Italian Railway Network Research Articles

Innovative approaches to promote inclusive processes for noise barriers landscape integration

Noise barriers, in addition to their own functions as specific mitigation interventions to protect health, can represent important architectural elements, both in urban environment and natural context. In this perspective the design stage cannot ignore an integrated evaluation of the reference social and natural context based on the peculiarities and vocations of the territories involved. Aiming to the primary objective of identifying the "right" project that best fits into the reference territorial context, also for acoustic barriers it is therefore necessary to carry out a preliminary analysis of the socio-territorial and landscape context to understand specific local features of the territories and promote barriers as an essential part of the urban environment and landscape in a synergistic vision between innovation and sustainability also through new models of co-design and co-management with the communities involved. This paper aims to describe the approach adopted by RFI - the National Manager of the Italian Railway Network - to promote effective integration and synergy between noise mitigation interventions and the territorial reference context with the aim of creating a "tailor-made" project with the contribution of citizens and stakeholders.

A methodological framework for flood hazard assessment for land transport infrastructures

The complexity of land transport infrastructures, their length and the heterogeneity of potential hazardous scenarios across large areas make flood hazard assessment a challenging task. We propose a parsimonious, rapid and operational procedure that enables large scale applications and, at the same time, a detailed evaluation of individual segments of linear transport infrastructures. In particular, we focus on extensive land transport infrastructure networks (i.e., railways) and propose a methodological framework for: (1) identifying segments of the network that are prone to fluvial flooding, and (2) grouping the identified elements into different hazard classes depending on the mechanism and severity of the expected flooding scenario (i.e., damage due to potential embankment overtopping, embankment instability due to infiltration, erosion or washout, train or vehicles collision against fluvial deposits or debris, etc.). Network sections at risk, as well as main hazardous flood indices (e.g., water depth), are estimated referring to official inundation maps, when available, or by means of Digital Elevation Model (DEM)-based algorithms and geomorphic indices (e.g., Geomorphic Flood Index-GFI). In addition, a DEM-based approach is proposed to identify potential debris flow sources threatening the infrastructure. The procedure has been developed and tested for the Italian Railway Network (focusing on seven river basins), but it can be adapted to different cases and different land transport infrastructures. A first comparison with historical flood events proves the potential of the procedure, which can support stakeholders in planning measures for managing and mitigating the flood risk associated with the transport networks across large spatial scales.

Improvement of Energy Savings in Electric Railways Using Coasting Technique

The main goal of this work is the evaluation of the energy saving achievable in railway drive when using the coasting technique extensively, with reference to a practical case of the Italian railway network taken as an example. This technique consists in exploiting the kinetic energy accumulated by the running train whenever possible. To implement a driving style on purpose, the only driver contribution is not enough; indeed, it is necessary to provide an embedded automatic calculation control system. In the paper, an algorithm has been developed to evaluate the energy absorption of railway locomotives during the normal service and validated on a real railway line. The proposed hardware and software system could be implemented aboard the train, allowing motion data processing in real-time. Speed, time intervals and power absorption for a given path are calculated; then, the best coasting parameters are estimated to maximize the energy savings. In particular, the case study presented in the paper showed that the fast-run strategy, always adopted by the railway company to recover an unexpected delay, can lead to a negligible time recovery with respect to the coasting strategy, while determining a significantly larger energy consumption.

Rockfall susceptibility and network-ranked susceptibility along the Italian railway

Rockfalls pose a substantial threat to ground transportation, due to their rapidity, destructive potential and high probability of occurrence on steep topographies, often found along roads and railway routes. Approaches for the assessment of rockfall susceptibility range from purely phenomenological methods and statistical methods, suitable for modeling large areas, to purely deterministic ones, usually easier to use in local analyses. A common requirement is the need to locate potential detachment points, often found uphill on cliffs, and the subsequent assessment of the runout areas of rockfalls stemming from such points.Here, we apply a physically based model for the calculation of rockfall trajectories along the whole Italian railway network, within a corridor of total length of about 17,000 km and varying width. We propose a data-driven method for the location of rockfall source points based on expert mapping of potential source areas on sample representative locations. Using empirical distributions of gridded slope angle values in source areas mapped by experts, we derived probabilistic maps of rockfall sources in the proximity of the railway network, regardless of a particular trigger.Source areas act as starting points of simulated trajectories within the three-dimensional model STONE. The program provides a pixel-by-pixel trajectory count, covering 24,500 km2 and representing the largest homogeneous application of the model to date. We classified the raster map into a vector susceptibility map, analyzing the railway track as a collection of segments, for which we provide segment-wise rockfall susceptibility.Eventually, we considered an equivalent graph representation of the network, which helps classifying the segments both on the basis of rockfall susceptibility and the role of each segment in the network, resulting in a network-ranked susceptibility. Both maps are useful for subsequent hazard assessment, and to prioritize safety improvements along the railway, at national scale.

