  In Part I of this paper series, several macroscopic traffic model elements
for mathematically describing freeway networks equipped with managed lane
facilities were proposed. These modeling techniques seek to capture at the
macroscopic the complex phenomena that occur on managed lane-freeway networks,
where two parallel traffic flows interact with each other both in the physical
sense (how and where cars flow between the two lane groups) and the
physiological sense (how driving behaviors are changed by being adjacent to a
quantitatively and qualitatively different traffic flow).
  The local descriptions we developed in Part I are not the only modeling
complexity introduced in managed lane-freeway networks. The complex topologies
mean that network-scale modeling of a freeway corridor is increased in
complexity as well. The already-difficult model calibration problem for a
dynamic model of a freeway becomes more complex when the freeway becomes, in
effect, two interrelating flow streams. In the present paper, we present an
iterative-learning-based approach to calibrating our model's physical and
driver-behavioral parameters. We consider the common situation where a complex
traffic model needs to be calibrated to recreate real-world baseline traffic
behavior, such that counterfactuals can be generated by training purposes. Our
method is used to identify traditional freeway parameters as well as the
proposed parameters that describe managed lane-freeway-network-specific
behaviors. We validate our model and calibration methodology with case studies
of simulations of two managed lane-equipped California freeways.
