Available Masters' Theses

This master's thesis aims to develop a methodology for deriving historical and geographical pollution data of a city using data from monitoring stations. The following aspects shall be considered: 1. Systematic removal of weather and event influences to eliminate distorted data. 2. Focus on pollution data associated with traffic influences. 3. Mapping of monitoring stations onto the urban traffic network. Methodologically, the reliability of the results will be assessed, and various approaches will be discussed to verify and potentially adjust them. The specific application example will be the temporal evolution of pollution in Paris from 2015 to 2023. Proficiency in Python is advantageous and recommended for implementing the methodology.

The master's thesis aims to investigate how political measures can be best quantified. Various political approaches exist to curb traffic, including parking pricing, reducing parking spaces, and the concept of the "15-minute city." To determine the effectiveness of these measures, they must first be quantified. This work aims to develop a methodology to effectively quantify political measures. The reliability of the results will be examined, and various methods will be discussed to validate and potentially adjust them. This includes verifying data integrity and applying validation techniques to ensure the reliability of the results. Paris serves as a case study, utilizing official data from Paris Open Data in conjunction with information from Google StreetView and OpenStreetMap. Proficiency in Python is advantageous.

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.

Cycling is widely recognized as a sustainable and healthy mode of transportation, yet the shift toward cycling remains limited in several places. One significant barrier to increased bike use is traffic stress, such as anxiety about accidents. A bicycle simulator experiment offers a controlled method for data collection, allowing for precise manipulation of scenarios and easy behavioral measurement. However, effective methods for measuring stress in virtual environments have not yet been established. This study aims to investigate and evaluate stress measurement methods within a bike simulator. The approach includes designing simulator experiments and analyzing biometric data, such as heart rate, eye-tracking, and cycling behavior.

The aim of the thesis is to study the routing of a fleet of vehicles (with big batteries) that can charge other on-demand ride-hailing or ride-pooling vehicles (with smaller batteries). The thesis will first research the available methods for the routing of on-demand charging vehicles, develop a new routing scheme and then evaluate their efficiency in an agent-based simulation framework, called FleetPy.

The aim of the thesis is to study the routing of a fleet of vehicles (with big batteries) that can charge other privately owned electric vehicles (with smaller batteries). The thesis will first research the available methods for the routing of on-demand charging vehicles, develop a new routing scheme and then evaluate their efficiency in an agent-based simulation framework, called FleetPy.

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.

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.

The master's thesis aims to investigate and compare quantitative methods for supplementing detector data. Detector data is often incomplete, which compromises its usability. Within the machine learning community, there are now effective procedures for approximating missing data, known as "imputing missing values". The primary objective of the quantitative analysis is to test and compare various machine learning methods using a geodataset. The geodataset pertains to Paris and documents traffic behavior since 2015. Since 2015, Paris has undergone significant network changes, which are reflected in traffic patterns. These impacts will be examined through the analysis results. Prerequisites for the analysis include basic knowledge of Python. Ideally, the student should have advanced Python skills and experience with machine learning libraries such as scikit-learn.

In many large-scale sporting events, such as cross-country skiing marathons, bottlenecks and overcrowding result in the formation of congestion analogous to that observed in road traffic. Using publicly available split times as stationary detector data and GPS tracks as floating car data, the goal of this thesis is to perform a detailed analysis of the congestion formation in several high-profile cross-country ski marathon events.

The accurate estimation of queue lengths at traffic lights is a critical point in order to efficiently adapt the control system. An existing AI method based on drone data is to be tested and further developed as part of this project. The existing code and approaches for improvement will be provided by the mentor.

Mobility-on-demand services (MoD) perform frequent Pick-Up and Drop-Off (PUDO) processes, which can have a significant impact on the capacity and performance of urban road networks. This thesis will use microsimulation tools (e.g., SUMO) to quantify these impacts considering different network configurations, PUDO frequencies, road typologies (i.e., number of lanes, width of the lanes, etc.), and type of stops (in the curbside or as "double-parking").

The operational problem of ride-pooling is very challenging because of its dynamic and stochastic nature. Based on estimations of future demand, operators can reposition their (idle) vehicles to be prepared better for the future. Since the rewards of making these decisions evolve over time, creating an objective function for repositioning is non-trivial. This is where machine-learning methods (e.g. value function estimation or reinforcement learning) can come into play. With FleetPy, a simulator will be available, which should be extended in this thesis

The goal of the thesis is to analyze how different service designs, network structures and demand patterns, which relate to transfers between line-based public transport and a ridepooling service, affect the performance indicators of a ridepooling fleet. The chair’s fleet simulator FleetPy can be used as basis to model a ridepooling service. A small programming extension is necessary to model it as access and egress service for public transport.

Artificial intelligence methods are currently driving the development of automated driving. This thesis focuses on how the interaction between automated vehicles (and other road users (optionally)) can be optimized with the help of artificial intelligence. Existing conventions on right of way or fixed lane allocation may be challenged.

Similar to lane-based traffic, where vehicles choose a proper lane upon an intersection, in the lane-free case, the vehicle should also adjust their lateral location properly before reaching an intersection depending on their turn movement. This work develops optimal approaches for choosing lateral locations for vehicles.

Lane-free traffic allows connected and automated vehicles to move across the entire road surface in a flexible fashion. This allows for a reorganization of today's traffic control at intersections. To conduct an evaluation of the developed ideas, a self-developed simplified simulation environment (e.g. Cellular Automata) can be used.

With the introduction of Connected Autonomous Vehicles (CAVs), there is a growing interest in Lane-Free Traffic (LFT), where CAVs drive without managed lanes. The movement of CAVs in LFT is controlled by control algorithms that coordinate their movements. However, the performance of this controller can be severely affected by the presence of human drivers. Therefore, this thesis develops a control strategy for CAVs capable of dealing with mixed traffic where humans and CAVs share the same road without degrading the performance of CAVs in LFT. The simulation will be conducted in a customized SUMO for LFT and requires some basic programming skills in C++.

The openPASS platform has recently been developed by the BMW Group to conduct scenario-based traffic simulation. Compared to other standard traffic simulators, advanced driver assistance and automated driving systems are integrated into openPASS, allowing for the advanced simulation of vehicle-based scenarios. The goal of this thesis is to understand how openPASS works, what its capabilities are, and how to develop Traffic simulation of highway, rural, and urban scenarios.

Lane-free movement, an innovative approach for Connected and Automated Vehicles (CAVs), allows vehicles to navigate across the entire road width without fixed lanes, optimizing space and reducing congestion. Combined with internal boundary control, which reallocates road width based on traffic flow, it can improve road capacity. This thesis aims to address the infrastructure challenges of implementing lane-free systems, proposing modifications to support flexible movement and real-time traffic management for safer, more efficient roads.