Design tasks and their complexity for the European Train Control System with Hybrid Train Detection

Railway networks have become increasingly important in recent times, especially in moving freight and public transportation from road traffic and planes to more environmentally friendly trains. Since expanding the global railway network is time- and resource-consuming, maximizing the rail capacity of the existing infrastructure is desirable. However, simply running more trains is infeasible as certain constraints enforced by the train control system must be satisfied. The capacity of a network depends (amongst others) on the distance between trains allowed by this safety system. While most signaling systems rely on fixed blocks defined by costly hardware, new specifications provided by Level 2 with Hybrid Train Detection of the European Train Control System (ETCS L2 HTD), formerly known as ETCS Hybrid Level 3, allow the usage of virtual subsections. This additional degree of freedom allows for shorter train following times and, thus, more trains on existing railway tracks. On the other hand, new design tasks arise on which automated methods might be helpful for designers of modern railway networks. However, although first approaches exist that solve design problems arising within ETCS L2 HTD, neither formal descriptions nor results on the computational complexity of the corresponding design tasks exist. In this paper, we fill this gap by providing a formal description of design tasks for ETCS L2 HTD and proof that these tasks are NP-complete or NP-hard, respectively. By that, we are providing a solid basis for the future development of methods to solve those tasks, which will be integrated into the Munich Train Control Toolkit available open-source on GitHub at https://github.com/cda-tum/mtct . • Design automation methods are of great interest to plan railway infrastructure. • Arising design tasks are (for the first time) explicitly specified. • Arising design tasks are NP-hard even if some important decisions are fixed. • Future results will be included in the open-source Munich Train Control Toolkit.