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

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.

Authors

• Hamid Mirzaei University of California, Irvine
• Guni Sharon Texas A&M University
• Stephen Boyles
• Tony Givargis
• Peter Stone The University of Texas at Austin Sony AI

Keywords

• Micro-tolling
• Policy Gradient
• Reinforcement Learning