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.

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In Proceedings of the 9th International Workshop on Agents in Traffic and Transportation (ATT 2016), New York, NY, USA, July 2016.

@inproceedings{ATT16-guni, title={Delta-Tolling: Adaptive Tolling for Optimizing Traffic Throughput}, author={Guni Sharon and Josiah Hanna and Tarun Rambha and Michael Albert and Peter Stone and Stephen D. Boyles}, booktitle={Proceedings of the 9th International Workshop on Agents in Traffic and Transportation (ATT 2016)}, month={July}, address={New York, NY, USA}, url="http://www.cs.utexas.edu/users/ai-lab?guni:att16", year={2016} }