This MSc thesis research has the objective to empirically determine the perceived stochastic nature of the deceleration regimes and to determine the impact it has on infrastructure occupation within the network corridor. This research expands on the data-driven reconstruction model to estimate the speed profiles of realised train runs developed by N. Besinovic et al., calling it the 'Deceleraton Reconstruction' (DR) model. This model elaborates on the deceleration regimes of the speed profiles in a more dynamic and generalised manner to provide a more detailed description of the realised deceleration behaviour, through introducing the concept of sub-regimes to describe the deceleration regimes and the concept of a non-uniform braking rate in the braking regimes. This research expands on the number of data sources used for location tracking of the realised train runs and introduces the concept of 'Data Fusion' in which the different sources of location tracking are combined in order to provide a more reliable and more detailed input for the DR model. The results of the empirical and comparative analysis, provide interesting insights to the correlations between the departure delays, realised running times and deceleration loss time performance, and provides insights to the impact the deceleration behaviour has on infrastructure occupation. The DR model provides interesting results regarding the quality and detail of the estimated deceleration behaviour (e.g. braking rate, deceleration regime profile) and compares these in relation to the nominal characteristics used in timetabling tools and simulation models.