Trenitalia (Italy)

Probabilistic Fault Displacement Hazard Analysis (PFDHA) quantifies the probability and the expected amount of coseismic displacement associated with the activity of Active and Capable Faults (ACFs) at a given site. Common PFDHA approaches distinguish between primary on-fault displacement and distributed off-fault ruptures occurring on secondary faults or fractures and typically rely on empirical scaling relationships calibrated for specific earthquake magnitudes. However, these methods are often calibrated for specific tectonic or kinematic settings and lack readily available computational tools. Moreover, available PFDHA approaches do not commonly include the possibility to investigate the floating rupture mechanism, i.e., the possibility that surface ruptures involve only portions of the full fault trace.To overcome these limitations, we developed FaulTED, a new user-friendly MATLAB-based code for PFDHA that integrates a comprehensive set of published models, including magnitude–frequency distributions, fault scaling relationships, and surface rupture probability functions. The toolkit comprises two main modules: (i) a site-specific hazard curve calculator and (ii) a fault-specific hazard map generator for user-defined return periods. Both modules explicitly account for on-fault and distributed off-fault ruptures and incorporate the floating rupture approach commonly adopted in probabilistic seismic hazard analysis.The modular architecture of FaulTED allows users to flexibly select and compare alternative models through a structured input file, enabling sensitivity analyses and systematic exploration of epistemic uncertainties. FaulTED is designed as a user-oriented platform to support infrastructure planning in regions affected by ACFs.

Floods pose significant threats to railway infrastructure, given their linear extension across diverse landscapes and frequent intersections with rivers. While European countries have developed Flood Risk Management Plans (FRMPs) following the EU Floods Directive, a comprehensive analysis of railway network flood susceptibility at national scale is still lacking for Italy. Here we develop a comprehensive flood hazard classification for the Italian Railway Network (IRN). Our methodology integrates flood hazard maps, railway infrastructure data, and digital elevation models to characterize flood hazard classes along flood-prone railway routes. The approach distinguishes between steep rapid, rapid and slow flood processes, based on topographical characteristics. Results demonstrate that, for the low probability flood hazard scenario (return period ≥500 years), 25.63 % of the IRN (4,523.4 km) exhibits flood susceptibility, with this proportion declining to 19.09 % and 9.77 % for medium and high flood hazard, respectively. By performing a regional analysis across seven hydrographic districts in Italy, a substantial spatial variability emerges, with the Po River district encompassing nearly half (47.5 %) of all flood-prone railway sections. Our analysis reveals also a marked predominance of rapid flood processes, characterized by values for the time of concentration <12 h. Our classification framework provides crucial insights for risk mitigation and resource allocation, relying exclusively on FRMPs and digital elevation models. The methodology presents a scalable approach applicable to other transportation networks and study areas, supporting infrastructure managers in developing targeted flood protection measures. • DEM-based flood hazard classification for the Italian Railway Network (IRN). • Low probability flood hazard scenario impact about 26 % of the IRN. • Rapid flood processes dominate 65 % of flood-prone IRN. • 42 % of flood-prone IRN is also prone to debris-flow.

Transport systems have a significant impact on socioeconomic growth. However, traditional cost-benefit analysis (CBA) techniques are often insufficient to address the critical social aspects of transport planning. Our research comprehensively reviews notable social theories and their relevance to transportation. We examine the existing literature on the social impact of investments in transportation networks and services, emphasizing social justice and equity in decision-making processes. Furthermore, we list additional methods and tools to complement CBA in assessing social dimensions. Finally, this study presents a novel application based on the methodological review above, developed in order to determine the impact of introducing high-speed rail on demographic vibrancy in Italy.

