The demand for sustainable railway transportation is increasing over time.At the same time, the capacity of railway networks is limited.Hence, efficient algorithms for generating optimal timetables are of great interest.Previous research focuses on trains being separated by classical fixed block signaling systems.With modern control systems based on moving block, e.g., within the European Train Control System (ETCS), the principles of safely separating trains change significantly.Only limited research on optimal routing on such modern railway networks exists.With this work, we propose a simulation approach tailored to be used with heuristic optimization algorithms to tackle this problem.Moreover, we show how such a framework can allow for more general inputs to jointly optimize what is usually planned sequentially as of today.The simulation framework is included within the open-source Munich Train Control Toolkit (MTCT) available on GitHub at https://github.com/cda-tum/mtct.

Increasing the capacity of our railway infrastructure will become more and more essential in coping with the need for sustainable transportation. This can be achieved by intelligently implementing train control systems on specific railway networks. Methods that automate and optimize parts of this planning process are of great interest. For control systems based on hybrid train detection, such optimization tasks simultaneously involve routing and block layout generation. These tasks are already complex on their own; hence, a joint consideration often becomes infeasible. This work-in-progress paper proposes an idea to tackle the corresponding complexity. To this end, we present a pipeline that allows to sequentially handle corresponding optimization tasks in a less complex fashion while generating results that remain (close to) optimal. Results from an initial case study showcase that this approach is, indeed, promising. A prototypical implementation is included in the open-source Munich Train Control Toolkit available at https://github.com/cda-tum/mtct.

To increase the capacity of existing railway infras-tructure, the European Train Control System (ETCS) allows the introduction of virtual subsections. As of today, the planning of such systems is mainly done by hand. Previous design automation methods suffer from long runtimes in certain instances. However, late breaking results show that these methods can highly benefit from an iterative approach. An initial implementation of the resulting method is available in open-source as part of the Munich Train Control Toolkit at https://github.com/cda-tum/rntct.

Railroad transportation plays a vital role in the future of sustainable mobility.Besides building new infrastructure, capacity can be improved by modern train control systems, e.g., based on moving blocks.At the same time, there is only limited work on how to optimally route trains using the potential gained by these systems.Recently, an initial approach for train routing with moving block control has been proposed to address this demand.However, detailed evaluations on so-called lazy constraints are missing, and no publicly available implementation exists.In this work, we close this gap by providing an extended approach as well as a flexible open-source implementation that can use different solving strategies.Using that, we experimentally evaluate what choices should be made when implementing a lazy constraint approach.The corresponding implementation and benchmarks are publicly available as part of the Munich Train Control Toolkit (MTCT) at https://github.com/cda-tum/mtct.