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Dynamic Tolling in Arc-based Traffic Assignment Models
- Publication Year :
- 2023
-
Abstract
- Tolling in traffic networks offers a popular measure to minimize overall congestion. Existing toll designs primarily focus on congestion in route-based traffic assignment models (TAMs), in which travelers make a single route selection from their source to destination. However, these models do not reflect real-world traveler decisions because they preclude deviations from a chosen route, and because the enumeration of all routes is computationally expensive. To address these limitations, our work focuses on arc-based TAMs, in which travelers sequentially select individual arcs (or edges) on the network to reach their destination. We first demonstrate that marginal pricing, a tolling scheme commonly used in route-based TAMs, also achieves socially optimal congestion levels in our arc-based formulation. Then, we use perturbed best response dynamics to model the evolution of travelers' arc selection preferences over time, and a marginal pricing scheme to the social planner's adaptive toll updates in response. We prove that our adaptive learning and marginal pricing dynamics converge to a neighborhood of the socially optimal loads and tolls. We then present empirical results that verify our theoretical claims.<br />Comment: 18 pages, 4 figures, 2 tables. arXiv admin note: text overlap with arXiv:2304.04705
- Subjects :
- Electrical Engineering and Systems Science - Systems and Control
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2307.05466
- Document Type :
- Working Paper