Open Thesis
Available Bachelor Thesis Topics
Beschreibung
Der hydraulische Abgleich in Heizungsanlagen sorgt dafür, dass die Wärme im Haus gleichmäßig über die Heizkörper verteilt wird. Zu diesem Zweck gibt es Berechnungsschemata, die auf verschiedenen Parametern basieren, die oft geschätzt werden müssen. Ziel der Bachelorarbeit ist es, die Schätzungen durch einen Feedback-Mechanismus zu verbessern, indem der vorhergesagte und gemessene Energieverbrauch und die Temperaturen iterativ verglichen werden. Das Modell soll mit einfachen Simulationen von Heizungsanlagen in Häusern getestet werden. Im Erfolgsfall kann der Ansatz dazu beitragen, die Energiebilanz von Heizungsanlagen deutlich zu verbessern.
Voraussetzungen
- Eigenständiges Arbeiten
- Sicherer Umgang mit Python
Kontakt
- Prof. Dr. Frank Puppe, frank.puppe@uni-wuerzburg.de
- Prof. Dr.-Ing. Marco Pruckner, marco.pruckner@uni-wuerzburg.de
Sprache
- Deutsch
Available Master Thesis Topics
Description
Realistic mobility demand is an essential input for various scientific simulations. For example, we can model the spread of diseases better if we can determine what groups of people come into regular contact. Other examples are the performance evaluation of close-range peer-to-peer communication systems and transportation system planning. However, for privacy reasons, it is not desirable to track the movement of the entire population of a country to acquire the necessary data. Instead, it is better to create synthetic mobility demand with an agent-based simulation that is representative of the original population without intruding on the privacy of individuals.
This is the purpose of the open-source mobility demand generator OMOD we developed at our chair (https://github.com/L-Strobel/omod).
A crucial step in creating synthetic mobility demand is determining where people will conduct activities like shopping or working. In classical models, this is done in two steps. First, the number of trips that originate and end in all locations is determined with linear models. Then, a so-called gravity model connects origins and destinations (See Modelling Transport, Ortúzar and Willumsen).
In OMOD, we use a similar approach adapted to fit the agent-based viewpoint. However, this approach runs into limitations if non-linear features are to be included for the attractiveness of a location. For example, there is a complex relationship in clusters of shops. On the one hand, a group of shops might be more attractive than the sum of its parts because a person could shop for multiple things at once there; think of a city center. On the other hand, at some point, a location already provides a shop for most things, and shops start to compete, reducing the marginal attractiveness of additional shops.
Another problem that arises from the classical formulation is finding a suitable calibration for the parameters of the function. This is usually done using the maximum likelihood method. However, solving the resulting optimization is increasingly difficult with a rising number of parameters. Therefore the methodology is limited in its ability to model more complex relationships between mapping information and sociodemographic-features of individuals.
The goal of this work is to find alternatives to the classical implementation of the destination choice using modern machine learning techniques, such as neural networks. The techniques can be applied to directly determine the destination choice or, alternatively, to find suitable parameters for the old implementation.
Requirements
Necessary:
- Confident with Python
Helpfull:
- Understanding the Maximum-Likelihood concept
- Experience with a machine learning library
Contact
Leo Strobel, leo.strobel@uni-wuerzburg.de
Description
In agent-based transport models, it is necessary to determine the shortest path between an agent's current location and destination to simulate the agents' decision-making process. Algorithms for finding the shortest path on a network are well understood and applicable (Dijkstra). However, we must determine thousands of paths per agent for millions of agents in agent-based models. Therefore, determining the shortest paths is often the bottleneck of the simulation. The problem is especially prevalent in public transit decisions, where finding the shortest path depends on the weekday and time of day because of irregular schedules.
The goal of this thesis is to create a lightweight public transit router based on publicly available GTFS files (https://developers.google.com/transit/gtfs). A GTFS file parser is already available, and routing algorithms from existing libraries can be used. The focus of the thesis should be on searching the literature for applicable routing algorithms and methods to simplify the routing graph to obtain approximate solutions. The algorithms are then to be implemented and evaluated regarding runtime and accuracy of the resulting travel duration.
Requirements
Necessary:
- Experience with Java or Kotlin
- Basic knowledge of shortest path algorithms (Dijkstra)
Helpfull:
- Experience with handling data tables (>= 100 k rows)
Contact
Leo Strobel, leo.strobel@uni-wuerzburg.de