Development and validation of a safe communication protocol compliant to railway standards

Railway systems are composed of a multitude of subsystems, sensors, and actuators that exchange datagrams through safety-critical communication protocols. However, the vast majority of these protocols rely on ad hoc interlacing mechanisms and safety codes which raise the heterogeneity and complexity of the overarching railway system. Therefore, Rete Ferroviaria Italiana, the company who is in charge of managing the Italian railway network, coordinated the definition of the Protocollo Vitale Standard (Standard Vital Protocol). This protocol is inspired to, and compliant with, the communication protocols adopted for the European Train Control System (ETCS) (SUBSET, UNISIG, 037, Euroradio FIS, version 2.3. 0; SUBSET, UNISIG, 098, RBC-RBC safe communication interface, 2007), and it is meant to become the standard layer to enable safe communication between components of the Italian railway system. This paper reports our experience in the design, implementation, verification, and validation of the Protocollo Vitale Standard in compliance with the European safety standards for railway systems. We first defined a safety plan and a verification and validation plan, which guide the design, development, verification, and validation activities as required by safety standards. Guidelines of such plans have been followed strictly until completion of the work, which concludes with the provision of a safety case where all safety evidences are summarized. Noticeably, we (i) selected appropriate safety mechanisms, (ii) verified the software design, (iii) implemented the software in compliance with code metrics and coding rules, (iv) conducted tests to validate the protocol against its functional and performance requirements, and ultimately (v) devised all relevant documentation and a safety case which summarizes the evidences needed for certification.

Pork, Infrastructure and Growth: Evidence from the Italian Railway Expansion

This paper studies the role played by politics in shaping the Italian railway network, and its impact on long-run growth patterns. Examining a large state-planned railway expansion that took place during the second half of the 19th century in a recently unified country, we first study how both national and local political processes shaped the planned railway construction. Exploiting close elections, we show that a state-funded railway line is more likely to be planned for construction where the local representative is aligned with the government. Furthermore, the actual path followed by the railways was shaped by local pork-barreling, with towns supporting winning candidates more likely to see a railway crossing their territory. Finally, we explore the long-run effects of the network expansion on economic development. Employing population and economic censuses for the entire 20th century, we show that politics at a critical juncture played a key role in explaining the long-run evolution of local economies.

Methods for managing hydrogeological and seismic hazards on the Italian railway infrastructure

As dramatically confirmed by the recent earthquakes in Amatrice and Visso in 2016, and by the collapse of a road bridge along the Savona-Torino highway in 2019, Italy and all its constructions and infrastructures passing through its territory are potentially exposed to considerable seismic and hydrogeological risks. Rete Ferroviaria Italiana (RFI), in its role of Infrastructure Manager, is responsible for the management and safety of the rail traffic on the Italian network. RFI has therefore implemented a safety management system that ensures the control of the risks associated with railway operations, including seismic and hydrogeological risks. The first part of the paper provides an overview on the exposition of the Italian railway network. The strategies adopted by RFI for mitigating the abovementioned risks are then described as based on a combination of passive and active countermeasures. Eventually, the paper describes the future trends and developments for updating and improving the internal procedures and the tools currently available for reducing the consequences on the railway network of potentially harmful natural events.

Insularity and the development of a local railway network

This paper quantitatively assesses the negative impact of land discontinuity on the development of a railway network on an island. This implicit cost of insularity is because an insular railway network only serves the territory in which it is located while the same network on a mainland also serves other regions. We apply this idea to the case of a simplified Italian railway network and we implement it through a simulation model. The simulation results highlight the strong negative effect of land discontinuity: whereas the railway lines located on the island of Sardinia are the least profitable under the factual scenario, their relative profitability is significantly boosted in every counterfactual scenario where the land discontinuity is artificially removed.

