Current Projects
Here is a brief overview of the current projects at the Chair, click for more details:
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FE 77.0702/2024 - Pedestrian Networks for Sustainable Mobility and Attractive Public (Street) Spaces
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CRC 408 AgiMo – Data-driven agile planning for responsible mobility
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Automatic mapping of cycling infrastructure using deep learning (AI4CycleMaps)
- Demand for Advanced Air Mobility (AAM)
- SML - Smart Mobility Lab in Hoyerswerda | Subproject Reallabor
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Project Name |
FE 77.0702/2024 - Pedestrian Networks for Sustainable Mobility and Attractive Public (Street) Spaces |
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Sponsor |
Federal Highway and Transport Research Institute (BASt) |
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Cooperation Partner(s) |
University of Stuttgart (Chair for Transport Planning and Traffic Engineering), Ramboll Germany GmbH, STRATMO GbR, platomo GmbH |
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Duration |
07/2026–04/2029 |
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Goals |
The goal is to develop a standardized, operationally feasible, and scalable method for the systematic planning of pedestrian networks. To this end, individual network segments are to be assigned to network levels (basic network—BN, main network—MN, higher-level network—HN, and, if applicable, additional sub-levels). Building on this, the method is intended to support the further development of the networks by enabling the derivation of network-level-specific quality standards in the context of the respective design situation. Another component is the systematic identification of network gaps: This involves specifically identifying those network segments that are necessary for establishing a contiguous network within a given network level. |
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Website |
Website: FoPS |
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Contact Person(s) |
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Project Name |
Collaborative Research Center (CRC) 408 AgiMo – Data-driven agile planning for responsible mobility |
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Sponsor |
Deutsche Forschungsgemeinschaft (DFG) |
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Cooperation Partner(s) |
TUM Technical University of Munich (TUM), Technical University of Berlin (TUB), Technische Universität Braunschweig (TUBS), German Aerospace Center (DLR) |
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Duration |
10/2025–06/2029 |
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Motivation |
The transportation sector has been an unquestioned driving force of societal progress for decades. However, it is now facing unprecedented challenges, e.g., rapid decarbonization, broader demands for equity, and livable cities. Efficiency gains from technological innovation tend to be offset by unintended consequences such as increasing traffic volumes. Integrated planning approaches are key to address these challenges but current research methods in planning are often fragmented, and too focused on motorized transportation and efficiency gains. The opportunities from new data sources are not fully considered, and models are often too technical in their implementation to be suitable for evidence-based participatory planning methods. |
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Goals |
Phase I of the proposed CRC will address the identified research gaps with the following four main research goals:
For meeting these ambitions, this CRC will be organized into three Research Areas (RAs). RA A and RA B will focus on advancing individual mobility planning methods.
The long-term perspective for this CRC is to further develop the AgiMo Digital Twin in its components and their integration, its functionalities and its interaction with an active community into a virtual laboratory that enables research and supports practical planning tasks. |
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Website |
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Contact Person(s) |
Spokesperson: Prof. Dr.-Ing. Regine Gerike Managing Director: Dr.-Ing. Caroline Koszowski |
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Project Name |
CRC 408 AgiMo // Project A1: Innovative survey designs for better understanding human (travel) behavior |
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Sponsor |
Deutsche Forschungsgemeinschaft (DFG) |
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Cooperation Partner(s) |
TUM Technical University of Munich (TUM), Technical University of Berlin (TUB), Technische Universität Braunschweig (TUBS), German Aerospace Center (DLR) |
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Duration |
10/2025–06/2029 |
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Goals |
Project A1 aims to enhance smartphone-based survey methods as one component of future multi-method travel surveys to achieve representative samples and travel estimates, collect longitudinal data for entire households, and make the surveys continuous and cover travel and non-travel activities. We will conduct a modular longitudinal smartphone travel survey with systematically varied methods of recruitment, household coverage, survey administration and content as a basis for studying travel and non-travel activities, and the effects of different survey methods. Finally, we will develop use cases and designs for future travel surveys. |
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Website |
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Contact Person(s) |
Principle Investigator (PI): Prof. Dr.-Ing. Regine Gerike |
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Project Name |
CRC 408 AgiMo // Project A3: New data on street design and street user activities for innovative safety assessment methods |
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Sponsor |
Deutsche Forschungsgemeinschaft (DFG) |
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Cooperation Partner(s) |
TUM Technical University of Munich (TUM), Technical University of Berlin (TUB), Technische Universität Braunschweig (TUBS), German Aerospace Center (DLR) TUD: Junior Professorship in Geosensor Systems |
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Duration |
10/2025–06/2029 |
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Goals |