In tectonically active regions, surface faulting and offset of the ground surface caused by capable faults pose significant hazard to urban settlements and critical infrastructures. Given the challenges in fully parametrizing the geometry, kinematics, and activity of a capable fault, Probabilistic Fault Displacement Hazard Analysis (PFDHA) is widely employed. PFDHA is a relatively recent methodology that estimates the probability and magnitude of expected surface displacement at a given site during an earthquake.Current methods for Fault Displacement Hazard Analysis (FDHA) are commonly tailored to specific kinematic scenarios and often rely on scaling laws that are based on the characteristic earthquake magnitude. These approaches typically distinguish between displacements occurring along the primary fault (PF) and those occurring at distributed off-fault ruptures (DR). However, only a limited number of these methods are associated with computational tools, and their accessibility to users varies widely.This study introduces a new Matlab-based tool that integrates published scaling laws, surface rupture models, and fault displacement models into a PFDHA framework. The tool supports hazard assessment for both PF and DR displacements and incorporates the concept of floating rupture along faults, a common practice in probabilistic seismic hazard assessment.The modular design of the code provides users flexibility in generating hazard curves and maps by allowing them to select a variety of kinematic-specific components within the PFDHA. Furthermore, it allows the hazard assessment that considers distinct frequency-magnitude distributions.Moreover, a novel approach to address co-seismic ruptures that may be shorter than the total length of the main fault is proposed. It involves translating fault segments along the fault trace, with the co-seismic rupture length evaluated over a range of Mw values, such as those derived from a truncated Gutenberg-Richter distribution. The conditional probability of exceedance is then determined by recalculating the x/L points corresponding to the site location (x) in each rupture length (L) translating along the total length of the fault. Contributions from all scenarios are aggregated to produce the total hazard for distributed ruptures.This new tool aims to advance the current state of PFDHA by addressing variability among current models, facilitating direct comparisons between published methodologies for both PF and DR.

Geo-hydrological hazards, particularly rapid flow-like landslides, present a critical challenge for transportation infrastructures globally. These phenomena pose severe risks due to their ability to propagate rapidly and cause extensive damage to railway tracks, vehicles, and human life. Climate change exacerbates these risks by intensifying precipitation patterns, further increasing landslide frequency and impact.This study introduces an innovative methodology for assessing the exposure of railway infrastructure to rapid flow-like landslides on a national scale [1]. Applying this methodology to Italy's extensive railway network, we integrate statistical and conceptual models, utilizing digital elevation models (DEMs) and landslide inventories to identify landslide source areas, simulate runout paths, and evaluate exposure. The results yield susceptibility and exposure maps that highlight vulnerable railway segments and provide a foundation for risk mitigation and resource allocation.The methodology involves distinguishing between hillslope and channelized landslides, each with unique source area characteristics and propagation behaviors. Channelized landslides, often occurring within confined channels, exhibit longer runout distances and lower reach angles compared to hillslope phenomena, which are more dispersed and occur on open slopes. This distinction allows for tailored modeling approaches to improve the accuracy of predictions. Validation using an independent landslide dataset confirmed the model's robustness, achieving Area Under the Receiver Operating Characteristic (AUROC) curve values between 0.7 and 0.95 in most regions, demonstrating its effectiveness for large-scale assessments. However, in areas where model performance was lower, biases in the validation dataset, such as inconsistent landslide classifications or incomplete coverage, were often identified as contributing factors.Key findings indicate that approximately 20.1% of the Italian railway network exhibits exposure values exceeding 0.5, with 13.4% classified as highly exposed (exposure &gt;0.75) to rapid flow-like landslides. Regions such as Trentino-Alto Adige, Campania, and Sicily are particularly affected due to their geomorphological and climatic conditions. This highlights the urgent need for targeted interventions to safeguard critical infrastructure and minimize disruptions to transportation services.The study emphasizes the utility of high-quality landslide inventories and DEMs in developing predictive models applicable at national scales. The outputs enable stakeholders to prioritize interventions, such as reinforcing vulnerable railway segments, implementing early warning systems, and optimizing maintenance schedules. These measures not only mitigate immediate risks but also contribute to long-term infrastructure resilience. Furthermore, the methodology&amp;#8217;s adaptability makes it applicable to other linear infrastructures and regions facing similar hazards, showcasing its potential for broader implementation.Marchesini et al., Eng. Geol. 332 (2024) https://doi.org/10.1016/j.enggeo.2024.107474

