Moving Block (MB) and Virtual Coupling (VC) rail signalling will change current train operation paradigm by migrating vital equipment from trackside to onboard to reduce train separation and maintenance costs. Their actual deployment is however constrained by the industry's need to identify configurations of MB and VC signalling equipment which can effectively guarantee safe train movements even under degraded operational conditions involving component faults. In this paper, we analyse the effectivity of MB and VC in safely supervising train separation under nominal and degraded conditions by using an innovative approach which combines Fault Tree Analysis (FTA) and Stochastic Activity Networks (SAN). An FTA model of unsafe train movement is defined for both MB and VC capturing functional interactions and cause-effect relations among the different signalling components. The FTA is used as a basis to apportion signalling component failure rates needed to feed the SAN model. Effective MB and VC train supervision is analysed by means of SAN-based simulations in the specific scenario of an error in the Train Position Report (TPR) for five rail market segments featuring different traffic characteristics, namely high-speed, mainline, regional, urban and freight. Results show that the thresholds of the design variables depend on the considered signalling system alternative and the investigated market segment. In particular, the TPR delay threshold allowed for MB is higher than for VC. This means that to ensure a safe train movement, VC cannot absorb a TPR delay of longer than 1.5 s, which corresponds to the mainline market segment. For MB instead, the results show that the maximum TPR delay can reach 3.9 s for high-speed and freight railways. In addition, results showed that the integration of an FTA in a SAN model can provide a better understanding of the safety-performance behaviour of a system where VC showed a higher number of braking indications with respect to MB for the same TPR error failure rate. This means that for VC to effectively supervise the train separation at the same safety level as MB, we would need to have a much higher reliability of the TPR. The overall approach can support infrastructure managers, railway undertakings, and rail signalling suppliers in investigating the effectiveness of MB and VC to safely supervise train movements in scenarios involving different types of degraded conditions and failure events. The proposed method can hence support the railway industry in identifying effective and safe design configurations of next-generation rail signalling systems.

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Moving Block (MB) and Virtual Coupling (VC) rail signalling will change current train operation paradigm by migrating vital equipment from trackside to onboard to reduce train separation and maintenance costs. Their actual deployment is however constrained by the industry’s need to identify confi gurations of MB and VC signalling equipment which can eff ectively guarantee safe train movements even under de-graded operational conditions involving component faults. In this paper, we analyse the eff ectivity of MB and VC in safely supervising train separation under nominal and degraded conditions by using an innovative approach which combines Fault Tree Analysis (FTA) and Stochastic Activity Network (SAN). A FTA model of unsafe train movement is defi ned for both MB and VC capturing functional interactions and cause-eff ect relations among the diff erent signalling components. The FTA is then used as a basis to apportion signalling component failure rates needed to feed the SAN model. Eff ective MB and VC train supervision is analysed by means of SAN-based simulations in the specifi c scenario of an error in the Train Position Reporting (TPR) for fi ve rail mar-ket segments featuring diff erent traffi c characteristics, namely high-speed, mainline, regional, urban and freight. Results show that the overall approach can support infra-structure managers, railway undertakings, and rail system suppliers in investigating ef-fectiveness of MB and VC in safely supervising train movements in scenarios involving diff erent types of degraded conditions and failure events. The proposed method can hence support the railway industry in identifying eff ective and safe design confi gura-tions of next-generation rail signalling systems.@en

Moving Block (MB) and Virtual Coupling (VC) rail signalling will change current train operation paradigm by migrating vital equipment from trackside to onboard to reduce train separation and maintenance costs. Their actual deployment is however constrained by the industry’s need to identify configurations of MB and VC signalling equipment which can effectively guarantee safe train movements even under degraded operational conditions involving component faults. In this paper, we analyse the effectivity of MB and VC in safely supervising train separation under nominal and degraded conditions by using an innovative approach which combines Fault Tree Analysis (FTA) and Stochastic Activity Network (SAN). A FTA model of unsafe train movement is defined for both MB and VC capturing functional interactions and cause effect relations among the different signalling components. The FTA is then used as a basis to apportion signalling component failure rates needed to feed the SAN model. Effective MB and VC train supervision is analysed by means of SAN-based simulations in the specific scenario of an error in the Train Position Reporting (TPR) for five rail market segments featuring different traffic characteristics, namely high-speed, mainline, regional, urban and freight. Results show that the overall approach can support infrastructure managers, railway undertakings, and rail system suppliers in investigating effectiveness of MB and VC in safely supervising train movements in scenarios involving different types of degraded conditions and failure events. The proposed method can hence support the railway industry in identifying effective and safe design configurations of next-generation rail signalling systems.@en

