Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks
作者: Khadidja Kadem, Mostafa Ameli, Carlos Lima Azevedo, Mahdi Zargayouna, Latifa Oukhellou
分类: cs.LG, cs.AI, math.OC
发布日期: 2026-06-22
💡 一句话要点
提出基于动态多智能体深度强化学习的定价与激励方法以解决多模式交通网络问题
🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 多模式交通 共享出行 深度强化学习 动态定价 激励机制 交通管理 可持续发展
📋 核心要点
- 现有的共享出行服务在高密度区域的需求集中，导致交通系统效率低下和排放问题。
- 提出的多智能体深度强化学习框架通过动态定价和激励策略，协调公共交通与SMS之间的利益冲突。
- 实验结果显示，动态激励降低了约20%的通勤成本和10%的排放，同时几乎使公共交通利润翻倍。
📝 摘要（中文）
在多模式交通系统中，共享出行服务（SMS）因其提升灵活性和减少拥堵的潜力而受到推广。然而，SMS需求往往集中在高密度区域，这限制了不同通勤群体的有效性和可达性。为了解决这一问题，本文提出了一种多智能体深度强化学习框架，通过动态定价和激励策略来协调公共交通和SMS的利益。该框架集成了两个强化学习代理，分别为公共机构和SMS提供者，能够根据不断变化的需求和网络条件调整策略。实验结果表明，该方法有效降低了拥堵峰值，减少了通勤成本和排放，同时提高了公共交通的利润。
🔬 方法详解
问题定义：本文旨在解决多模式交通网络中共享出行服务（SMS）需求集中导致的交通效率低下和排放问题。现有方法未能有效协调公共机构与SMS提供者之间的利益冲突。
核心思路：通过引入多智能体深度强化学习框架，本文设计了两个智能体：公共机构和SMS提供者，分别负责优化公共交通激励和动态调整SMS定价，以实现可持续和公平的出行。
技术框架：该框架包括两个主要模块：公共机构智能体负责分配时空激励以提高公平性和效率；SMS提供者智能体则根据需求和网络状态动态调整票价。两个智能体通过与交通系统的交互不断适应策略。
关键创新：本研究的创新在于将多智能体深度强化学习应用于交通网络定价与激励，能够有效平衡公共利益与私营利益之间的冲突，提升了交通系统的整体效率。
关键设计：在模型设计中，设置了适应性强的损失函数，以便在不同的交通条件下优化激励和定价策略，同时采用了深度学习网络来处理复杂的交通数据和需求预测。
🖼️ 关键图片
📊 实验亮点
实验结果表明，动态激励策略有效降低了通勤高峰期的拥堵，通勤成本降低约20%，排放减少约10%。同时，公共交通的利润几乎翻倍，显示出该方法在促进公平性和效率方面的显著成效。
🎯 应用场景
该研究的潜在应用领域包括城市交通管理、共享出行服务优化以及公共交通系统的可持续发展。通过提供决策支持工具，能够帮助政策制定者和交通管理者在规划和实施交通政策时实现更高的效率和公平性，推动未来的智能交通系统发展。
📄 摘要（原文）
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.