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Many combinatorial problems can be modelled as Constraint Satisfaction Problems (CSPs). Solving a general CSP is known to be NPcomplete; so that closure and heuristic search are usually used. However, many problems are inherently distributed and the problem complexity can be reduced by dividing the problem into a set of subproblems. Nevertheless, general distributed techniques are not always appropriate to distribute real life problems. In this work, we model the railway scheduling problem by means of domain dependent distributed constraint models and we show that these models maintained better behaviors than general distributed models based on graph partitioning. The evaluation is focussed on the railway scheduling problem, where domain dependent models carry out a problem distribution by means of trains and contiguous set of stations.