Advancing Integrated Mobility Systems: Conceptual Frameworks, Empirical Evidence, and Behavioral Modelling of Intermodal Travel

Transport systems worldwide are undergoing profound transformation as societies seek mobility solutions that are more sustainable, inclusive, and efficient. Traditional car-oriented models are increasingly unable to address challenges such as congestion, environmental pressures, and social exclusion. In response, approaches such as Mobility-as-a-Service, shared mobility, and intermodal integration have gained prominence, aiming to provide flexible, user-centered, and multimodal transport solutions. However, important questions remain regarding how such systems are adopted, how they perform across different contexts, and how they can be effectively modelled and implemented.

This dissertation investigates how integrated mobility systems can be better understood, evaluated, and modelled to support both academic research and policy-making. It addresses key gaps by combining conceptual analysis, empirical evidence, and advanced modelling approaches into a coherent framework.

The research highlights the fragmented nature of multimodal networks and emphasizes the need for approaches that account for interactions between users, service providers, and institutional actors. It demonstrates that adoption of integrated mobility solutions depends on a combination of economic, technical, and socio-cognitive factors, requiring analytical frameworks capable of capturing complex dynamics.

Empirical findings from both rural and urban contexts show that innovative mobility solutions can support intermodal systems in different ways. Low-cost, community-based initiatives can act as connectors in rural areas, while new transport modes in cities can enhance system performance when effectively integrated with existing networks. Across contexts, reliability, accessibility, and user characteristics are key drivers of adoption.

Building on these insights, the dissertation advances the modelling of intermodal travel behavior using discrete choice methods. The results reveal significant heterogeneity in travel decisions and highlight the influence of socio-demographic factors, infrastructure conditions, and policy measures on mode combinations within trips. By capturing individual-level variability, the research provides a more realistic representation of travel behavior compared to traditional approaches.

Overall, this work contributes to a deeper understanding of integrated mobility by linking theory, empirical evidence, and methodological innovation. It provides actionable insights for designing and evaluating mobility systems that are more efficient, user-centered, and adaptable to diverse contexts, supporting the transition toward more sustainable and interconnected transport systems.