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Capacity calculations are essential for the long-term planning of railway infrastructure. Many of the methods currently used in practice calculate characteristic capacity values separately for the single elements (mainly lines and nodes) of a railway network. Approaches that consider the entire network to account for interactions between the elements often rely on many assumptions, which renders a direct practical application difficult. This paper therefore introduces a linear optimization model that comprises railway-specific constraints such as minimum headway times, line capacities, and route conflicts. Under the consideration of these constraints, the developed model permits the calculation of a network-wide capacity. The model is validated on a sample network that is based on a real network of the German railway infrastructure. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a fully working, easily usable algorithm for planning the placement of the trackside balise groups for ETCS level 1 applications to transmit infill information to the passing trains by minimizing the operational impact. This paper presents a method to determine the optimal positions of infill balise groups for applications of European Train Control System (ETCS) level 1. ETCS will be the only train protection system in the European Union of the future. Level 1 specifically enables a less complex system while retaining full compatibility to and interoperability of ETCS level 2. In level 1, the Movement Authority (MA), which contains information on how far and at what speed the train is authorized to run, is transmitted at fixed locations where switchable radio beacons—infill balise groups—are placed. Due to technical and economical constraints, their locations need to be optimized so that they provide the maximum operational benefit. Such a method with universal adaptability has not been available to date, so we developed an algorithm that determines the optimal locations of infill balise groups. The algorithm uses the basic laws of motion combined with domain knowledge about railways in general and specifically ETCS. All important parameters are considered while retaining simplicity in the application, making it convenient to use for planning new ETCS deployments and as a foundation for future research on ETCS level 1. The algorithm was implemented using Python and tested with real-world train characteristics. [ABSTRACT FROM AUTHOR]

Because of the long planning periods and their long life cycle, railway infrastructure has to be outlined long ahead. At the present, the infrastructure is designed while only little about the intended operation is known. Hence, the timetable and the operation are adjusted to the infrastructure. Since space, time and money for extension measures of railway infrastructure are limited, each modification has to be done carefully and long lasting and should be appropriate for the future unknown demand. To take this into account, we present the robust network design problem for railway infrastructure under capacity constraints and uncertain timetables. Here, we plan the required expansion measures for an uncertain long‐term timetable. We show that this problem is NP‐hard even when restricted to bipartite graphs and very simple timetables and present easier solvable special cases. This problem corresponds to the fixed‐charge network design problem where the expansion costs are minimized such that the timetable is conductible. We model this problem by an integer linear program using time expanded networks. To incorporate the uncertainty of the future timetable, we use a scenario‐based approach. We define scenarios with individual departure and arrival times and optional trains. The network is then optimized such that a given percentage of the scenarios can be operated while minimizing the expansion costs and potential penalty costs for not scheduled optional trains. [ABSTRACT FROM AUTHOR]

This paper presents a model that establishes a relationship between the financial resources allocated to maintain and replace railway station infrastructure assets and the resulting infrastructure quality. Until now, there has been no known relationship between quality and financial resources, making targeted planning for railway stations and the control of financial resources challenging. This model aims to predict infrastructure quality by analyzing the age-related degradation of infrastructure assets using historical data. The effects of maintenance and replacement measures are implemented in the model to map quality-improving measures. This allows for the comparison and analysis of different budget allocation scenarios on different assets, with different approaches for prioritizing replacement and maintenance measures. In this paper, the influence of budget allocation and prioritization strategies on station infrastructure quality is quantified by comparing different scenarios. Based on the results of the analysis, a quality assessment method can be evaluated, and disincentives can be identified. The analyses in this paper are carried out for quality measurement defined in Germany and the German railway station infrastructure. [ABSTRACT FROM AUTHOR]