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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.

Despite the fact that freight transport has a huge impact on the economy and the environment, datasets have always been scarce or restricted to very small a-spatial samples. We here aim at diverting spatial data collected in Belgium for tolling trucks proportionally of their use of the road network, and at further extracting geographical structures and dynamics out of this massive dataset. The paper first relates to the numerous problems encountered when using and transforming big data generated by On Board Units GNSS (cleaning, transforming and preprocessing), second it maps and comments movements (traffic) and stops of trucks within the entire country, and finally partitions the country into mathematical communities of places that most interact. Analyses are performed for the complete sample, but also for sub-categories based on the country of registration underlining the spatial specificities of freight transit in Belgium. This exploratory spatial data analysis enables to reveal multi-level spatial structures associated to urban hierarchies, transport infrastructure but also firm locations or political organizations, tickling the complexity and interconnectivity of any measure taken for a more sustainable future.