling strategies to influence driver behaviour and mitigate congestion. We formally define the MMRP, highlighting the computational complexity of solving the MMRP, and use an adapted version of Proximal Policy Optimisation (PPO) to improve update stability in multiagent environments to address this problem in online settings. Our empirical analysis demonstrates that our PPO-based approach not only matches the performance of existing benchmarks but also significantly enhances equity, reduces travel times for users, and increases profitability for providers.
Text
AAMAS_2025_Behrad_ExtendedAbstract
- Accepted Manuscript
More information
Accepted/In Press date: 19 December 2024
Published date: 19 May 2025
Identifiers
Local EPrints ID: 499891
URI: http://eprints.soton.ac.uk/id/eprint/499891
PURE UUID: 198a32fc-01ef-4873-893f-8aab196e3aef
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Date deposited: 08 Apr 2025 16:34
Last modified: 22 Aug 2025 01:59
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Contributors
Author:
Behrad Koohy
Author:
Sebastian Stein
Author:
Enrico Gerding
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