Moving Block (MB) and Virtual Coupling (VC) rail signalling will change current train operation paradigm by migrating vital equipment from trackside to onboard to reduce train separation and maintenance costs. Their actual deployment is however constrained by the industry’s need to identify configurations of MB and VC signalling equipment which can effectively guarantee safe train movements even under degraded operational conditions involving component faults. In this paper, we analyse the effectivity of MB and VC in safely supervising train separation under nominal and degraded conditions by using an innovative approach which combines Fault Tree Analysis (FTA) and Stochastic Activity Network (SAN). A FTA model of unsafe train movement is defined for both MB and VC capturing functional interactions and cause effect relations among the different signalling components. The FTA is then used as a basis to apportion signalling component failure rates needed to feed the SAN model. Effective MB and VC train supervision is analysed by means of SAN-based simulations in the specific scenario of an error in the Train Position Reporting (TPR) for five rail market segments featuring different traffic characteristics, namely high-speed, mainline, regional, urban and freight. Results show that the overall approach can support infrastructure managers, railway undertakings, and rail system suppliers in investigating effectiveness of MB and VC in safely supervising train movements in scenarios involving different types of degraded conditions and failure events. The proposed method can hence support the railway industry in identifying effective and safe design configurations of next-generation rail signalling systems.@en

This deliverable has the objective to define a mathematical model for an optimised real-time management of railway traffic under Moving Block (MB). The formulated real-time traffic management model contains: i) a core module for the detection and the sub-optimal resolution of track occupation conflicts under MB and ii) a non-vital module for providing early-warning predictions of potentially hazardous MB traffic situations. The proposed real-time traffic management model includes a mathematical translation of requirements and constraints identified for both MB signalling within WP2 (namely deliverables D2.1 and D2.2) and the GNSS localisation and train integrity devices within WP3 (i.e. deliverables D3.1 – D3.3). An extensive literature review on real-time traffic management models and algorithms shows that so far research efforts have mainly focused on fixed-block and distance-to-go railway operations. Significant gaps still exist in the modelling of MB train operations, despite an increasing number of research works on MB signalling technology is observed since year 2003. A modelling gap analysis is here performed which indicates the need of enhancing existing real-time traffic management algorithms to better align them to the MB concept in terms of infrastructure representation and speed-headway functional dependency. To this end, the RECIFE-MILP real-time traffic management algorithm is enhanced. On one hand a finer infrastructure discretisation is implemented to offer a more suitable track representation under moving block which no longer uses fixed block sections. On the other hand, two different speed levels (namely maximum speed and scheduled speed) are introduced enabling a speed-dependent headway computation in either nominal or delayed traffic scenarios, thereby overcoming the limitation of speed-independent headways, typical of fixed-block traffic rescheduling models. A non-vital early-warning prediction model of hazardous MB traffic conditions is also proposed which includes a short- and a medium-term hazard identification method. In the short-term, potentially hazardous MB traffic condition are identified as violations of safety-critical threshold values of design variables relating to MB train operations (e.g. driving reaction times), the GNSS system (e.g. GNSS error or latency) and/or the GSM-R layer (e.g. MA communication delay). Safety-critical thresholds of the different design variables are identified by means of an extensive sensitivity analysis which uses a Stochastic Activity Network built for MB within WP2. In the medium-term warnings of potentially hazardous MB conditions are instead triggered whenever RECIFE-MILP detects track occupation conflicts in geographical areas with limited GNSS and/ or GSM-R signal availability, such as deep valleys or tunnels. The defined models contribute to the definition of an optimised automated Traffic Management System for Moving Block which can also support traffic dispatchers in preventively avoiding the occurrence of potentially dangerous MB traffic conditions. @en

Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges.

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Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.

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The aim of this paper is to review existing publicly available and open artificial intelligence (AI) oriented datasets in different domains and subdomains of the railway sector. The contribution of this paper is an overview of AI-oriented railway data published under Creative Commons (CC) or any other copyright type that entails public availability and freedom of use. These data are of great value for open research and publications related to the application of AI in the railway sector. This paper includes insights on the public railway data: we distinguish different subdomains, including maintenance and inspection, traffic planning and management, safety and security and type of data including numerical, string, image and other. The datasets reviewed cover the last three decades, from January 1990 to January 2021. The study revealed that the number of open datasets is very small in comparison with the available literature related to AI applications in the railway industry. Another shortcoming is the lack of documentation and metadata on public datasets, including information related to missing data, collection schemes and other limitations. This study also presents quantitative data, such as the number of available open datasets divided by railway application, type of data and year of publication. This review also reveals that there are openly available APIs—maintained by government organizations and train operating companies (TOCs)—that can be of great use for data harvesting and can facilitate the creation of large public datasets. These data are usually well-curated real-time data that can greatly contribute to the accuracy of AI models. Furthermore, we conclude that the extension of AI applications in the railway sector merits a centralized hub for publicly available datasets and open APIs.@en