  We study a robust toll pricing problem where toll setters and users have
different level of information when taking their decisions. Toll setters do not
have full information on the costs of the network and rely on historical
information when determining toll rates, whereas users decide on the path to
use from origin to destination knowing toll rates and having, in addition, more
accurate traffic data. Toll setters often also face constraints on price
experimentation which means less opportunity for price revision. Motivated by
this we propose a novel robust pricing methodology for fixing prices where we
take non-adversarial view of nature different from the existing robust
approaches. We show that our non-adversarial robustness results in less
conservative pricing decisions compared to traditional adversarial nature
setting. We start by first considering a single origin-destination parallel
network in this new robust setting and formulate the robust toll pricing
problem as a distributionally robust optimization problem, for which we develop
an exact algorithm based on a mixed-integer programming formulation and a
heuristic based on two-point support distribution. We further extend our
formulations to more general networks and show how our algorithms can be
adapted for the general networks. Finally, we illustrate the usefulness of our
approach by means of numerical experiments both on randomly generated networks
and on the data recorded on the road network of the city of Chicago.
