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

AI框架通过动态定价和激励优化多式联运

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-22 12:39

研究人员开发了一个多智能体深度强化学习框架，通过平衡公共管理部门、共享出行服务（SMS）提供商和出行者之间的冲突目标，来优化多式联运系统。该系统使用两个智能体：一个负责公共交通激励，另一个负责动态SMS定价。实验表明，该方法可以减少拥堵，降低通勤者成本和排放，并提高公共交通的盈利能力和公平性。 AI

影响这项研究有望通过AI驱动的优化实现更高效、更公平的城市交通系统。

排序理由这是一篇详细介绍多智能体深度强化学习新应用的学术论文。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Latifa Oukhellou · 2026-06-22 12:39

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

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 co…

报道来源 [1]

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

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