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In the present work, our driver agent is modeled regarding route choice only.
Our driver agent is limited in relation to the update rule of drivers' knowledge.
Section 'Methods' presents our proposed approach divided in the driver agent, link manager agent, and simulation models.
Also, a more accurate driver agent model in relation to the knowledge update rule can be used.
Another extension of the driver agent model is related to its cost function: currently, it is a linear relation of travel time and credits expenditure.
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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In our model, similarly to [10, 11], driver agents are heterogeneous, that is, different drivers can evaluate costs in distinct ways (see Section 'Driver agent model').
The market consists in driver agents purchasing reservations to cross the intersections from intersection manager agents.
For simplicity, we have assumed that all driver agents receive the same rate of new topics.
Driver agents use their cars to move between their demographically assigned home neighborhoods and their jobs.
Fig. 2 Diffusion results for the selection of high- or low-degree driver agents.
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