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Link-based Parameterized Micro-tolling Scheme for Optimal Traffic Management

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Peter StoneAuthors Info & Claims

AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

Pages 2013 - 2015

Published: 09 July 2018 Publication History

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Abstract

In the micro-tolling paradigm, different toll values are assigned to different links within a congestible traffic network. Self-interested agents then select minimal cost routes, where cost is a function of the travel time and tolls paid. A centralized system manager sets toll values with the objective of inducing a user equilibrium that maximizes the total utility over all agents. A recently proposed algorithm for computing such tolls, denoted Δ- tolling, was shown to yield up to 32% reduction in total travel time in simulated traffic scenarios compared to when there are no tolls. Δ- tolling includes two global parameters: β which is a proportionality parameter, and R which influences the rate of change of toll values across all links. This paper introduces a generalization of Δ- tolling which accounts for different β and R values on each link in the network. While this enhanced Δ- tolling algorithm requires setting significantly more parameters, we show that they can be tuned effectively via policy gradient reinforcement learning. Experimental results from several traffic scenarios indicate that Enhanced Δ- tolling reduces total travel time by up to 28% compared to the original Δ- tolling algorithm, and by up to 45% compared to not tolling.

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Cited By

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Qiu WChen HAn B(2019)Dynamic electronic toll collection via multi-agent deep reinforcement learning with edge-based graph convolutional networksProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367678(4568-4574)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367471.3367678

Index Terms

Link-based Parameterized Micro-tolling Scheme for Optimal Traffic Management
1. Applied computing
  1. Operations research
    1. Transportation
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning

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Published In

AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

July 2018

2312 pages

General Chairs:
Elisabeth Andre
Augsburg University, Germany
,
Sven Koenig
University of Southern California, USA
,
Program Chairs:
Mehdi Dastani
Utrecht University, Netherlands
,
Gita Sukthankar
University of Central Florida, USA

In-Cooperation

ACM: Association for Computing Machinery

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 09 July 2018

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AAMAS '18

Sponsor:

SIGAI

AAMAS '18: Autonomous Agents and MultiAgent Systems

July 10 - 15, 2018

Stockholm, Sweden

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AAMAS '18 Paper Acceptance Rate 149 of 607 submissions, 25%;

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

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Qiu WChen HAn B(2019)Dynamic electronic toll collection via multi-agent deep reinforcement learning with edge-based graph convolutional networksProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367678(4568-4574)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367471.3367678

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