The reliability of a metro system is an important factor in building user trust and ridership, as it helps maintain efficient and sustainable urban mobility. Within the high-frequency environment of metro networks, even minor disturbances can propagate across the network, resulting in secondary delays that reduce schedule stability and passenger satisfaction. With the need for analysis approaches that balance predictive flexibility with analytical interpretability, this thesis develops a data-driven framework for modelling and quantifying delay propagation in metro networks.

The proposed framework reduces the modelling of delay propagation to the statistical fitting of relationship functions between network elements using subsets of available variable data. The methodology follows five general steps: defining relationship structures, setting analysis dimensions and their granularity, selecting the method for fitting the relationship functions, fitting the functions using available data, and quantifying the residual variability. This framework is applied to the Washington D.C. Metro using schedule and operational train movement data to analyse and characterise delay propagation behaviour.

Two model implementations are developed. The first explores the full breadth of variables available in the delay data to identify those offering statistically significant delay relationships, revealing that propagation occurs predominantly between directly connected stations and along shared lines. The second, more targeted model quantifies these relationships, demonstrating that propagation strength is independent of delay magnitude and mostly consistent across different time periods. Further cross-examination revealed minimal sensitivity to operational and network design variables, such as headways and connectivity, suggesting that other variables like scheduling regimes might be the cause of the different observed delay propagation behaviours.

The results highlight that localised delay effects dominate over network-wide influences, suggesting that metro operators can improve service reliability by focusing mitigation efforts on key inter-station connections rather than entire lines. The framework offers a versatile and multiplicative foundation for future applications in delay propagation prediction and analysis.

We present a method to classify street networks using only geo-tagged street-level imagery. By combining pre-trained image embeddings with unsupervised clustering, it produces visually coherent street typologies without supervised training or labeled data and requires only minimal data curation. The approach is lightweight, scalable, and, in principle, transferable across urban contexts. In a Delft (Netherlands) case study, we classify approximately 2,000 road sections using over 70,000 images. Our method recovers distinct street types such as residential, arterial, and historic ones. These results show that pre-trained visual embeddings alone can support effective street classification from visual inputs, offering a practical tool for urban planning, transport analysis, and mobility research.

The project’s objective is to create software that can quickly create a realistic preliminary design of an urban drainage system with only a few needed input parameters. With such software, designers are not only able to create a network layout with only a few manual inputs, but can also quickly ascertain the values of multiple network attributes with only a small number of needed parameters. Doing this process with software will save a significant amount of time for designers.

The software is coded entirely in Python, with the GitHub codebase being publicly available to serve as a place to download it. The program downloads the road network data for the desired area from OpenStreetMap and converts it into a network of nodes and conduits. With a small list of general system parameters given by the user, Automatic Preliminary Design of Urban Drainage Structures (APDUDS) can calculate initial values for attributes of elements in the system. The software calculates the subcatchment area for each node, the needed installation depth of the nodes, the flow direction through the conduits, and the required conduit diameters for a given design storm. As the last step, the network is transferred into a Storm Water Management Model file, making it possible to simulate the effects of a design storm to obtain a more in-depth evaluation of the system.

The software can create preliminary designs of systems for almost any kind of road network. The resulting network and its attributes are displayed to the user using multiple graphs, and the user can interact with the program through terminal prompts. The generated networks can almost completely handle the given design storm, but some issues do occur. Some flooding exist for nodes on the fringes of the system, although this is a low amount (below five percent). It also does not consider the actual geography of the area, which may result in the software creating a system that is unusable in the real world. Overall the objective of quickly creating preliminary networks has been completed, with network creation possible for any area with available OpenStreetMap road data.