An agent-based approach for road pricing: system-level performance and implications for drivers

Abstract

Road pricing is a useful mechanism to align private utility of drivers with a system-level measure of performance. Traffic simulation can be used to predict the impact of road pricing policies. The simulation is not a trivial task because traffic is a social system composed of different interacting entities. To tackle this complexity, agent-based approaches can be employed to model the behavior of the several actors in transportation systems. We model traffic as a multiagent system in which link manager agents employ a reinforcement learning scheme to determine road pricing policies in a road network. Drivers who traverse the road network are cost-minimizer agents with local information and different preferences regarding travel time and credits expenditure. The vehicular flow achieved by our reinforcement learning approach for road pricing is close to a method where drivers have global information of the road network status to choose their routes. Our approach reaches its peak performance faster than a fixed pricing approach. Moreover, drivers’ welfare is greater when the variability of their preferences regarding minimization of travel time or credits expenditure is higher. Our experiments showed that the adoption of reinforcement learning for determining road pricing policies is a promising approach, even with limitations in the driver agent and link manager models.

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