Public transport reliability is often undermined by two interrelated factors: in-vehicle crowding and headway variability. While both phenomena are widely recognized, limited empirical research has quantified their combined effect in high-frequency surface transport systems, particularly under conditions of overlapping routes. This study analyzes over 1.7 million stop-level observations from the Geneva bus and tram network, integrating Automatic Passenger Count (APC), Automatic Vehicle Location (AVL), and GTFS schedule data. A random-effects ordered logistic regression model is applied to estimate how operational irregularities and network design features shape crowding levels, measured through a five-level index. Results show that headway variability is the strongest predictor of in-vehicle crowding: vehicles arriving late absorb accumulated demand and are significantly more likely to exceed design capacity. Route overlap amplifies this effect by introducing interdependencies between lines, leading to higher crowding levels in shared segments. Vehicle type also plays a role, with trams more frequently operating under crowded conditions, while trolleybuses remain less affected. These findings provide actionable insights for operators: interventions such as holding control, coordinated scheduling across overlapping lines, and targeted stop-level monitoring can mitigate the most severe crowding events without major infrastructure investments. The study contributes to transport reliability research by providing large-scale empirical evidence of the crowding–headway–overlap nexus, and offers practical guidance for agencies aiming to improve comfort and service quality in urban surface transport systems.

This thesis explores the relationship between urban fragmentation and the spatial segregation of non-EU immigrant communities in Europe. While previous research has linked infrastructural barriers to ethnic group boundaries in American cities, this study argues that such findings cannot be universally applied due to unique historical contexts in different regions. The study aims to not only identify connections between urban fragmentation and segregation but also to contextualize these connections within the local dynamics of various European cities. We employ a mixed-method approach. The quantitative component involves constructing spatial patterns of urban fragmentation using OpenStreetMap data and segregation patterns using data from the European Commission's Data for Integration (D4I) initiative. These patterns are compared using mutual information to assess their similarity. Synthetic urban fragmentation patterns are also generated to ensure observed similarities are not the result of random chance. The qualitative component involves examining the local contexts of selected cities to understand the specific factors influencing segregation. From the 106 cities analyzed, only 33 showed statistically significant relationships between urban fragmentation and segregation patterns, with 26 showing positive and 7 showing negative correlations. These findings indicate that the relationship between urban fragmentation and immigrant segregation is not a generalizable phenomenon in Europe. The city-level analysis of nine cities across seven countries reveals that factors such as urban decay and housing quality, rather than infrastructure, are more closely associated with immigrant concentration. Future research should investigate segregation patterns among other social groups and explore broader impacts of infrastructure on vulnerable populations beyond urban fragmentation.