Since tourism involves the movement of people, mobility and tourism are deeply interconnected, with mutual growth dynamics but also shared negative effects, such as greenhouse gas emissions. Among the wide spectrum of policies and strategies for making tourism sustainable, soft mobility has gained increasing importance, becoming more and more a widespread form of active tourism, especially after the COVID-19 pandemic. A sustainable approach, aimed at maintaining a balance between economic development and environmental protection, requires greater promotion of soft mobility and integration with rail transport. To make active tourism truly sustainable, the support of the railway network, a low-emission collective transport system, is needed, allowing people to reach destinations and move between destinations, taking advantage of intermodality. This needs the improvement of the connection between the two transport systems, the creation of services for visitors, and the organization of an appropriate railway service. Within this framework, the present work defines a methodology that, starting from the analysis of the relationships between railway stations, the soft mobility network, and attractions in the surrounding territory, allows us to identify stations that could serve as intermodal hubs for connecting local resources through soft mobility, according to a priority scale defined by the Soft Mobility HUB (SMH) Index. The methodology, applied to Italian railway stations, is based on the use of GIS (Geographic Information System) and AHP (Analytic Hierarchy Process), allowing a rigorous, transparent, and participatory approach. The proposed model, which is replicable in other contexts, has been effective in identifying, in different scenarios, the stations most suitable for becoming hubs for soft mobility. It can serve as a support tool for decision-makers to maximize investments by focusing on stations capable to provide the greatest contribution to the development of active and diversified tourism.

Floods often cause significant damage to transportation infrastructure such as roads, railways, and bridges. This study identifies several topographic, environmental, and hydrological factors (slope, elevation, rainfall, land use and cover, distance from rivers, geology, topographic wetness index, and drainage density) influencing the safety of the railway infrastructure and uses multi-criteria analysis (MCA) alongside an analytical hierarchy process (AHP) to produce flood susceptibility maps within a geographic information system (GIS). The proposed methodology was applied to the catchment area of a railway track in southern Italy that was heavily affected by a destructive flood that occurred in the autumn of 2015. Two susceptibility maps were obtained, one based on static geophysical factors and another including triggering rainfall (dynamic). The results showed that large portions of the railway line are in a very highly susceptible zone. The flood susceptibility maps were found to be in good agreement with the post-disaster flood-induced infrastructural damage recorded along the railway, whilst the official inundation maps from competent authorities fail to supply information about flooding occurring along secondary tributaries and from direct rainfall. The reliable identification of sites susceptible to floods and damage may provide railway and environmental authorities with useful information for preparing disaster management action plans, risk analysis, and targeted infrastructure maintenance/monitoring programs, improving the resilience capacity of the railway network. The proposed approach may offer railway authorities a cost-effective strategy for rapidly screening flood susceptibility at regional/national levels and could also be applied to other types of linear transport infrastructures.

Nowadays, within the built environment, railway infrastructures play a key role to sustain national policies oriented toward promoting sustainable mobility. For this reason, national institutions and infrastructure managers need to increase their awareness in relation to the current and future climate risks on their representative systems. Among climate change impacts, preventing the effects of sea-level rise (SLR) on coastal railway infrastructures is a priority. The first step in the climate change adaptation policy cycle is the development of an ad hoc climate risk assessment. In this view, this research develops a vulnerability and a risk assessment metric to identify the hotspots within a national coastal railway due to the SLR impacts. The proposed methodology required different steps to quantify the SLR projections and the vulnerability characteristics of the assets, in terms of sensitivity and adaptive capacity. The investigated case study is the coastal railway infrastructure in Italy, thanks to an initial approach of co-design participative processes with the national Infrastructure Manager: Rete Ferroviaria Italiana (RFI). The results of this application, although not included in the paper due to confidential reasons imposed by the infrastructure manager — led to a clear identification of the areas and the coastal railway sections which are exposed to high levels of risks and of the places which require priority actions for urgent adaptation in a view of climate proof infrastructures.