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Guni Sharon, Josiah Hanna, Tarun Rambha, Michael Albert, Peter
Stone, and Stephen D. Boyles. Delta-Tolling:
Adaptive Tolling for Optimizing Traffic Throughput. In Proceedings of the 9th International Workshop on Agents in Traffic
and Transportation (ATT 2016), July 2016.
ATT 2016: Ninth International Workshop
on Agents in Traffic and Transportation
In recent years, the automotive industry has been rapidly advancing towardconnected vehicles with higher degrees of autonomous capabilities. Thistrend opens up many new possibilities for AI-based efficient trafficmanagement. This paper investigates traffic optimization through thesetting and broadcasting of dynamic and adaptive tolls under the assumptionthat the cars will be able to continually reoptimize their paths as tollschange. Previous work has studied tolling policies that result in optimal traffic flowand several traffic models were developed to compute such tolls.Unfortunately, applying these models in practice is infeasible due to thedynamically changing nature of typical traffic networks. Moreover, this papershows that previously developed tolling models that were proven to yieldoptimal flow in theory may not be optimal in real-life simulation. Next, thispaper introduces an efficient tolling scheme, denotedDelta-tolling, for setting dynamic and adaptive tolls. We evaluatethe performance of Delta-tolling using a traffic micro-simulator.Delta-tolling is shown to reduce average travel time by up to 35\% overusing no tolls and by up to 17\% when compared to the current state-of-the-arttolling scheme.
@InProceedings{ATT16-guni,
author = {Guni Sharon and Josiah Hanna and Tarun Rambha and Michael Albert and Peter Stone and Stephen D.\ Boyles},
title = {Delta-Tolling: Adaptive Tolling for Optimizing Traffic Throughput},
booktitle = {Proceedings of the 9th International Workshop on Agents in Traffic and Transportation (ATT 2016)},
location = {New York, NY, USA},
month = {July},
year = {2016},
abstract = {
In recent years, the automotive industry has been rapidly advancing toward
connected vehicles with higher degrees of autonomous capabilities. This
trend opens up many new possibilities for AI-based efficient traffic
management. This paper investigates traffic optimization through the
setting and broadcasting of dynamic and adaptive tolls under the assumption
that the cars will be able to continually reoptimize their paths as tolls
change.
Previous work has studied tolling policies that result in optimal traffic flow
and several traffic models were developed to compute such tolls.
Unfortunately, applying these models in practice is infeasible due to the
dynamically changing nature of typical traffic networks. Moreover, this paper
shows that previously developed tolling models that were proven to yield
optimal flow in theory may not be optimal in real-life simulation. Next, this
paper introduces an efficient tolling scheme, denoted
Delta-tolling, for setting dynamic and adaptive tolls. We evaluate
the performance of Delta-tolling using a traffic micro-simulator.
Delta-tolling is shown to reduce average travel time by up to 35\% over
using no tolls and by up to 17\% when compared to the current state-of-the-art
tolling scheme.
},
wwwnote={ATT 2016: <a href="http://ceur-ws.org/Vol-1678/">Ninth International Workshop on Agents in Traffic and Transportation</a>},
}
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