Available master's theses

Private car ownership involves high fixed costs that depend heavily on vehicle age. Car subscriptions and leasing offer flexible access but mostly feature new vehicles. This creates a dilemma between subscribing to a new car and buying an older used vehicle. This thesis aims to develop a stochastic Total Cost of Ownership (TCO) model comparing new vehicle subscriptions and leasing against private ownership across various lifecycles. Using simulations for variables like fuel prices and depreciation, the study will identify probabilistic break-even points to determine when access-based models become financially viable.

Private cars are parked for 95% of their lifetime. Car subscriptions can reduce this idle time through flexible returns and accelerate the market integration of energy-efficient vehicles. However, frequent vehicle replacements and fleet logistics generate environmental overhead. This thesis aims to evaluate the environmental sustainability of subscriptions versus private ownership in Germany. By conducting a scenario-based Life Cycle Assessment (LCA), the study quantifies how reduced idle time, higher utilisation and accelerated fleet renewal impact the environmental footprint to identify ecological break-even conditions.

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.

Perceived unsafety is considered one of the major barriers to a modal shift toward cycling, despite strong efforts by many city planners to promote bicycle use. In particular, complex interactions with motor vehicles at intersections can leave a strongly negative impression on cyclists and reduce their sense of safety. The aim of this study is to propose a model for assessing perceived safety while explicitly considering the effects of interactions with cars. The existing link-level perceived safety model will be extended to the area level, with a focus on intersection-centered environments and the incorporation of traffic volume data. The model results will then be compared with findings from real-world cycling perception experiments.

Perceived stress is an important factor influencing cyclists' safety perception in urban traffic. This study aims to investigate how cyclists' visual attention patterns vary across different levels of subjective stress and traffic environments. By combining eye-tracking data, first-person video recordings, GPS trajectories, and subjective stress reports, the study will examine relationships between gaze behavior, perceived stress, road characteristics, and interactions with other road users. The findings will help identify potential visual attention indicators of cycling stress and provide a basis for future stress prediction models.

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 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.

In the event of an energy blackout, on-demand vehicles can act as energy suppliers to vital facilities. This thesis aims to develop a method of allocating ODM vehicles to energy supply stations and vital facilities. The technical and economic feasibility of this concept will be evaluated.

When aircraft are turned around at an airport, the ground processes need staff and equipment, but these resources are limited and several flights compete for them at once. Different rules can decide who is served first — for example, giving priority to the flight closest to its departure, or to the one that has waited longest since arrival. This thesis studies, in a synthetic or semi-synthetic scenario, how such allocation rules perform against chosen key performance indicators, such as total delay and resource utilisation. The student will use and extend an existing turnaround simulation tool, run it under different scenario settings, and analyse the outcomes.

A large share of air traffic delays arises during ground handling between landing and departure (turnaround). Possible causes include late inbound flights, missing staff or equipment, or apron congestion, but it is often unclear which of them explain the observed delays. This thesis first identifies known bottlenecks through a literature review and analyzes available operational data to reveal recurring disruption patterns. Based on this, a methodological framework is developed that translates delay causes into simulation scenarios and defines suitable metrics for their evaluation. The student will use and extend an existing turnaround simulation and demonstrate the framework on a selected handling process.

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.

This thesis aims to develop an online estimator for the time-varying critical density in lane-free bidirectional traffic. Because a movable internal boundary dynamically reallocates road width-and therefore capacity-between the two travel directions, the critical density of each direction changes over time. The proposed estimator, based on either classical estimation methods or intelligent data-driven techniques, will use available traffic measurements and the internal-boundary position to estimate the direction-specific critical densities in real time. The student will require knowledge in C++ coding.