This article proposes Δ-tolling, a simple adaptive pricing scheme which only requires travel time observations and two tuning parameters. These tolls are applied throughout a road network, and can be updated as frequently as travel time observations are made. Notably, Δ-tolling does not require any details of the traffic flow or travel demand models other than travel time observations, rendering it easy to apply in real-time. The flexibility of this tolling scheme is demonstrated in three specific traffic modeling contexts with varying traffic flow and user behavior assumptions: a day-to-day pricing model using static network equilibrium with link delay functions; a within-day adaptive pricing model using the cell transmission model and dynamic routing of vehicles; and a microsimulation of reservation-based intersection control for connected and autonomous vehicles with myopic routing. In all cases, Δ-tolling produces significant benefits over the no-toll case, measured in terms of average travel time and social welfare, while only requiring two parameters to be tuned. Some optimality results are also given for the special case of the static network equilibrium model with BPR-style delay functions.
|Original language||English (US)|
|Number of pages||16|
|Journal||Transportation Research Part C: Emerging Technologies|
|State||Published - Nov 2017|
Bibliographical noteFunding Information:
A portion of this work has taken place in the Learning Agents Research Group (LARG) at UT Austin. LARG research is supported in part by the National Science Foundation (CNS-1330072, CNS-1305287, IIS-1637736, IIS-1651089), ONR (21C184-01), AFOSR (FA9550-14-1-0087), Raytheon, Toyota, AT&T, and Lockheed Martin. Peter Stone serves on the Board of Directors of, Cogitai, Inc. The terms of this arrangement have been reviewed and approved by the University of Texas at Austin in accordance with its policy on objectivity in research. The authors would like to thank the Texas Department of Transportation for supporting this research under project 0-6838, Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas. The authors would also like to acknowledge the support of the Data-Supported Transportation Operations & Planning Center and the National Science Foundation under Grant No. 1254921. Finally, the authors would like to thank the four (anonymous) reviewers of this article for helping improve it.
© 2017 Elsevier Ltd
- Adaptive tolling
- Connected vehicles
- Flow optimization
- Network flow