Available Masters' Theses

Third-party integrators, like Google Maps, offer user convenience but face a crisis of commercial viability. This creates a paradox where market efficiency does not equate to sustainability. This research investigates this imbalance using a game-theoretic model between an Integrator and TNCs. The study aims to derive optimal strategies for TNCs within this ecosystem, while proposing regulations to ensure social benefit is maintained.

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.

Despite the promise of a "Netflix for transport," the B2C Mobility-as-a-Service (MaaS) model faces severe headwinds, evidenced by MaaS Global’s 2024 bankruptcy. This thesis utilizes empirical analysis, such as surveys and expert interviews, to investigate this "Subscription Paradox." It aims to deconstruct the gap between industry hype and user reality, specifically analyzing willingness-to-pay and bundle preferences. By identifying root causes of low adoption, from price sensitivity to trust issues, this research uncovers practical implementation challenges and evaluates the viability of shifting from rigid subscriptions to flexible models.

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.

Many studies of on-demand mobility services assume constant travel times as their focus is on operational algorithms rather than traffic dynamics. FleetPy is a modular framework to simulate on-demand services and by default also uses deterministic travel times. The goal of this thesis is to adapt the network model within FleetPy to be dynamic and efficient; more specifically, a dynamic MFD-based model will be implemented in this thesis. Thereafter, the thesis will analyze fleet performance indicators for both deterministic and dynamic scenarios, as well as study the impacts of traffic congestion caused by ODM on mode choice.

This master’s thesis will investigate the implications of allowing Mobility-on-Demand (MoD) vehicles to use existing public transport (PT) bus stops for pick-up and drop-off (PUDO) operations. Building on the FleetPy simulation framework and existing multimodal SUMO-based simulations, the work will extend current methodologies to enable a more detailed representation of PUDO maneuvers at bus stops, capturing the stochastic interactions between PT services and MoD vehicles. The thesis will further aim to evaluate the operational impacts, benefits, and challenges of shared stop usage and develop a control strategy to coordinate bus stop sharing and mitigate conflicts between PT and MoD services.

In ride-pooling services, customers book a trip with the provider via an app and the trips are dynamically integrated into the route planning of fleet vehicles. No-shows of a booked trip can result in costs for the operator as well as for other customers. The goal of this work is to integrate no-shows into the existing simulation environment "FleetPy" and to simulate the effects on the overall system.

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.

In this thesis, it is explored how cognitive biases (such as negativity bias, the peak–end rule, and recency effects) influence cyclists’ perceptions of their routes. It proposes methods to detect these biases in real-world GPS cycling data and integrate them into a perceptually informed routing model. The ultimate goal is to develop a proof-of-concept “Perceptual Routing Engine” that designs routes optimized for human experience, not just efficiency.

While Munich offers extensive car, bike, and scooter sharing, these services operate independently. This thesis uses real-world data to model the current state and compares it against a simulated scenario involving a third-party integrator. The goal is to assess the benefits and challenges brought by such a platform integration and explore the potential unsustainable pressure on stakeholders.

Fairness in transportation is contested, particularly regarding the definition of equity versus efficiency. This thesis surveys established fairness metrics and evaluates their application across different modeling levels, specifically using agent-based simulation. The student will simulate a regulatory measure designed to maximize specific fairness criteria. A key objective is to analyze the trade-offs: how maximizing one fairness metric impacts others, as well as overall system effectiveness and environmental sustainability.

This thesis explores the implementation of a two-directional roundabout in SUMO with adaptive lane configurations for the two directions based on real-time traffic demand. The student will develop a dynamic lane allocation system, fine-tune its parameters, and analyze its potential for different roundabout configurations.

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.

The fundamental diagram, or the network fundamental diagram for urban networks, mirrors the macroscopic behavior of traffic flow. Since the automated vehicles may not be programmed to mimic the driving behavior of the human-driven vehicles, their emerging macroscopic behavior could be different. Therefore, this thesis explores, in a simulation-based study with automated vehicles being used in automated intersection management or speed advisory systems, what the emerging relation between flow speed and density at the network level could look like.