  Traffic management by applying congestion pricing is a measure for mitigating
congestion in protected city corridors. As a promising tool, pricing improves
the level of service in a network and reduces travel delays. However,
real-world implementations are restricted to static pricing, i.e., the price is
fixed and not responsive to the prevailing regional traffic conditions. Dynamic
pricing overcomes these limitations but also affects the users route choices.
This work uses dynamic pricing's influence and predicts pricing functions to
aim for a system optimal traffic distribution. The framework models a
large-scale network where every region is considered homogeneous, allowing for
the Macroscopic Fundamental Diagram (MFD) application. We compute Dynamic
System Optimum (DSO) and a Quasi Dynamic User Equilibrium (QDUE) of the
macroscopic model by formulating a linear optimization problem and utilizing
the Dijkstra algorithm and a Multinomial Logit model (MNL), respectively. The
equilibria allow us to find an optimal pricing methodology by training
Multi-Layer-Neural (MLN) network models. We test our framework on a case study
in Zurich, Switzerland, and showcase that (a) our neural network model learns
the complex user behavior and (b) allows predicting optimal pricing functions.
Results show a significant performance improvement when operating a
transportation network in the DSO and highlight how dynamic pricing influences
the user's route choice behavior towards the system optimal equilibrium.

Robotics;Computational Engineering, Finance, and Science