  In the context of public transport modeling and simulation, we address the
problem of mismatch between simulated transit trips and observed ones. We point
to the weakness of the current travel demand modeling process; the trips it
generates are over-optimistic and do not reflect the real passenger choices. We
introduce the notion of mini activities the travelers do during the trips; they
can explain the deviation of simulated trips from the observed trips. We
propose to mine the smart card data to extract the mini activities. We develop
a technique to integrate them in the generated trips and learn such an
integration from two available sources, the trip history and trip planner
recommendations. For an input travel demand, we build a Markov chain over the
trip collection and apply the Monte Carlo Markov Chain algorithm to integrate
mini activities in such a way that the selected characteristics converge to the
desired distributions. We test our method in different settings on the
passenger trip collection of Nancy, France. We report experimental results
demonstrating a very important mismatch reduction.
