<p>We aim to model passenger movements within communicating coaches equipped with infra-red sensors at each door, counting the numbers of passengers boarding and alighting at that door. The business objective is to better estimate the real occupancy rate of each coach than solely using boarding counts and discarding passenger movements. To do so, we propose modelings based on stochastic transition matrices, that are specific to each station in the most complex modeling. The latter, called a local modeling, also has to estimate alighting counts, which it does through data-based alighting rates rather than with origin–destination matrices. This piece of the methodology is of independent interest. The local modeling may actually be seen as a neural network (a recurrent neural network with a many-to-many architecture featuring one hidden layer). All modelings are fit through least-squares minimizations. We evaluate them both qualitatively and quantitatively, on data from line H of the suburban railway network of the Greater Paris area. The qualitative evaluation consists of successfully interpreting the outcomes of the models (transition matrices, alighting rates) based on the geographies of the platforms of the boarding or alighting stations. The quantitative evaluation consists of using the models constructed to forecast alighting counts: modeling passenger movements improves the forecasting performance by about at least 15% compared to ignoring the existence of such movements. All in all, this study backs up the upgrade of the passenger-movement modeling layer in the real-time crowding information deployed in the Greater-Paris area, from the global modeling currently used to a local modeling.</p>