Automated Railway Signs Detection. Preliminary Results

Abstract Nowadays safety in railways is mostly achieved by automated system technologies such as ERTMS/ETCS. Nevertheless, on local railways (suburban and regional lines) several tasks still depend on the choices and actions of a human crew. With the aim to improve safety in such type of railways, this research proposes a system for the automatic detection and recognition of railway signs by means of the digital image processing technique. First field applications, carried out on the Italian railway network, show that the proposed system is very accurate (the percentage of correctly detected railway signs is about 97%), even at high train speeds.

A dynamic, interpretable, and robust hybrid data analytics system for train movements in large-scale railway networks

We investigate the problem of analysing the train movements in large-scale railway networks for the purpose of understanding and predicting their behaviour. We focus on different important aspects: the Running Time of a train between two stations, the Dwell Time of a train in a station, the Train Delay, the Penalty Costs associated to a delay, and the Train Overtaking between two trains which are in the wrong relative position on the railway network. Two main approaches exist in the literature to address these problems. One is based on the knowledge of the network and the experience of the operators. The other one is based on the analysis of the historical data about the network with advanced data analytics methods. In this paper, we will propose a hybrid approach in order to address the limitations of the current solutions. In fact, experience-based models are interpretable and robust but not really able to take into account all the factors which influence train movements resulting in low accuracy. From the other side, data-driven models are usually not easy to interpret nor robust to infrequent events and require a representative amount of data which is not always available if the phenomenon under examination changes too fast. Results on real-world data coming from the Italian railway network will show that the proposed solution outperforms both state-of-the-art experience-based and data-driven systems in terms of interpretability, robustness, ability to handle nonrecurring events and changes in the behaviour of the network, and ability to consider complex and exogenous information.

Innovative Suspended Superstructure for the Retrofitting of a Steel Truss Railway Bridge

The bridge in Poggio Renatico crossing the Reno river on the railway line between Bologna and Ferrara, consists of steel decks supported by masonry abutments and piers, while foundations and pier caps are made of reinforced concrete. After the 2012 Emilia Earthquake and accounting for the fluvial erosion below the piers foundations, a structural assessment of the bridge was carried out in accordance with to the current Italian rules. Although a sufficient structure capacity against the actual transit loads resulted, the bridge showed some inadequacies with respect to the load models provided by both the national standard for constructions and the guidelines of the Italian railway network company. The retrofitting project consists of an improvement of the structural capacity of all the elements: girders, piers and abutments. An innovative retrofitting solution provides for the strengthening of the existing decks through a suspension system of cables anchored to steel towers standing on both abutments and piers and creating a kind of suspended bridge. The retrofitting is compatible with the normal scheduled interruptions of the rail traffic thereby reducing the maintenance costs of the railway system. The structural solution represents a model easily replicable to solve analogous situations along the railway network.

Dynamic Delay Predictions for Large-Scale Railway Networks: Deep and Shallow Extreme Learning Machines Tuned via Thresholdout

Current train delay (TD) prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of endogenous (i.e., generated by the railway system itself) and exogenous (i.e., related to railway operation but generated by external phenomena) data available. Additionally, they are not designed in order to deal with the intrinsic time varying nature of the problem (e.g., regular changes in the nominal timetable, etc.). The purpose of this paper is to build a dynamic data-driven TD prediction system that exploits the most recent tools and techniques in the field of time varying big data analysis. In particular, we map the TD prediction problem into a time varying multivariate regression problem that allows exploiting both historical data about the train movements and exogenous data about the weather provided by the national weather services. The performance of these methods have been tuned through the state-of-the-art thresholdout technique, a very powerful procedure which relies on the differential privacy theory. Finally, the performance of two efficient implementations of shallow and deep extreme learning machines that fully exploit the recent in-memory large-scale data processing technologies have been compared with the current state-of-the-art TD prediction systems. Results on real-world data coming from the Italian railway network show that the proposal of this paper is able to remarkably improve the state-of-the-art systems.

