Network-wide adaptive tolling for connected and automated vehicles

Guni Sharon; Michael W. Levin; Josiah P. Hanna; Tarun Rambha; Stephen D. Boyles; Peter Stone

doi:10.1016/j.trc.2017.08.019

Network-wide adaptive tolling for connected and automated vehicles

Guni Sharon, Michael W. Levin, Josiah P. Hanna, Tarun Rambha, Stephen D. Boyles, Peter Stone

Civil, Environmental, and Geo- Engineering

Research output: Contribution to journal › Article › peer-review

38 Scopus citations

Abstract

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)
Pages (from-to)	142-157
Number of pages	16
Journal	Transportation Research Part C: Emerging Technologies
Volume	84
DOIs	https://doi.org/10.1016/j.trc.2017.08.019
State	Published - Nov 2017

Bibliographical note

Funding 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.

Publisher Copyright:
© 2017 Elsevier Ltd

Keywords

Adaptive tolling
Connected vehicles
Flow optimization
Micro-tolling
Network flow

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.trc.2017.08.019

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Cite this

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title = "Network-wide adaptive tolling for connected and automated vehicles",

abstract = "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.",

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author = "Guni Sharon and Levin, {Michael W.} and Hanna, {Josiah P.} and Tarun Rambha and Boyles, {Stephen D.} and Peter Stone",

note = "Funding 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. Publisher Copyright: {\textcopyright} 2017 Elsevier Ltd",

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AU - Boyles, Stephen D.

AU - Stone, Peter

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N2 - 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.

AB - 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.

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KW - Network flow

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ER -

Network-wide adaptive tolling for connected and automated vehicles

Abstract

Bibliographical note

Keywords

UN SDGs

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