Moving block signalling promises a significant reduction of the infrastructure occupation compared to a fixed block system, such as NS’54/ATB. This is mainly caused by a strong reduction of the approach and running time. However, to assess the capacity gains track occupation data, such as blocking times are needed. ETCS L3 Moving Block is still in development, so gathering data out of daily operations is not possible. Also gathering moving block data out of simulation isn’t always a convenient solution. For example, in simulation software FRISO, used at ProRail, ETCS L3 Moving Block is not (yet) implemented.
With infrastructure data, rolling stock parameters and planned time-distance data, blocking times for a moving block signalling system can be estimated. The model presented in this thesis has an average error of 0.87s to the blocking time. In 95% of the cases the error is within a range of (-3,3) seconds. Given that ProRail plans with a precision of 6 seconds, it can be concluded that the model both precise as accurate. With the blocking times of all trains, bottlenecks in both railway corridors and complex nodes can be identified. One could sum all blocking times per block and consider blocks with the highest summed blocking times as bottleneck. However, this can only be applied for homogeneous traffic situations. Another approach is identifying bottlenecks by the shortest buffer time between two trains, also called a critical block. This can be applied for both homogeneous as heterogeneous traffic situations. An advantage is that it is not needed to split corridors into line sections. One could analyse a whole network at once and identify bottleneck at a microscopic level. By keeping the same timetable, the buffer time between two trains increases on average by 75 seconds (60%) using moving block over NS’54.