  This paper analyzes how the presence of service stations on highways affects
traffic congestion. We focus on the problem of optimally designing a service
station to achieve beneficial effects in terms of total traffic congestion and
peak traffic reduction. Microsimulators cannot be used for this task due to
their computational inefficiency. We propose a genetic algorithm based on the
recently proposed CTMs, that efficiently describes the dynamics of a service
station. Then, we leverage the algorithm to train a neural network capable of
solving the same problem, avoiding implementing the CTMs. Finally, we examine
two case studies to validate the capabilities and performance of our
algorithms. In these simulations, we use real data extracted from Dutch
highways.
