Alex Landex

Practical use of the UIC 406 Capacity Leaflet by including Timetable Tools in the Investigations

This paper describes the relatively new UIC 406 method, which calculates capacity consumption on railway lines in Denmark. The UIC 406 method is an easy and effective way of calculating the capacity consumption, but it is possible to expound the UIC 406 method in different ways which can lead to different capacity consumptions. This paper describes how the UIC 406 method is expounded in Denmark, and it also describes the importance of choosing the right length of the line sections examined and how line sections with multiple track sections are examined. Furthermore, the possibility of using idle capacity to run more trains is examined. At the end of the paper a method to examine the expected capacity utilization of future timetables is presented. The method is based on the plan of operation instead of the exact (known) timetable.

Measures for track complexity and robustness of operation at stations

Abstract Stations are often limiting the capacity of a railway network. However, most capacity analysis methods focus on open line capacity. This paper presents methods to analyse and describe stations by the use of complexity and robustness measures at stations. Five methods to analyse infrastructure and operation at stations are developed in the paper. The first method is an adapted UIC 406 capacity method that can be used to analyse switch zones and platform tracks at stations with simple track layouts. The second method examines the need for platform tracks and the probability that arriving trains will not get a platform track immediately at arrival. The third method is a scalable method that analyses the conflicts and the infrastructure complexity in the switch zone(s). The fourth method can be used to examine the complexity and the expected robustness of timetables at a station. The last method analyses how optimal platform tracks are used by examining the arrival and departure patterns of the trains. The developed methods can be used to analyse a station to gain comprehensive knowledge about the capacity and complexity of the different elements at the station.

Capacity Measurement with the UIC 406 Capacity Method

This paper describes the fast and effective UIC 406 method for calculating capacity consumption on railway lines. It is possible to expound the UIC 406 method in different ways which can lead to different capacity consumptions. Therefore, this article describes how the methodology is expounded in Denmark. This includes how and where to divide the railway lines into line sections, how to analyze stations and junctions, and how to examine line sections with different amounts of tracks.

Simulation of Disturbances and Modelling of Expected Train Passenger Delays

This paper describes how forecasting of regularity for railway systems have traditionally – if at all – been computed for trains, not for passengers. It has only relatively recently become possible to model and evaluate the actual passenger delays. This paper describes how it is possible to use a passenger regularity model to estimate the actual passenger delays. The combination of the passenger regularity model with railway simulation software is described, demonstrating the possibility of predicting future passenger delays. The described passenger regularity model is run daily to calculate the passenger delays of the Copenhagen suburban rail network the previous day. The results obtained with the passenger regularity model used together with the simulation software are very similar to the daily calculated passenger regularity of the Copenhagen suburban network. As the combined method includes simulation software and reflects the actual passenger regularity, it is possible to use a combination of a passenger regularity model and simulation software to evaluate and compare future scenarios.

The Network Effects of Railway Investments

This paper describes how network effects occur when a change at one place in the railway network results in changes elsewhere in the network, and they may be far away from the original change. Railway investments have network effects, and therefore, this paper describes the network effects and how these network effects can be examined by queuing time. This paper gives examples of network effects and describes the importance of the size of the analysis area and the connections between trains in the railway network.

Catchment Areas for Public Transport

This paper on catchment areas used for public transport is from the proceedings of 14th International Conference on Urban Transport and the Environment in the 21st Century, which was held in Malta in 2008. The authors explain that, in the planning of public transport, the catchment areas of stops are often included to estimate the potential number of travelers. They describe different approaches to GIS-based catchment area analyses, including the Circular Buffer approach which is the fundamental, but also the simplest approach; the Service Area approach, which is based on searches in road networks, represents the actual feeder routes, and is a more detailed approach; and the Service Area approach, which can be refined by adding additional resistance to certain points in the road network, e.g. stairways. The authors illustrate differences between the Circular Buffer approach and the Service Area approach and compare the sizes of the resulting catchment areas. One case example illustrates the strength of the Service Area approach and the impact on the catchment area when adding additional time resistance for the crossing of stairways. Another case example illustrates how the additional time resistance in stairways affects the catchment area of an underground station compared to a ground-level station. The benefits of catchment area analyses include improved planning of stops on a new line by calculating travel potential along the line; the prevention of inaccessible areas from being included in the catchment area; and the allowance of detours in feeder routes to/from stations. The authors conclude that GIS-based catchment area analyses are a beneficial multiple decision support tool for the planning of public transport where the level of detail can be suited to the purpose.

Network Effects In Railways

An alteration in one part of a railroad network can affect other parts of the network. This is known as a network effect. Network effects happen when train runs are very long and the railroad system has many interdependencies. This paper discusses network effects (1) in general, and (2) for trains specifically, measured by scheduled waiting time. The paper also describes the occurrence of network effects for passengers, and the measurement of same by passenger delay models.

Revising the UIC 406 method: Revenue generating capacity

The UIC 406 method has been published by the International Union of Railways (UIC) in 2004 and quantifies capacity utilisation for a given infrastructure. It is generally suitable for capacity analysis in European railways that are focused mainly on passenger operations and run according to exact timetables. This method defines railway capacity as “the total number of possible paths in a defined time window, considering the actual path mix or known developments respectively”. To measure the railway capacity consumption, timetable graphs can be used where by the given infrastructure and the type of rolling stock are implicitly included as they determine the size of the blocking stairs. The capacity consumption is measured by compressing the timetable graphs so that the buffer times are equal to zero. This paper adopts a system approach toward railways and describes the interactions between its different subsystems. Infrastructure management is discussed in the context of performance evaluation. The significance of timetable is stressed as what links all railway subsystems together, determining performance of the whole railway system. Timetable characteristics that affect railway capacity are discussed followed by current theoretical methods to evaluate railway capacity by compressing the timetable. Some of the weaknesses of the UIC 406 method are identified and discussed. To tackle them, the authors define a new term as “Revenue generating capacity” that analyses capacity utilisation according to an estimation of revenue, taking into account wider socio-economic benefits. A weighted UIC method that considers a proper weight for different types of trains that takes into account the revenue generated based on the train type, load factor as well as probability and costs of delays is suggested for further research.Copyright

Simulation of disturbances and modelling of expected train passenger delays

Forecasts of regularity for railway systems have traditionally – if at all – been computed for trains, not for passengers. It has only relatively recently become possible to model and evaluate the actual passenger delays. This paper describes how it is possible to use a passenger regularity model to estimate the actual passenger delays. The combination of the passenger regularity model with railway simulation software is described, demonstrating the possibility of predicting future passenger delays. The described passenger regularity model is run daily to calculate the passenger delays of the Copenhagen suburban rail network the previous day. The results obtained with the passenger regularity model used together with the simulation software are very similar to the daily calculated passenger regularity of the Copenhagen suburban network. As the combined method includes simulation software and reflects the actual passenger regularity, it is possible to use a combination of a passenger regularity model and

Evaluation of Robustness Indicators Using Railway Operation Simulation

The classical way of evaluating the robustness of railway timetables is the use of microscopic simulation. This is precise and offers a high level of detail, but it also requires a high amount of work. The alternative is to use robustness indicators that directly or indirectly indicate the robustness of a railway system. However, the semantics of these are mainly unknown and indicators are therefore best for comparison of alternatives. The paper therefore reviews and evaluates different robustness indicators against a microscopic simulation. This evaluation show that the indicators compare well to the microscopic simulation and are, to some extent, able to predict the outcome of the simulation.