Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Khadidja Kadem, Mostafa Ameli, Carlos Lima Azevedo, Mahdi Zargayouna, Latifa Oukhellou
• Venue: arXiv: cs.LG
• Published: 2026-06-22
• Collected: 2026-06-23
• URL: http://arxiv.org/abs/2606.23257v1
• PDF: https://arxiv.org/pdf/2606.23257v1
• DOI: UNKNOWN

Plain-Language Brief

A study proposes a multi-agent deep reinforcement learning framework to coordinate shared mobility services (SMSs) and public transport in multimodal transportation systems. The framework aims to improve efficiency, reduce congestion, lower emissions, and promote spatial equity by dynamic pricing and incentivization strategies for SMSs and public transport.

What the Document Says

• The text discusses a multi-agent deep reinforcement learning framework for shared mobility services (SMSs) in multimodal transportation systems.
• The framework aims to coordinate SMS providers, public authorities, and travelers with conflicting objectives: revenue maximization for providers, sustainable and equitable mobility for authorities, and cost minimization for travelers.
• The framework includes two reinforcement learning (RL) agents: a public authority that allocates incentives, and an SMS provider that adjusts fares dynamically.
• Numerical experiments were conducted over a three-hour morning peak period, showing the reduction of congestion peaks, lower commuters' costs by around 20%, approximately 10% decrease in emissions, nearly doubling public transport profit, and a more equitable distribution of benefits.
• The authors suggest that the proposed approach could serve as a decision-support tool for sustainable and equitable multimodal mobility planning.

Key Concepts Mentioned

• multimodal transportation systems
• shared mobility services (SMSs)
• reinforcement learning (RL) agents
• dynamic pricing
• incentivization strategies
• public transport
• efficiency
• congestion
• emissions
• spatial equity

Claims Made by the Document

• The proposed multi-agent deep reinforcement learning framework can effectively reduce congestion peaks and emissions while nearly doubling public transport profit.
• The framework supports a more equitable distribution of benefits.

Evidence Used

• Numerical experiments conducted over a three-hour morning peak period.

Methods / Approach

• Multi-agent deep reinforcement learning framework
• Dynamic pricing and incentivization strategies

Limitations Stated by the Authors

Unknown

Limitations Noted by the Agent

Agent interpretation — not from the source.

The model is based on numerical experiments, and it's unclear how well it would perform in real-world scenarios with various complexities.

Open Questions

• How effective will the proposed approach be in different urban structures and traffic patterns?
• What are the potential challenges in implementing the framework in real-world multimodal transportation systems?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text does not provide information on how the authors validated their model or tested its scalability.

Raw Abstract / Excerpt

In multimodal transportation systems, shared mobility services (SMSs) are promoted for their potential to enhance flexibility and reduce congestion. However, SMS demand is often concentrated in high-density areas, which can limit the effectiveness and accessibility for various commuter groups. This uneven integration challenges transportation system efficiency, especially in terms of emissions and spatial equity. Addressing these issues requires coordination among multiple stakeholders whose objectives frequently conflict. Whereas authorities aim to ensure sustainable and equitable mobility, SMS providers focus on revenue maximization, and travelers seek to minimize personal travel costs. This paper proposes a multi-agent deep reinforcement learning framework that captures these interactions through dynamic pricing and incentivization strategies for SMSs and public transport. The framework integrates two reinforcement learning (RL) agents: (i) a public authority that allocates spatio-temporal public transport incentives to improve equity, emissions, and efficiency, and (ii) an SMS provider that dynamically adjusts fares to optimize revenue. The agents interact with the transportation system and adapt strategies in response to evolving demand, congestion, and network conditions. Numerical experiments conducted over a three-hour morning peak period show that dynamic incentivization effectively reduces congestion peaks, lowers commuters' costs by around 20% and emissions by approximately 10%, while nearly doubling public transport profit and supporting a more equitable distribution of benefits. When combined with dynamic SMS pricing, the two RL agents demonstrate the ability to balance conflicting objectives between private providers and public authorities. The proposed approach provides a decision-support tool for sustainable and equitable multimodal mobility planning.