Background: 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. Methods: 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. Results: 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. Conclusions: 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.
['Anderson Rocha Tavares', 'Ana L. C. Bazzan']
An agent-based approach for road pricing: system-level performance and implications for drivers