Many lines in the Swedish railway network are heavily utilized and the demand forfreight transport and passenger traffic is increasing. The Swedish National RailAdministration has been forced to declare some sections to be overloaded andwith the up-coming deregulation of railway traffic, the load is going to increaseeven more.This paper describes how the performance of the entire Swedish railway networkhas been investigated and the results. Data from several different databases(supplied by the Swedish Railway Administration) has been processed. The dataconsists of the scheduled timetable, data about the design of the infrastructureand operational data such as recorded delays, train weights and train lengths.To perform the calculations, the railway network has been divided into 123sections. The division has been made according to traffic patterns and type ofinfrastructure (single-track, double-track). For each line, the data has been usedto calculate several descriptive parameters, e.g. delay development, number oftrains/h during different parts of the day, time for the peak hour, inter-stationdistance, track length, train length, train weight, total mass/day (freight trains),train average speed and the heterogeneity of the train speeds.Together, all of the parameters form a picture of the traffic flows in the Swedishrailway grid and identifies the characteristics of the different lines such as: load,traffic mix, delay situation and infrastructure performance. The information can beused to detect deficiencies and propose possible solutions.Finally, the correlation between the calculated parameters and the delay situationhas been investigated.

Today many railway lines are operated close to maximum capacity. A common question asked is: “How is the quality of operation affected if one additional train/h is scheduled?” With the upcoming deregulation of operation, the necessity to be able to answer this question accurately increases when service operators that are denied train slots due to congestion demand a motivation.

The objective of this paper is to investigate how secondary delays on a congested double-track line depend on several parameters such as:

• Number of trains/h.

• Timetable heterogeneity (speed difference).

• Primary delay levels.

• Inter-station distance.

Each combination of settings of the parameters is investigated to capture possible interaction effects.

The infrastructure model consists of a fictitious double-track line with overtaking stations at regular intervals. A program that generates timetables and perturbation data according to specified input settings is developed. The output of the program is data files that can be read directly by the well-known simulation tool RailSys. This process makes it possible to simulate the hundreds of different timetables that are the result when the parameters investigated are varied. The outcome of the simulations is analysed to find the influence of the investigated parameters on the secondary delays.

Many lines in Sweden have mixed traffic, with both passenger and freight trains. In contrast to passenger trains, freight trains can deviate significantly from their timetable. This study evaluates the effect of modelling freight trains running ahead as well as behind schedule. In previous Swedish studies freight trains have been modelled as being on time or delayed. RailSys is used to simulate a section of the Southern Main Line and a fictive double-track line. Simulation results are compared to data from real train operation. Results show that freight trains can be modelled in a more realistic way. This can improve simulation analysis of freight operations.

The demand for transportation on railways grows for each year and many railway lines are already used close to maximum capacity. One way to increase capacity is to reduce traffic heterogeneity. Heterogeneity is introduced when train services with different speeds operate on the same line. There are many definitions of heterogeneity in literature. Good measures are important in order to be able to quantify capacity lost due to heterogeneity, and consequently how capacity can be gained by reducing it. This paper analyse some of the existing measures as well as introduces a new one, Mean Pass Coefficient (MPC). Other measures analysed are: number of speed levels (SL), speed ratio of fastest to slowest train (SR), mean difference in free running time (MDFR) as well as sum of shortest headway reciprocals (SSHR) and sum of arrival headway reciprocals (SAHR).

Two infrastructure models of double-track lines with overtaking stations spaced at different intervals are simulated. A large number of timetables are created where traffic density as well as the mix of slower and faster trains is varied. Each timetable is characterized using the different definitions of heterogeneity and the results are used in regression analyses to determine their explanatory value with respect to secondary delays created in the simulations. Results show that MPC performs best closely followed by MDFR and SR, while SL is worse when it comes to explaining secondary delays. SSHR and SAHR also show good performance. The performance of the measures increases when primary delays are high, but is unaffected by interstation distance.

Capacity is partially a matter of quality versus quantity. Increasing capacity utilization gives more scheduled waiting time in the timetable as well as more delays when trains are operated. A common way to analyse capacity is to first make a timetable and then use it in a simulation where trains are perturbed. This way it is possible to establish the scheduled waiting time and the expected delays of the trains at the same time as market demands on traffic, e.g. frequency of different train services, are considered. However, setting up simulations are often time consuming and requires a lot of input data.

A method is developed with the objective to improve and speed up capacity analysis of double track railway lines. It can be used for both timetable generation and timetable simulation. The focus of this paper is to present the model and validate that it can simulate timetables accurately. The model has a macroscopic representation of the railway infrastructure and rolling stock while timetables and perturbations are modelled in more detail. When resolving conflicts, the dispatching algorithm can consider several trains and take into account how the dispatching decision affects the possibility to make good decisions at the next station.

The validation is performed using the micro simulation tool RailSys as reference. The validation covers a wide range of scenarios of different traffic density, timetable heterogeneity, inter station distance and primary delays. The validation shows good results and the conclusion is that despite the simplicity of the model, it is accurate enough to use in capacity analysis.