Final Presentation of the Teaching Project Data-driven Solutions for the Smart City Hamburg

12 February 2026, by Marten Borchers & Janis-Marie Paul

Photo: Marten Borchers/UHH

On Wednesday, the final event of the interdisciplinary and transdisciplinary teaching project “Data-driven Solutions for the Smart City Hamburg” took place. The format has now been successfully implemented for the eighth time – in close collaboration between Hamburger Hochbahn AG, the University of Hamburg, and the Hub of Computing and Data Science (HCDS).

The course enables students to work on real-world challenges in urban mobility and sustainability using data-driven approaches. It fosters practical problem-solving skills, innovation capacity, and interdisciplinary collaboration.

Together with several departments of Hamburger Hochbahn AG – including Mobility Strategy and Strategic Product Planning (Aurelia Mennerich), Sustainability Management (Nienke Berger, Laureen Brieve), Mobility Data Analytics & Modeling (Jastine Bergelt), and Bus Operations Management (Mirco Kötzsch) – students developed AI-supported prototype solutions for concrete use cases.

AI-Supported Analysis of Operational Impacts in Public Transport

The project team consisting of Michelle Hallmann, Nina Strarovitova, Julia Wilhelm, Bahar Barikzei, and Patrick Leonhardt, in collaboration with Jastine Bergelt, Michael Thüne, and Mirco Kötzsch, developed a digital dashboard to assess operational impacts of route diversions and stop relocations.

The prototype automatically parses and analyzes text-based reports using AI methods and quantifies operational effects through queries to stop registries and OpenStreetMap data. The system structures and standardizes reporting processes, making impact analyses more transparent and efficient.

Data-Driven Sustainability Risk Profiling in Supply Chains

The team of Julien Graf, Erik Pauli, Sardorbek Sabirov, Marvin Schmidt, and Lukas Owen, working with Nienke Berger and Laureen Brieve, developed an AI-supported solution for generating sustainability risk profiles for product groups and goods.

The prototype integrates a search engine and a large language model to decompose products into their components and automatically identify human rights and environmental risks along the supply chain. The intelligent search process supports systematic risk assessment and transparently prepares qualitative information and sources.

Improving Accessibility Through AI-Based Knowledge Access

The group consisting of Yasmine Khamassi, Leon Sothmann, and Josef Hermann, in cooperation with Aurelia Mennerich, developed an AI-based prototype to improve access to accessibility requirements for implementation managers.

Based on a Retrieval-Augmented Generation (RAG) architecture, the system integrates various documents on accessibility standards and makes them accessible through an intuitive chatbot interface. The goal is to support responsible staff in quickly retrieving structured and relevant information.

Strengthening Smart City Innovation Through Collaboration

The project once again demonstrates how data-driven methods and AI applications can be implemented in real organizational contexts. Students gain valuable insight into complex decision-making processes, while industry partners benefit from innovative, practice-oriented prototypes.

We would like to thank all participating students, our colleagues at Hamburger Hochbahn AG, and the teaching team – Jan Krause, Heiko Bornholdt, Benjamin Klinkigt, Eva Bittner, Martin Semmann, and Marten Borchers – for the successful completion of this year’s edition. We look forward to continuing this collaboration in future iterations of the project.