  We present an integrated microsimulation framework to estimate the pedestrian
movement over time and space with limited data on directional counts. Using the
activity-based approach, simulation can compute the overall demand and
trajectory of each agent, which are in accordance with the available partial
observations and are in response to the initial and evolving supply conditions
and schedules. This simulation contains a chain of processes including:
activities generation, decision point choices, and assignment. They are
considered in an iteratively updating loop so that the simulation can
dynamically correct its estimates of demand. A Markov chain is constructed for
this loop. These considerations transform the problem into a convergence
problem. A Metropolitan Hasting algorithm is then adapted to identify the
optimal solution. This framework can be used to fill the lack of data or to
model the reactions of demand to exogenous changes in the scenario. Finally, we
present a case study on Montreal Central Station, on which we tested the
developed framework and calibrated the models. We then applied it to a possible
future scenario for the same station.
