发表机构
Eindhoven University of Technology（埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种代币经济机制，通过动态定价实现快速车道公平分配，在保持交通效率的同时减少紧迫性加权出行时间，为拥堵管理提供新方案。
AI 中文摘要
我们研究了一种用于高速公路车道分配的代币经济设计，旨在不牺牲交通效率的前提下提高公平性。受圣马特奥101快速车道项目的启发，我们考虑了一个场景：高占用率车辆可以无限制地使用快速车道，而其余用户则可以通过赚取和花费非货币代币在普通车道和快速车道之间切换。我们将由此产生的交互建模为一个具有异质时间偏好和有限信息演化策略修订的有限人口动态拥塞博弈。基于平均场近似，我们推导出代币价格，这些价格强制执行系统最优的车道分配，同时通过轮流使用在时间上实现公平性。该方案在具有真实世界需求数据的微观交通模拟中进行了评估。结果表明，所提出的价格产生的平均出行时间与没有预留快速车道的基线场景几乎相同，同时大幅减少了以紧迫性加权的感知出行时间。这些发现凸显了代币经济作为货币拥堵收费的一种有前景的替代方案，用于更公平地管理稀缺的道路容量。
英文摘要
We study the design of a token economy for highway lane allocation that aims to improve fairness without sacrificing traffic efficiency. Motivated by the San Mateo 101 Express Lanes Project, we consider a setting in which high-occupancy vehicles have unrestricted access to an express lane, while the remaining users can alternate between regular and express lanes by earning and spending nonmonetary tokens. We model the resulting interaction as a finite-population dynamic congestion game with heterogeneous time preferences and limited-information evolutionary policy revisions. Building on a mean-field approximation, we derive token prices that enforce the system-optimal lane split while inducing fairness over time through turn-taking. The scheme is evaluated in a microscopic traffic simulation with real-world demand data. The results show that the proposed prices yield nearly the same average travel time as a baseline scenario in which no lane is reserved as an express lane, while substantially reducing urgency-weighted perceived travel time. These findings highlight token economies as a promising alternative to monetary congestion pricing for fairer management of scarce road capacity.
CommentsAccepted to the 2026 IFAC Workshop on Cyber-Physical Human Systems
