Conference Agenda

Several rail traffic management approaches have been suggested in literature, mostly proposing either centralised or decentralised optimisation models. However, the impact of these models on both traffic flow and passenger experience needs further investigation before receiving acceptance from the railway industry. To this end, a modular simulation-based framework is proposed in this research which allows assessing impacts of rail traffic rescheduling approaches on both train service performances and passenger satisfaction. The developed framework interfaces modules for the simulation of rail traffic and passenger flows, with a rail traffic rescheduling algorithm, to assess optimised traffic plans provided by this latter on train punctuality and passenger travel times for different delayed traffic scenarios. A scalable messaging interface, Zero-MQ, is adopted for an efficient and tool-agnostic data exchange among the framework modules.

In the presented setup the state-of-the-art RECIFE-MILP rail traffic rescheduling algorithm is assessed by using the EGTRAIN rail traffic micro-simulation platform and a specifically-developed microscopic passenger flow model to describe passenger-dependent train dwell times at stations. In this setup, the impact of an open-loop configuration of the RECIFE-MILP rescheduling is compared to a rolling-horizon setup where traffic plans are regularly adjusted based on current traffic conditions. Those configurations are benchmarked against the original train schedule regarding train arrival delay and total passenger travel times. Results show the ability of the framework to evaluate different setups and algorithms for optimised rail traffic management and will be further used to assess novel paradigms including self-organising train operations.

Rail freight operators have long benefited from planning support based on mathematical optimization. Due to the highly complex nature of resource allocation problems in this industry, they have typically been tackled on an isolated basis. Achieving new levels of efficiency requires a broader perspective that considers more than one group of resources at a time. In this work, we study an integrated train timetabling, locomotive and wagon set scheduling problem for a rail freight carrier, in a long-term planning perspective. The objective is to find the optimal allocation of locomotives and wagons, measured in terms of costs or alternatively the number of locomotives or wagons used. In the problem we consider, some of the trains have a fixed timetable, while others have to be scheduled by our algorithm. On the one hand, this opens up additional optimization potential, but on the other, it adds computational complexity. In addition, mileage-based locomotive maintenance constraints must be considered. We model this problem on the basis of two interconnected multi-commodity flow problems with additional constraints. To validate the effectiveness of our approach, we conduct experiments using real-world data provided by DB Cargo Polska S.A.

The continuous effort to increase railway capacity is supported by the introduction of modern technologies such as the European Train Control System (ETCS). By the end of 2028, more than 4,000 kilometers of German railway lines are expected to be equipped with ETCS L2. To ensure the capacity gains brought by these systems, optimizing block sizes (distance between ETCS markers) is of central importance. Within this context, the challenge lies in ensuring demand-oriented block sizes that can meet current and future capacity needs. Another challenge arises from the currently established planning rules applicable to ETCS-L2oS (without signals) regarding the placement of ETCS elements (ETCS signals and balizes) on the infrastructure. Planning restrictions can currently limit the placement of ETCS elements; however, these restrictions are not absolute and can change over time.

There are a limited number of approaches for optimizing block sizes in ETCS L2oS. These approaches aim to adjust block sizes to specific train sequence scenarios and do not consider restrictions on the placement of ETCS signals. Thus, prevailing practical requirements are not fully met. Furthermore, existing approaches do not clearly explain how to respond to the increasing trend in future capacity needs. This contribution seeks to address the open questions by tackling two challenges. The first challenge involves specific steps to optimize block sizes on ETCS L2oS lines while considering flexible planning restrictions. The second aspect offers a strategic solution for a resilient planning, which can be flexibly extended to allow greater capacity than is currently required.