Rature, for example the C-Logit (Cascetta et al., 1996), Path Size Logit (Ben-Akiva and Bierlaire, 1999), Link-Nested Logit (Vovsha and Bekhor, 1998) and subnetwork (Frejinger and Bierlaire, 2006) models. A latent cho- sen route corresponds to an unobserved choice where only an approximate choice description is available. Instead of an exact route description, trav- ellers describe their choice in terms of a sequence of locations and cities that they have traversed, without the need to relate the actual network used by the analyst. We compute the probability of an (aggregate) observation with an un- derlying route choice model using a detailed network description and actual paths. In this context, not only several routes can correspond to the same observation, but the exact origin-destination pair is not necessarily known. We therefore consider several possible origin-destination pairs and their as- sociated set of routes, generated by a choice set generation algorithm. We derive from this list the probability of each observation, in order to perform the maximum likelihood estimation of the route choice model. The methodology is illustrated by estimating Path Size Logit and sub- network models using a dataset collected in Switzerland. This application is one of few based on revealed preferences (RP) data that are presented in the literature. In addition, the network used here (39411 unidirectional links and 14841 nodes) is to our knowledge the largest network used for evaluation of route choice models based on RP data. The estimation results are very sat- isfactory. Indeed, they do not only show that it is possible to estimate route choice models based on aggregate observations, but also that the parameter estimates are stable across different types of models and that the standard errors are small.