When railway traffic is perturbed by unexpected events, an appropriate train routing and scheduling may be applied to minimize delay propagation. In the literature, several algorithms have been proposed and tested in various traffic situations. However, their performance have almost always been studied neglecting the imperfect knowledge of precise traffic conditions which is almost impossible to avoid in reality. In this paper, we propose an empirical analysis assessing the applicability of these algorithms in case of imperfect information. In particular, we consider RECIFE-MILP as a traffic management algorithm and advanced or delayed train entrance times in the infrastructure as a source of imperfect information. The results show that the application of traffic management optimization allows outperforming the first-come-first-served management strategy even if the actual traffic conditions are not perfectly known by the optimization algorithm.