Recently, railway-infrastructure managers and suppliers have launched major programs with the aim to revise – or even reinvent – the overall interlocking architectures plus adjacent systems and operational principles. Representatives of such initiatives are “SmartRail 4.0” and “Digitale Schiene Deutschland”. Those programs are backed-up by a set of motivations, namely: Existing command and control technology is overaged or becomes outdated, skills to maintain technology get lost due to demographic aging, applied technology is expensive and does not allow any further capacity gain.Since capacity improvement is a core target of all programs, there is a severe need to express the capacity impact of the related system architecture. Such quantifications serve the broad portfolio from political decision processes to detailed requirement specifications.This article describes necessary extensions of the blocking-time model to meet the requirements of future interlocking architectures in detail. It contributes to extent the standardised blocking-time model in such a manner, that its applicability is ensured in different setups. Besides providing and extending the theoretic background, the article provides examples of practical computations and applications. They cover sensitivity analysis to elaborate the decisive impact parameters on headway times as well as timetabling-studies based on a future technology setup.
Thanks to a direct operation on random variables and a suitable class of cumulative distribution functions, the network-wide computation of delay propagation in railway networks is enabled in short computation times. On this basis, new work procedures to evaluate timetable robustness during the annual capacity allocation phase, to assess the interactions between temporary speed restrictions and to forecast the quality of operations have been established at European infrastructure managers. In their practical application the need for extensions appeared: Firstly, concepts of passenger services are often elaborated in detail also for long-term purposes while, in contrast, knowledge on freight services solely exists in numbers of trains per relation and time. Assuring purposeful indicators demands a consideration of interaction with freight services by linking the train-path representation of delay propagation with the stream-based modelling of freight services. By applying methods of queuing theory it proves possible to estimate the impact of "unknown" train paths and to incorporate the load-dependent spreading of knock-on delays to the passenger service concept. Secondly, there is a need of fine-tuning the delay-propagation model to consider the relationship between infrastructure utilisation and train priorities. By manipulating the inequations underlying the stochastic operations, a relationship of the dispatching regime to the server load is created, allowing a closer fitting of simulation results to reality.
Bottlenecks have significant impact on routing options and network exploitation. Therefore, it is essential to know the location of critical areas in order to use the existing infrastructure more efficiently and to expand the network appropriately. This paper presents a method to identify bottlenecks based on delays and to weight them by their significance. As delays can be derived from operational data or from simulations, statements about past operating conditions or forecasts based on simulated operating conditions are possible.The method considers the occurrence of delays at one location and delay increases based on trains' initial delays. It aims to weight additional delay increases higher for punctual trains compared to already heavily delayed trains. This reflects that a deviation from planned times of trains without initial delays has particularly negative impacts on operational quality as these disruptions are indicators for bottlenecks and can lead to delay propagations. Additional delays for already highly delayed trains however have lower significance for operational quality. Furthermore, such delays are often caused by the already existing deviation from the timetable and do not necessarily indicate a bottleneck. Therefore, delays are categorised.Both delay increases and delay category changes can be weighted based on the affected train type to calculate the bottleneck's severity. This gives information on the specific infrastructure's significance concerning the networks performance. Sorting investigated railway lines and stations by their severity gives an overview on the most
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