This paper uses on-line railway travel requests from the iRail schedule-finder application for assessing the suitability of that kind of big data for transportation planning and to examine the temporal and regional variations of the travel demand by train in Belgium. Travel requests are collected over a two-month period and consist of origin-destination flows between stations operated by the Belgian national railway company in 2016. The Louvain method is applied to detect communities of tightly-connected stations. Results show the influence of both the urban and network structures on the spatial organization of the clusters. We also further discuss the implications of the observed temporal and regional variations of these clusters for transportation travel demand and planning.