By setting up a bicycle mobility data space (DRM), research and companies are given the opportunity to support cities in the transport transition. With the International Data Space (IDS) approach, both data providers and data recipients have the opportunity to make completely sovereign decisions about the use of the data at any time.

Piloting of an information network that collates and analyses data on visitor movements from various sources and provides (near) real-time information on the current and future capacity utilisation of tourist hotspots in Bavaria.

The project aims to utilise innovative technologies such as AI to identify and predict potential forest fire risks, enabling early prevention and intervention measures and coordinating local response teams based on real-time data.

The aim of the project is to apply protocol anomaly detection methods to the IoT base stations that connect the devices to the Internet via low-energy wireless networks such as LoRaWAN (Long Range Wide Area Network) or NB-IoT (NarrowBand IoT).

The tourism sector represents enormous potential for strengthening the structure of lignite mining regions in Lusatia that are undergoing change. Unfortunately, the region is facing major challenges in the area of mobility, for which this project aims to find solutions through intelligent, multimodal mobility concepts.

The aim of the research project is to develop and test appropriate AI to enable proactive positioning and charge status optimisation of e-vehicles in sharing mode for tourists. The focus is on the necessary data integration based on open data, as well as on testing the business process defined in the use case.

The Moby Dex project is developing a mobility index that compares supply and demand. Demand is recorded using mobile phone data and FCD, among other things. Moby Dex thus enables the needs-based expansion of sustainable mobility for entire regions. The system is being trialled in the target regions of the Ruhr and Baden-Württemberg.

The aim of the project is to categorise P+R facilities and thus transfer the results to a large area. With the help of machine learning, a forecasting model will be created for each type of facility that can predict the utilisation of P+R facilities in order to reduce the time users spend searching for a parking space and to facilitate and promote the switch between private and public transport.

The SDIKA project is concerned with the use and testing of secure digital identities. The technology of secure digital identities primarily contributes to digital sovereignty and data security in communication between people and institutions.

An exhibit on the topic of traffic was created for the exhibition at wortreich in Bad Hersfeld. A map of the city with stops for various modes of transport and car and bike sharing locations is displayed on a wooden wall. The visitor's aim is to get from one end of the map to the other. In order to cover this distance, the visitor must follow a continuous path with the help of various chains.

With the real-time data that is now available - i.e. automatically recorded data on car park occupancy and traffic frequency - and with the help of artificial intelligence, forecasts on the utilisation of hotspots and car parks as well as traffic volumes are to be created in future. Guests will then know in advance how busy the local car parks will be over the next few hours. At the same time, attractive alternatives will be suggested.