Project A3 will develop methods for mapping Vulnerable Road Users (VRUs) at the city scale based on aerial and Very High Resolution (VHR) satellite images. At the local street scale, we will prepare a multi-sensor system to map VRUs, and advance photogrammetric approaches to process multimodal 2D and 3D data to map street user environments. Based on this data and exposure data from other projects, we will develop crash prediction models at the city scale and methods for assessing conflicts between pedestrians and cyclists at the street scale by combining physical measurements of movements with image data and manual assessments. |
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Website |
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Contact Person(s) |
Principle Investigator (PI): Prof. Dr.-Ing. Regine Gerike Principle Investigator (PI): Jun.-Prof. Dr. Anette Eltner |
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Project Name |
CRC 408 AgiMo // Project A4: Data analytics for characterizing and modeling travel and traffic patterns |
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Sponsor |
Deutsche Forschungsgemeinschaft (DFG) |
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Cooperation Partner(s) |
TUM Technical University of Munich (TUM), Technical University of Berlin (TUB), Technische Universität Braunschweig (TUBS), German Aerospace Center (DLR) TUD: Chair of Big Data Analytics in Transportation |
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Duration |
10/2025–06/2029 |
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Goals |
The focus of A4 is on data science methods, which we apply and advance for different data types. First, hybrid techniques will be developed for generating an aggregate Origin-Destination (OD) travel demand based on high-frequency car travel time data. Second, cluster and classification algorithms will be developed to identify patterns in bicycle traffic using floating bicycle data. Third, we will develop methods to dealing with overlaps in the coverage of data and models. Fourth, we will investigate intra-personal travel routines, using sequence alignment methods with a particular focus on the effects of intra-household interactions. |
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Website |
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Contact Person(s) |
Principle Investigator (PI): Prof. Dr. habil. Rico Wittwer Principle Investigator (PI): Prof. Dr. Pascal Kerschke |
| Project Name | Automatic mapping of cycling infrastructure using deep learning (AI4CycleMaps) |
| Sponsor | Federal Ministry for Transport (BMV) |
| Cooperation Partner(s) | |
| Duration | 12/2025 - 11/2028 |
| Motivation |
Cycling infrastructure is currently mostly recorded through expensive, manual inspections, resulting in outdated or incomplete data. Heterogeneous databases in administrations also prevent uniform network-related evaluation and make cooperation difficult. This results in a lack of up-to-date, reliable data for the targeted elimination of deficits (e.g., insufficient cycle path widths) and for effective strategic network planning and road safety work. There is a need for action in developing an automated, resource-saving procedure for mapping cycling facilities to create a transparent, comparable, and regularly updated database for all levels of administration. |
| Goals |
The aim is to develop and implement an innovative, AI-based process for automatically mapping cycling facilities and their metric characteristics (e.g., width) in inner-city networks. The underlying idea is to generate a regularly updatable, highly accurate, scalable, and resource-efficient data source by applying neural networks (deep learning) to publicly available multimodal image data (aerial images, Street View). These should enable local authorities to carry out efficient control and targeted investments in high-quality cycling infrastructure and improve cooperation in network planning. |
| Website | Mobilitätsforum Bund | Wissenspool |
| Contact Person(s) |
| Project Name | Demand for Advanced Air Mobility (AAM) |
| Sponsor | German Research Foundation (DFG) |
| Cooperation Partner(s) |
This project is one PhD project in the Research Training Group (RTG) “AirMetro Research Training Group 2947” at TU Dresden, which investigates the technological and operational integration of highly automated air transport in urban areas. Cooperation partners are listed at AirMetro project webpage. |
| Duration | First cohort: 5/2024 – 4/2028 |
| Motivation |
Thanks to rapid technological development, travel via Vertical take-off and landing (VTOL) aircraft such as air taxis could increasingly be considered a technologically realistic option for future passenger mobility . Advanced Air Mobility (AAM) offers several possible advantages, including low-congestion travel routes and higher speed than for the alternative modes on road and rail. However, to fully prepare for the emergence of AAM, methods for estimating future demand for this new mode of transportation need to be developed. |
| Goals |
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| Website | AirMetro Research Training Group 2947 at TU Dresden |
| Contact Person(s) |
| Project Name | SML - Smart Mobility Lab in Hoyerswerda | Subproject Reallabor |
| Client | Federal Ministry of Transport and Digital Infrastructure (BMDV) |
| Cooperation Partner(s) |
In addition, the following chairs at Dresden University of Technology are involved in the overall SML project: Chair of Air Transport Technology and Logistics (IFL), Chair of Agricultural Systems and Technology (AST), Chair of Information Technology for Traffic Systems (ITVS), Chair of Networked Systems Modeling (NSM), Chair of Software Technology (ST), Chair of Traffic Process Automation (VPA) |
| Duration | 4/2023 – 12/2026 |
| Goals | The urban area of Hoyerswerda will be equipped with technology for traffic monitoring. This will enable field tests on road safety and traffic behaviour to be carried out in public road traffic. |
| Content |
A key area of work is the further improvement and analysis of methods for assessing the criticality of interactions in road traffic, known as surrogate safety measures (SSMs). These can provide information about the road safety of a traffic infrastructure so that in future adjustments can be made to the traffic system to improve road safety before traffic accidents occur. Furthermore, new methods for recording and analysing traffic behaviour will be used. |
| Website | Smart Mobility Lab |
| Contact Person(s) |