🤖 AI Summary
This study addresses the inherent trade-off between efficiency and equity in highway congestion management by proposing a lane allocation mechanism grounded in a non-monetary token economy. Methodologically, the work integrates finite-population evolutionary game theory, mean-field approximation, and microscopic traffic simulation to construct a token pricing model that achieves equitable rotation and system-optimal flow distribution, thereby serving as an alternative to conventional monetary congestion pricing. The results demonstrate that the proposed scheme significantly reduces weighted perceived travel time while maintaining baseline travel times unchanged. These findings effectively validate the superiority of the token-based mechanism in simultaneously enhancing system-wide efficiency and preserving user-level equity.
📝 Abstract
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.