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Learning to Incentivise: Using Reinforcement Learning for Sustainable Urban Mobility — arXiv:2610.30469

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.30469
License (as archived): CC0-1.0
Archived: 2026-10-23T15:08:38.489Z
SHA-256 of the archived text: 4e5891aa307063fbe8fb99411e1dcb551f1674059f211fe3ff94065e500042ae
Stable URL of this page: https://metraipolis.com/documento/dc_66caa67a630b

How to cite

Learning to Incentivise: Using Reinforcement Learning for Sustainable Urban Mobility — arXiv:2610.30469 — arXiv (en), https://arxiv.org/abs/2610.30469 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_66caa67a630b (Metraipolis archive dc_66caa67a630b, sha256 4e5891aa307063fbe8fb99411e1dcb551f1674059f211fe3ff94065e500042ae)

BibTeX

@misc{dc66caa67a630b,
  title = {Learning to Incentivise: Using Reinforcement Learning for Sustainable Urban Mobility — arXiv:2610.30469},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.30469},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_66caa67a630b; sha256 4e5891aa307063fbe8fb99411e1dcb551f1674059f211fe3ff94065e500042ae},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.30469. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Traffic management has traditionally focused on toll-based and road pricing solutions. However, road pricing often raises concerns about accessibility and public dissatisfaction, leading to its prohibition in some places, such as Finland. This study optimises the dynamic allocation of incentives to drivers, encouraging them to reroute onto alternative (potentially longer) paths to achieve greater societal benefit, reduced total travel time (TTT) and total emissions in the transportation network, contributing to sustainable urban mobility. We employ a multi-agent reinforcement learning approach to dynamically assign incentives to drivers to reduce both TTT and emissions, with travel times estimated using traffic simulation software. We demonstrate that, with an unlimited budget and an objective of minimising travel time, the incentive scheme reduces TTT by 16%, compared to the dynamic User-Equilibrium (UE) with a budget equivalent to about 11% of the UE total time. When the goal is to minimise emissions, a 9% reduction in CO2 emissions is observed under an unlimited budget. We demonstrate a critical trade-off: minimising TTT leads to an increase in emissions, while prioritising emission reductions raises TTT. However, with the right combination of weights in the multi-objective function, both TTT and total emissions are improved beyond the baseline.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Multiagent Systems (cs.MA) ; Machine Learning (cs.LG); Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2610.30469 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-23T15:08:38.387Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).