Our research approach combines data-driven, model-based methods with an application-oriented, cross-actor perspective on multimodal transport and logistics systems. We want to increase the performance and resilience of multimodal networks – while at the same time contributing to climate and sustainability goals.

To achieve this, we primarily use the following methods:

• modelling, simulation and optimisation to provide a sound basis for addressing strategic, tactical and operational issues.
• Analysis of sensor data, logistics process data, and platform data to provide data-driven decision support.
• qualitative-empirical methods, such as interview studies, surveys and process modelling, to identify organisational, behavioural and regulatory factors.

By combining these methods, we develop practical tools that realistically model uncertainties, interdependencies and complex systems. They can be directly applied in practice for planning, control and analysis.

Our research content focuses on multimodal transport chains – in particular rail and road transport, but also air and water. We examine how these modes of transport can be efficiently linked within intermodal terminals, and we want to investigate how digital technologies and data platforms can contribute to more efficient, cross-actor processes. In doing so, we focus thematically on the planning and analysis of multimodal transport systems from two key perspectives:

From a business perspective, we take the following into account:

• increases in productivity and efficiency
• optimisation of resources and capacity
• reduction and prevention of delays, breakdowns and empty runs
• data-driven decision support for planning and operations

From a broader societal perspective, we focus on:

• reduction of external effects (CO₂, noise, congestion, energy consumption, etc.)
• shifting transport to environmentally friendly modes
• resilient, sustainable and efficient freight transport systems