Available Masters' Theses

This research models the competition between asset-heavy platforms owning proprietary fleets and asset-light competitors that aggregate user-owned autonomous vehicles using game theory. It focuses on "prosumers"—riders who also supply capacity—creating a theoretical "loyalty moat" that reduces price sensitivity. By analyzing this interaction, the project determines whether decentralized ownership enables asset-light firms to withstand aggressive pricing wars from vertically integrated giants, providing insights into the future robotaxi economy.

This thesis focuses on recognizing the intentions of extra vulnerable road users (eVRU), specifically wheelchair users. The analysis relies on camera and Lidar data, utilizing both conventional algorithms and deep learning approaches.

This thesis focuses on recognizing the intentions of vulnerable road users (VRU), specifically pedestrians and cyclists. The analysis relies on camera and Lidar data, utilizing both conventional algorithms and deep learning approaches.

In the bicycle simulator, the choice of the ridden path and the speed can be decoupled. In this study, we investigate the ridden path and focal points in driving simulator studies in order to use them for microscopic modeling of cyclists.

This thesis focuses on recognizing the intentions of cyclists at bus stops. The analysis relies on camera data, which is already available from a previous bike simulator study. The goal is to find out the importance or relation between body gestures or movements to the final maneuver decision, utilizing both conventional algorithms and deep learning approaches.

This thesis focuses on analyzing the cyclists behavior at bus stops. The analysis relies on both trajectory and the camera data of the cyclitsts, which is already available from a previous bike simulator study. The goal is to find out the correlation between trajectory and body gestures and body movements of cyclists.

The main goal of this thesis is to design and conduct a test filed study involving the interaction between VRU and shuttle bus. Data is collected by this study, followed by the development and evaluation of a system that uses both camera data from the vehicle perspective and LiDAR data from an infrastructure perspective to detect, track, and predict the intentions of VRU

The thesis aims to further develop an e-scooter simulation model that models the riding physics of the scooter as well as the rider and their weight transfer.

In automated ride-pooling services, trip requests are dynamically processed by a central optimizer to assign new routes to fleet vehicles and serve customer requests. Once a vehicle has completed the route, however, the question arises as to where it should wait for new assignments. Various strategies are conceivable: The search for the next parking lot, or a return to the depot. The aim of the thesis is to work out different strategies and to implement and evaluate them in a simulation. Within the work, the strategies are to be implemented in a framework developed at the chair, consisting of FleetPy and SUMO, and evaluated for operational and traffic effects.

The coordinated service of customer requests by mobility-on-demand (MoD) providers in combination with line-based public transport (PT) systems represents an interesting research question in the further development of MoD services. Urban ropeways constitute a special form of public transport due to their characteristic operational properties. The objective of this thesis is to develop and simulate a coordinated service strategy for handling customer requests using mobility-on-demand fleets and urban ropeway systems.

Research question: how to choose a right map matching algorithm balancing accuracy and computational time? By comparing the performances of different map-matching algorithms, the student is expected to find a balance in the accuracy of map-matching of GPS trajectory data to network graph for a dynamic and static use case within a reasonable computational time.

This thesis explores integrating autonomous vehicle navigation through urban construction sites into existing path planning algorithms, focusing on adapting for safe passage. The evaluation of the adapted planning behavior is conducted and assessed within a simulator, forming a vital part of the research.

This thesis focuses on integrating dynamic stops into the route planning of automated shuttle buses, aiming to efficiently adapt existing planning systems for urban transport networks. The evaluation of the planning behavior is conducted and assessed in a simulator, constituting a central part of the study.

This thesis explores Facial Emotion Recognition through Convolutional Neural Networks, aiming to advance human-computer interaction by accurately identifying human emotions from facial expressions.

This project focuses on integrating e-scooter simulation within Unity and VR environments, employing an actual electric scooter for immersive and realistic user experiences. It aims to bridge the gap between virtual simulations and real-world scooter maneuvering, enhancing training and entertainment applications.

In this thesis motion planners, mainly used in the domain of automated driving, should be applied to cyclists and validated using real-world data

This thesis explores how users respond to changes in traffic system dynamics using Elastic Dynamic Traffic Assignment. It investigates the potential for improving system resilience through demand-oriented policies. By analysing user sensitivity and demand shifts under various scenarios.

This thesis investigates urban traffic flow dynamics during large-scale evacuations, aiming to design effective evacuation plans that minimise congestion, reduce evacuation time, and improve overall system performance under emergency conditions.

This thesis aims to explore methods for dynamically assessing link criticality based on network demand and topology. It further investigates the optimal sequence of link repairs to minimise total recovery time and enhance system throughput following a disruptive event.

Topic is only available in German. Please look at the German description.

This thesis investigates how automated intersections can balance vehicle and pedestrian delays. A queue-aware control strategy will be developed to dynamically prioritize crossings based on estimated pedestrian demand obtained from CAV perception and evaluated through traffic simulation.