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Available Study Projects
Here are the available study project topics structured by the following thematic ares of the Chair of Traffic Engineering and Control:
| Topic Category | Description |
| Effects and Impacts of Mobility | mobility pricing, LCA, impact assessments, mobility coins |
| Experimental Studies | data collection with e.g. field tests, surveys, test intersection, simulator |
| Transportation Systems and Concept | Public & private transport, micro-mobility, shared and/or autonomous fleets, ropeways, UAM/AAM, car sharing, ride haling, pedestrians and bike traffic, ... |
| Mobility Data Modeling and Simulation | AI based, large scale data modeling; methodical approaches, traffic flow, Macro- and microscopic simulations (Sumo, Visum, Vissim, Aimsun, ...) |
| Traffic Control and Management | traffic light control, managed lanes, lane free, Urban traffic control |
It is possible to hand in your own topic proposal - Dr.-Ing. Antonios Tsakarestos is pleased to receive them.
The topics are provided with one or more of the following icons, these icons illustrate the main applied method:
- Simulation: 🖥️
- Experiment: 🧪
- Concept: 💭
- Programming: 💻
- Survey: 📝
- Data analysis: 📈
Experimental studies
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Developing the bicycle simulator further.
Mentoring: Lindner, Pechinger.The Chair of Traffic Engineering and Control uses bicycle simulators in various research projects. These can be extended in their validity and functionality. Examples are "full-body motion tracking", the mapping of gradients or human-machine interfaces on the bicycle. In this work, one component is to be integrated on the bicycle simulator.
🧪💻
Mobility Data Modeling and Simulation
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Testbed for traffic simulation.
Mentoring: Lindner.In this project, three goals are pursued: (1) to duplicate data and models from an existing (online) platform for (specified) offline studies, (2) to implement procedures to incorporate real-time data into testbed(s), and (3) to investigate data aggregation with respect to traffic control.
🖥️💻 -
Image-based clustering of travellers according to their mobility patterns using a large-scale tracking dataset.
Mentoring: Alvarez, Dahmen.In transport research, population is often segmented into groups with homogeneous mobility behavior / characteristics. The idea of visualizing the activity behavior of each individual and then clustering these images according to "traditional" unsupervised ML methods has been explored for longitudinal panel data (DOI: 10.1016/j.trip.2020.100264). In this thesis, such an approach will be adapted for a large-scale dataset of mobility tracking data collected in the Mobilität.Leben project.
💻📈