Link-based parameterized micro-tolling scheme for optimal traffic management
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2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. 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 A-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: 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 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.
