Golze - Theses

Master Theses (finished)

• Analysis of 3D City Data Regarding Subtainability

  Climate change and rapid urbanization are intensifying heat stress in cities, raising concerns for public health and urban livability. This thesis examines how 3D geospatial data and shadow analysis can be applied to assess urban microclimates and enhance pedestrian comfort, focusing on the Linden-Nord district of Hannover, Germany. Utilizing mobile mapping point clouds, 3D building models, and a digital terrain model, a digital surface model was generated to simulate solar radiation for a representative summer day (25th July 2025). Shadow analysis from solar radiation was conducted in ArcGIS Pro to quantify shading patterns and their impacts, while also visualizing areas with higher and lower solar radiation potential. The results revealed clear temporal and spatial variations: radiation increased from morning to a midday peak before declining in the afternoon. The analysis quantified the shading contributions of trees, buildings, and their combined effects, showing that buildings accounted for the largest share, whereas trees offered a smaller but consistent contribution. Although thresholding in shadow delineation introduced some uncertainty, the overall balance between shadow cast by buildings and trees remained robust. To further support outdoor comfort, a radiation-based shortest path analysis was implemented through network analysis in ArcGIS Pro. The routing results demonstrated that shaded paths are feasible and adaptable to the time of day and the distribution of urban features. Although longer than the shortest path, these shaded routes provided substantial reductions in solar exposure, in some cases exceeding 50%, depending on the time of day and the specific origin-destination pair. The study underscores the complementary role of built structures and vegetation in shaping urban microclimates and demonstrates the potential of shadow-based routing to enhance pedestrian comfort. These findings offer practical insights for sustainable urban design and heat-resilient mobility planning.

  Led by: Sester, Thiemann, Golze

  Team: Most Nazmun Nahar

  Year: 2025
• Identification and analysis of movement patterns in trajectories

  In this work, movement patterns in trajectory datasets are identified with respect to the respective visited locations of a trajectory. For this purpose, further semantic information is assigned to the whereabouts points depending on the position, time of day, and duration of stay; the assignment of semantic information with respect to position is done using OpenStreetMap data. Another focus was on the identification of related trajectory segments, since the given dataset was anonymized as a consequence of data protection; for this purpose, coordinate prediction was performed for all trajectory endpoints in order to identify a suitable continuing starting point of another trajectory using a proximity search and temporal proximity. Recurrent motion pattern detection performed based on the whereabouts points does not produce meaningful patterns detected in multiple trajectories throughout the dataset for the datasets used; however, meaningful recurrent patterns are found for individual trajectories. An increasing level of detail in assigning categories with respect to whereabouts results in fewer recurring patterns, which, on the other hand, allow for greater meaningfulness given the interpretation of an observed person’s movement behavior.

  Led by: Golze, Feuerhake, Wage, Sester

  Team: Friderike Fischer

  Year: 2022
• Development of a modular sensor platform for mobile detection of vehicle encounters

  Riding a bike in a shared traffic area with motor vehicles causes discomfort for many bicyclists. Avoiding busy roads is only possible with good local knowledge, as no data is available on the frequency of encounters with motor vehicles on most roads. Acquiring a dataset that collects smartphone sensor data on vehicle encounters could become the basis for a smartphone-based vehicle detector. Magnetometer and barometer readings are used as indicators of passing vehicles. In this thesis, a sensor platform is first constructed to collect smartphone and other sensor data while driving. The system is designed to be used with other sensor configurations in the future. A methodology is then presented to create a dataset of vehicle encounters based on data from a camera and a distance sensor on the sensor platform. This data set contains all important sensor data of a commercially available smartphone including the timestamp of vehicle encounters. Finally, a three-class classifier is trained and evaluated based on the data set. It is investigated which approach can provide a generalizable classifier. Approaches based on Random Forests are investigated for the classifier. The structure and parameters of a sliding window function are adjusted for feature generation.

  Led by: Wage, Feuerhake, Golze, Sester

  Team: Tim Schimansky

  Year: 2022
• Nutzungsdatengetriebene Analyse des Potentials von Mikromobilitätsdiensten

  Der geteilten Mobilität wird in der öffentlichen Debatte um die Verkehrswende häufig eine entscheidende Rolle zugeordnet. Darunter fallen auch die sogenannten Mikromobilitätsdienste. Das Ziel dieser Masterarbeit ist es, das Potential von Mikromobilitätsdiensten für die Verkehrswende im Hinblick auf die Intentionen der Nutzer, auf zeitliche Variationen, sowie auf Vorteile gegenüber anderen Transportmitteln datenbasiert zu bewerten. Dafür wird eine Fallstudie anhand von Mobilitätsdaten der Bikesharing-Fahrräder und Elektrotretroller zweier Anbieter in Hannover durchgeführt.

  Led by: Wage, Feuerhake, Golze

  Team: Finn Boie

  Year: 2022
• Pattern Recognition of Movement Behavior for Intersection Classification using GPS Trace Data

  The aim of this thesis is to classify different regulator types of traffic road intersections based on GPS trace data. To reach this aim a variety of features is calculated to describe the driving behavior at intersections. These are derived from the measured units of the GPS trace data that compose an individual’s movement trajectory.

  Led by: Zourlidou

  Team: Jens Golze

  Year: 2019