Train Delay Prediction Systems: A Big Data Analytics Perspective

Abstract Current train delay prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of historical train movements data collected by the railway information systems. Instead, they rely on static rules built by experts of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven Train Delay Prediction System (TDPS) for large-scale railway networks which exploits the most recent big data technologies, learning algorithms, and statistical tools. In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays. Proposal has been compared with the current state-of-the-art TDPSs. Results on real world data coming from the Italian railway network show that our proposal is able to improve over the current state-of-the-art TDPSs.

Managing the Italian High-Speed Railway Network: Provisions for Reducing Interference Between Electric Traction Systems

The Italian high-speed (HS) railway network consists of about 1,000 km of double track lines. It is still growing, and a new section of about 50 km will be put in service in 2016. The HS railway network is broadly equipped, especially in the countryside, with a 2 ? 25 kV, 50 Hz electrification system, while in the urban areas as well as for the HS Rome-Florence line, a 3 kV direct current (dc) electrification system was adopted. The peculiarity of the Italian HS system is the high number of interconnections with the former railway network, called the conventional railway. Twenty double-track interconnections are currently in operation to manage many traffic routes. Since December 2005, when the first 2 ? 25 kV alternating current (ac) line was placed into service, the Italian railway infrastructure management has gained experience in managing the electrical interferences between the 3 kV dc and the 2 ? 25 kV ac electrification systems. This article refers to the main technical aspect related to more than ten years of experience in handling the boundary between different electric traction systems. Figure 1 gives an overview of the Italian HS network. For each line, the first year of service, the relevant electrification system, and the number of interconnections with the conventional railway network are given.

EMC Analysis of Axle Counters in the Italian Railway Network

The need of transport interoperability in Europe is a relevant issue nowadays. Particular interest is given to the signaling systems. The TEN-T project focuses one of its workpackages on “EMC Axle Counter Validation.” This work developed in the frame of the TEN-T project develops a procedure to assess the frequency spectrum of the magnitude of the magnetic field, due to the traction current, at the track level. Full-wave 3-D numerical models are developed to predict the field values. The measuring system is previously tested and then used for a measurement campaign. Results are reported in terms of tables and frequency spectra.

An innovative localisation algorithm for railway vehicles

In modern railway automatic train protection and automatic train control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. The aim of this work has been developing an innovative localisation algorithm for railway vehicles able to enhance the performances, in terms of speed and position estimation accuracy, of the classical odometry algorithms, such as the Italian Sistema Controllo Marcia Treno (SCMT). The proposed strategy consists of a sensor fusion between the information coming from a tachometer and an Inertial Measurements Unit (IMU). The sensor outputs have been simulated through a 3D multibody model of a railway vehicle. The work has provided the development of a custom IMU, designed by ECM S.p.a, in order to meet their industrial and business requirements. The industrial requirements have to be compliant with the European Train Control System (ETCS) standards: the European Rail Traffic Management System (ERTMS), a project developed by the European Union to improve the interoperability among different countries, in particular as regards the train control and command systems, fixes some standard values for the odometric (ODO) performance, in terms of speed and travelled distance estimation. The reliability of the ODO estimation has to be taken into account basing on the allowed speed profiles. The results of the currently used ODO algorithms can be improved, especially in case of degraded adhesion conditions; it has been verified in the simulation environment that the results of the proposed localisation algorithm are always compliant with the ERTMS requirements. The estimation strategy has good performance also under degraded adhesion conditions and could be put on board of high-speed railway vehicles; it represents an accurate and reliable solution. The IMU board is tested via a dedicated Hardware in the Loop (HIL) test rig: it includes an industrial robot able to replicate the motion of the railway vehicle. Through the generated experimental outputs the performances of the innovative localisation algorithm have been evaluated: the HIL test rig permitted to test the proposed algorithm, avoiding expensive (in terms of time and cost) on-track tests, obtaining encouraging results. In fact, the preliminary results show a significant improvement of the position and speed estimation performances compared to those obtained with SCMT algorithms, currently in use on the Italian railway network.

