High-speed rail (HSR) is often considered a promising and sustainable alternative for long-distance travel in the European context, aligned with Europe’s ambitious mobility and climate goals for 2050. However, a cohesive European HSR network is yet to be realised. Critically, the planning of a European HSR network requires considering how the network is to gradually evolve from its current fragmented state. We introduce an Evolutionary Network Growth model with Infrastructure and Network Effects considerations for European Rail (ENGINEER). This novel iterative network growth model selects the HSR infrastructure with the highest economic potential, continuously updating network configurations and demand patterns, subject to budget feasibility constraints. ENGINEER integrates cost estimates based on a microscopic representation and benefits estimated based on a macroscopic travel demand representation and is applied across 28 European countries. Our findings highlight the importance of path dependency and the benefits of an integrated decision-making in infrastructure planning. Model results demonstrate that ENGINEER can effectively identify promising HSR investments, yielding a cohesive and well-integrated European HSR network which leads to an increase in rail mode share per trip from 13% in 2023 to 27% by 2065.

Reducing the impact of disruptions is essential to provide reliable and attractive public transport. In this work, we introduce a topological approach for evaluating recoverability, i.e., the ability of public transport networks to return to their original performance level after disruptions, which we model as topological perturbations. We assess recoverability properties in 42 graph representations of metro networks and relate these to various topological indicators. Graphs include infrastructure and service characteristics, accounting for in-vehicle travel time, waiting time, and transfers. Results show a high correlation between recoverability and topological indicators, suggesting that more efficient networks (in terms of the average number of hops and the travel time between nodes) and denser networks can better withstand disruptions. In comparison, larger networks that feature more redundancy can rebound faster to normal performance levels. The proposed methodology offers valuable insights for planners when designing new networks or enhancing the recoverability of existing ones.

In this article, we introduces a model based on big data analysis to characterize the travel times of buses in public transportation systems. Travel time is a critical factor in evaluating the accessibility of opportunities and the overall quality of service of public transportation systems. The methodology applies data analysis to compute estimations of the travel time of public transportation buses by leveraging both open-source and private information sources. The approach is evaluated for the public transportation system in Montevideo, Uruguay using information about bus stop locations, bus routes, vehicle locations, ticket sales, and timetables. The estimated travel times from the proposed methodology are compared with the scheduled timetables, and relevant indicators are computed based on the findings. The most relevant quantitative results indicate a reasonably good level of punctuality in the public transportation system. Delays were between 10.5% and 13.9% during rush hours and between 8.5% and 13.7% during non-peak hours. Delays were similarly distributed for working days and weekends. In terms of speed, the results show that the average operational speed is close to 18 km/h, with short local lines exhibiting greater variability in their speed.

Public transport plays a key role in expanding the distances that people can travel using active modes of transport. Studying walking accessibility to public transportation systems is highly relevant, since the walk to stops/stations can be particularly challenging for children, the elderly, citizens with disabilities, and for the general population during bad weather conditions or in pedestrian-unfriendly cities. This work presents a study on walking accessibility for the public transport system in Montevideo, Uruguay. The proposed methodology combines information of the bus stops and lines that operate in the city, the road infrastructure, and demographic information of the city to compute walking accessibility indicators to the public transport system. The results of the analysis suggest that over 95.5% of the population can access at least one stop when walking up to 400 m. However, these values are not evenly distributed among the population, with young citizens and men showing lower levels of coverage compared to their counterparts.