The STRALI research project (Strategic Charging Infrastructure Planning for the Electrification of Urban Transport) aims to enable cities and municipalities to plan charging infrastructure in line with their needs and local transport policy objectives, based on multi-criteria data collection and analysis. In the future, suitable web-based applications will be used to make charging infrastructure planning available for the targeted, resource-efficient control of traffic flows. The web-based charging infrastructure planning tool is used for the targeted control of traffic according to specified criteria that are geared toward the interests of mobility users. The combination of empirical data with secondary and technology-based data from tracking apps and data loggers will provide a better data basis for a subsequent road-specific traffic model. The data from the simulation and traffic models will ultimately flow into a newly developed planning tool, which will be used to validate the extent to which mobility behavior can be influenced by strategic, multi-criteria charging infrastructure planning.

With an interdisciplinary team of engineers, psychologists, business economists, innovation researchers, and transportation researchers, in collaboration with two Essen-based SMEs, the three-year project was launched on August 1, 2023, with funding from the Federal Ministry of Digital and Transport.

In the STRALI project, Marten Sprehn and Prof. Dr. Heike Proff are responsible for the preliminary analysis and research, investigating price acceptance and electrification business models. As the project progresses, important tasks will include collecting data on the use of and awareness of existing e-mobility business models, as well as price acceptance for these business models. Furthermore, willingness to pay will be evaluated using a conjoint analysis. In addition, the effects of data-based charging infrastructure planning and the tools developed in the project on selected current and future provider networks and business models will be assessed, and suggestions for adaptation and change will be made.