Development of a model for the simultaneous analysis of wheel and rail wear in railway systems

The prediction of wheel and rail wear is a fundamental issue in the railway field, both in terms of vehicle stability and in terms of economic costs (planning of maintenance interventions). In particular the need of an accurate wear model arises from the interest of Trenitalia S.p.A. and Rete Ferroviara Italiana in designing new wheel and rail profiles and new bogie architectures optimized from the wear viewpoint with the aim of improving the wear and stability behavior of the standard ORE S1002 wheel profile matched with the UIC60 rail profile canted at 1/20 rad (which represents the wheel–rail combination adopted by the Italian railway line). In this work the authors present a wear model specifically developed for the evaluation of the wheel and rail profile evolution, the layout of which is made up of two mutually interactive but separate units: a vehicle model for the dynamical analysis and a model for the wear evaluation. Subsequently the new model has been compared with the wear evaluation procedure implemented in Simpack, a widely tested and validated multibody software for the analysis of the railway vehicle dynamics; the comparison aims both to evaluate the model performance (in terms of accuracy and efficiency) and to further validate the wear model (just tested, as regards the wheel wear prediction, in previous works related to the critical Aosta–Pre Saint Didier line). The comparison has been carried out considering a benchmark train composed by a locomotive (E.464) and a passenger vehicle (Vivalto) provided by Trenitalia while the simulations have been performed on a mean Italian railway line (obtained by means of a statistical analysis of the data relative to the whole Italian railway network provided by Rete Ferroviaria Italiana (RFI)).

A localization algorithm for railway vehicles based on sensor fusion between tachometers and inertial measurement units

The availability of a reliable speed and travelled distance estimation is relevant for the efficiency and safety of automatic train protection and control systems. This paper investigates the main features of an innovative localization algorithm that integrates tachometers and inertial measurement units. Nowadays, the estimation is performed by an odometry algorithm that relies on wheel angular speed sensors. The objective is to increase the accuracy of the odometric estimation, especially in critical adhesion conditions, through sensor fusion techniques based on Kalman filter theory. The Italian company ECM S.p.A. has supported the project, providing a custom inertial measurement unit based on micro electro-mechanical system sensors for the on-track testing of the algorithm. The preliminary results show a significant improvement of the position and speed estimation performances compared to those obtained with SCMT (Italian acronym for ‘Sistema Controllo Marcia Treno’) algorithms, currently in use on the Italian railway network. A wide set of simulated test results, showing the improvement of the estimation process, is presented and discussed. An accurate train navigation that scarcely relies on information from the infrastructure will open a road map for the development of a more and more effective and efficient exploitation of the railway infrastructure.

A pilot study of the wind speed along the Rome–Naples HS/HC railway line.: Part 2—Probabilistic analyses and methodology assessment

The safety of railway operations under wind actions has recently become a topical matter due to the development of high-speed trains, which are very sensitive to crosswind conditions. For this reason, RFI (the Italian railway network) has entrusted the University of Genoa with a study of the wind hazard of the Rome–Naples High Speed (HS)/High Capacity (HC) railway line. A consistent part of such a study is focused on the probabilistic analysis of the wind speed and direction along that railway line, with the aim of developing a general procedure that can be applied to any railway line. This paper represents the logical prosecution of a companion paper where numerical simulations have been carried out of the wind fields along the line. These results are used together with a large amount of data measured by neighbouring meteorological stations in order to establish a probabilistic model of the wind speed and direction along the line. Such model provides a full representation of the wind climate and hazard of the railway line, and represents the basic step towards the development of a full risk analysis. Preliminary evaluations are also reported in order to assess the robustness and reliability of the methodology applied and the results obtained.

A pilot study of the wind speed along the Rome–Naples HS/HC railway line. Part 1—Numerical modelling and wind simulations

The safety of railway operation under wind actions has recently become a topical matter due to the development of high-speed trains, which are very sensitive to crosswind conditions. For this reason, RFI (the Italian railway network) has entrusted the University of Genoa with a study of the wind hazard of the Rome–Naples High Speed (HS)/High Capacity (HC) railway line. A consistent part of this study is focused on the probabilistic analysis of the wind speed and direction along that railway line, with the aim of developing a general procedure that can be applied to any railway line. This paper and its companion illustrate both the general procedure that has been developed and the main results of its application. The present paper specifically deals with the numerical modelling and wind simulations preliminary to the probabilistic assessment of the wind speed along the line, which is treated in the companion paper.