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Stephen Boyles

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
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2

AAMAS Conference 2018 Conference Paper

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

  • Hamid Mirzaei
  • Guni Sharon
  • Stephen Boyles
  • Tony Givargis
  • Peter Stone

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 ∆-tolling, was shown to yield up to 32% reduction in total travel time in simulated traffic scenarios compared to when there are no tolls. ∆-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 ∆-tolling which accounts for different β and R values on each link in the network. While this enhanced ∆-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 ∆-tolling reduces total travel time by up to 28% compared to the original ∆-tolling algorithm, and by up to 45% compared to not tolling.

AAAI Conference 2018 Conference Paper

Traffic Optimization for a Mixture of Self-Interested and Compliant Agents

  • Guni Sharon
  • Michael Albert
  • Tarun Rambha
  • Stephen Boyles
  • Peter Stone

This paper focuses on two commonly used path assignment policies for agents traversing a congested network: selfinterested routing, and system-optimum routing. In the selfinterested routing policy each agent selects a path that optimizes its own utility, while the system-optimum routing agents are assigned paths with the goal of maximizing system performance. This paper considers a scenario where a centralized network manager wishes to optimize utilities over all agents, i. e. , implement a system-optimum routing policy. In many real-life scenarios, however, the system manager is unable to influence the route assignment of all agents due to limited influence on route choice decisions. Motivated by such scenarios, a computationally tractable method is presented that computes the minimal amount of agents that the system manager needs to influence (compliant agents) in order to achieve system optimal performance. Moreover, this methodology can also determine whether a given set of compliant agents is sufficient to achieve system optimum and compute the optimal route assignment for the compliant agents to do so. Experimental results are presented showing that in several large-scale, realistic traffic networks optimal flow can be achieved with as low as 13% of the agent being compliant and up to 54%.