This thesis covers applications and proposes methods for using simulation in a more effectiveway and also in a wider context than normally used. One of the proposed methods deals withdelay modeling that can be used in a calibration process. Furthermore, a method is presentedthat facilitates the management of having timetables, infrastructure scenarios and delays asvariables in simulation studies. The simulation software used in this thesis is RailSys, whichuses a microscopic formulation to describe the infrastructure and train movements.Timetable changes with respect to allowances and buffer times are applied on a real case(Western Main Line) in Sweden in order to analyze how the on-time performance is affectedfor high-speed passenger trains. The potential benefit is that increased allowances and buffertimes will decrease the probability of train interactions and events where the scheduled trainsequence is changed. The on-time performance improves when allowances are increasedand when buffer times concerning high-speed trains are adjusted to at least five minutes inlocations with potential conflicts. One drawback with this approach is that it can consumemore space in a timetable at certain locations, hence other trains may need adjustments inorder reach these buffer times.Setting up simulations, especially in large networks, can take significant amount of timeand effort. One of the reasons is that different types of delay distributions, representingprimary events, are required in order to obtain conformity with reality if a real timetable andnetwork is modeled. Considering train registration data in Sweden, the separation in primaryand secondary delays is not straightforward. The presented method uses the basic trainregistration data to compile distributions of run time deviations for different train groups ina network. The results from the Southern Main Line case study show that a reasonable goodfit was obtained, both for means and standard deviations of delays. A method for capturingthe variance in freight train operations is proposed, partly based on the findings from theaforementioned study. Instead of modeling early freight trains on time, the true initiationdistributions are applied on time-shifted freight trains.In addition to the already mentioned methods, which are applied on real networks, a methodfor reducing the uncertainties coming from assumptions of future conditions is proposed. It isbased on creating combinatorial departure times for train groups and locations and formulatingthe input as nominal timetables to RailSys. The dispatching algorithm implementedin the software can then be utilized to provide feasible, conflict-managed, timetables whichcan be evaluated. This can be followed by operational simulations with stochastic delays ona subset of the provided timetables. These can then consequently be evaluated with respectto mean delays, on-time performance etc.To facility the use of the infrastructure as a variable in these type of studies, an infrastructuregenerator is developed which makes it relatively easy to design different station layouts andproduce complete node-link structures and other necessary definitions. The number, locationand type of stations as well as the linking of stations through single-track or multi-tracksections can be done for multiple infrastructure scenarios. Although the infrastructure canbe defined manually in RailSys, a considerably amount of time and effort may be needed.In order to examine the feasibility of this method, case studies are performed on fictive linesconsisting mostly of single-track sections. This shows that the method is useful, especiallywhen multiple scenarios are studied and the assumptions on timetables consist of departureintervals for train groups and their stop patterns.

In this thesis the symptoms and underlying behaviour of congestion on railways are analysed and discussed. As well as in many other countries, Sweden faces increasing demand for transportation. To meet this new demand, railways play an important role. Today, the capacity of the Swedish rail network is not upgraded at the pace necessary to keep up with the increase in traffic demand. The sensitivity of the railway system rises as the capacity utilisation increases. At some point maximum capacity is reached when the marginal gain of operating one extra train is lower than the costs in terms of longer travel times and increased sensitivity to delays.

Several different methodologies are employed in this thesis to analyse capacity. The first uses real data from the Swedish rail network, train operation and delays to analyse how different factors influence available capacity and train delays. Several useful key performance indicators are defined to describe capacity influencing properties of the infrastructure and the rail traffic. The rail network is divided into subsections for which the indicators have been estimated. This makes it possible to discern their different characteristics and identify potential weaknesses.

The second approach employs the railway simulation tool RailSys in extensive simulation experiments. This methodology is used to analyse the characteristics of double-track operation. Simulation of several hundred scenarios are conducted to analyse the influence of traffic density, traffic heterogeneity, primary delays and inter-station distance on secondary delays, used timetable allowance and capacity. The analysis gives an in-depth understanding of the mechanisms of railway operation on double-track lines.

A simulation model for strategic capacity evaluation, TigerSim, is developed that can be used to speed up and improve capacity planning and evaluation of future infrastructure and timetables designs on double-track railway lines. For a given infrastructure and plan of operation, the model can be used to generate and simulate a larger number of timetables. This gives two major advantages:

• Using many timetables makes results general
• It is possible to consider both static and dynamic properties of the timetables in the capacity analysis

The first aspect is especially useful in the evaluation of future scenarios as the timetable then often is unknown. The second is an advantage since an improvement in capacity can be measured in a combination of increased frequency of service, shorter travel time and reduced delays. The output of the model can either be used to directly determine capacity from a quality of service perspective, or used as input to cost-benefit analysis (CBA).