Current Projects

Junior Professorship in Geosensor Systems

Cycling infrastructure is currently mostly recorded through expensive, manual inspections, resulting in outdated or incomplete data.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.

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.

Dr.-Ing. Sebastian Hantschel
+49 351 463-42394

Dipl.-Ing. Armin Kollascheck
+49 351 463-36581

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.

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.

• Quantify potential AAM trips: Investigate and identify trips that could potentially be replaced by AAM with a smartphone-based survey. This includes the analysis of current daily (mainly short distance) and less frequent (mainly long distance) trips.
• Develop predictive models for AAM demand: Develop statistical models that incorporate socio-economic factors, environmental considerations, regulatory impact and safety perceptions to forecast AAM demand. Utilize both revealed preference (RP) data investigated from a smartphone-based data collection and stated preference (SP) data from an online survey that capture potential users’ choices regarding AAM services.
• Estimate mode share of AAM within multimodal transportation networks: Analyse the projected proportion of trips conducted via AAM in comparison to other modes of transportation. This goal seeks to understand the role of AAM within the broader urban mobility landscape.
• Simulate AAM scenarios using agent-based modelling: Utilize the MATSim simulation tool and the newly collected data from RP and SP surveys to develop an agent-based model that simulates different AAM scenarios. This will provide insights into how changes in urban infrastructure, policy regulations, and change in AAM infrastructure could affect AAM demand and alter travel behaviour.

Prof. Regine Gerike
+49 351 463-36501

Vahid Noroozi, M.Sc.
+49 351 463-36652

• Dresden University of Technology, Chair of Automobile Engineering (LKT)
• City of Hoyerswerda

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)

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.

Prof. Regine Gerike
+49 351 463-36501

Dipl.-Ing. Armin Kollascheck
+49 (0)351 463-36501