Ultimately we would want to calibrate our simulation against an actual
transit system modeled by it. Due to the discrete timestep nature of our
schedule optimization model, the simulation would only be capable of
providing an approximation of a live system's performance. However, if
the live system uses the same schedule optimization algorithm used in
our simulation, we wouldn't expect simulated versus live performance to
differ appreciably unless vehicles run late and passengers miss
connections. The simulation currently does not model these types of
unexpected events, but adding such probabilistic failures to the
simulation shouldn't pose much of a challenge. The challenge lies in
calibrating those probabilities against those that might occur in the
live system due to factors discussed in section
.