High-speed rail (HSR) is gaining increasing attention due to its sustainability and transport capacity, aligning with ambitious European transport goals for 2030 and 2050. Despite its potential, the creation of a unified European HSR network faces challenges rooted in poor coordination and national interests. This study addresses the absence of a comprehensive, long-term strategy for establishing such a network, with a specific focus on critical infrastructure development. It introduces an iterative network growth model to determine where, when, and at what cost HSR infrastructure should be built under centralized decision-making processes given current budget allocations. The approach analyses the dynamic interaction between infrastructure expansion and the long-distance transport market demand distribution. Results emphasize the benefits of adopting centralized decision-making and appraisal processes, highlighting that achieving these goals requires a comprehensive, collaborative effort, as well as a proper European institutional investment management with more spending power.

Public transport (PT) plays a vital role in commuting billions of travellers in cities all over the world, providing a mode that is both sustainable and accessible. Metro networks are especially apt at this considering their high-capacity and high-speed operation in urban environments. Comparing different metro networks to one another is a suitable manner for transport planners to gain insights into the characteristics of their networks and which areas of improvement exist. In the field of network science, metro networks have been studied extensively in recent decades. While this provided many new insights in the field of network science, the practical relevance for the field of transport science often remained limited. This limited relevance is primarily caused by the lack of realism of the network representations used, not incorporating the actual operation and service that the network provides. As such, this study proposes a comprehensive comparison of metro networks worldwide including service information. This comparison study includes service characteristics in the form of the total travel time indicator for shortest path calculations, which is a combination of the in-vehicle time, waiting time and number of transfers. The median of this total travel time is taken for each network and compared to that of other networks. This metric in turn is contrasted with network- and city-related characteristics in order to explore relations between these factors and to explain the patterns discovered. From this analysis, it is revealed that the travel time increases with network size. The indicator that is especially apt at explaining the differences in total travel time between networks is the number of stations combined with the average direct station distance. The total travel time methodology applied in this study shows significantly different results to other commonly used methods that rely only on in-vehicle time or hops to calculate shortest path travel times. The waiting time turns out to be the main contributor to these significant differences. Future studies can expand on this by considering other network science indicators and looking further into local indicators. In addition, the methodology could be expanded with more detailed transfer information and other PT modes.