Railway transportation provides sustainable, fast and safe transport. Its attractiveness is linked to a broad concept of service reliability: the capability to adhere to a timetable in the presence of delays perturbing traffic. To counter these phenomena, real-time rescheduling can be used, changing train orders and times, according to rules of thumb, or mathematical optimization models, minimizing delays or maximizing punctuality. In the literature, different indices of robustness, reliability and resilience are defined for railway traffic. We review and evaluate these indices applied to railway traffic control, comparing optimal rescheduling approaches such as Open Loop and Closed Loop control, to a typical First-Come-First-Served dispatching rule, and following the timetable (no-action). This experimental analysis clarifies the benefits of automated traffic control for infrastructure managers, railway operators and passengers. The timetable order, normally used in assessing a-priori reliability, systematically overestimates unreliability of operations that can be reduced by real-time control.@en

Railway traffic management is responsible for the detection and resolution of conflicts in case of disturbed operations. To minimise delay propagation, rescheduling decisions are taken by human dispatchers, possibly supported by mathematical models. Existing conflict detection and resolution (CDR) models mostly refer to conventional fixed-block multi-aspect signalling systems, in which minimum train headways are determined based on a preset number of blocks considering worst-case braking distances and number of signal aspects. In moving-block signalling systems, minimum headways are based on absolute braking distances. This paper reviews literature on CDR with the aim to identify gaps and to propose next steps in the research on CDR under moving-block signalling. A research agenda presents various modelling options, for which modelling approaches are proposed based on a comparative analysis.@en

A wide literature is available on models and tools for the optimal real-time management of railway traffic, but the knowledge of their effects on real operations is still blurry and very limited due to the scarce implementation of these systems in practice. This paper analyses how these tools perform when interfaced in a closed-loop setup with a realistic traffic environment. A framework is developed that couples the rescheduling tool ROMA with the microscopic simulation model EGTRAIN. Railway traffic is managed for different perturbed scenarios using a rolling horizon scheme where optimal plans are periodically computed based on current traffic information and implemented in the simulation model. The closed-loop setup is investigated for different combinations of its parameters relatively to quality and stability of rescheduling plans. A comparison is performed against a typical open-loop approach that implements only the plan computed on the basis of expected train entrance delays. Both the closed-loop and the open-loop approaches are evaluated against the case in which no rescheduling is considered and trains keep on following the original timetable. Results obtained for the Dutch corridor Utrecht-Den Bosch show that the closed-loop always outperforms the open-loop in terms. @en

The ever-increasing need for railway capacity has led infrastructure managers to explore next generation signaling systems to drastically reduce train separation by overcoming traditional fixed-block railway operations. Technologies like ETCS Level 3 are under development to bring the railways to a configuration without track-side signaling where trains move at an absolute braking distance from each other. The railway industry is however looking into the alternative concept of Virtual Coupling which separates trains by a relative braking distance as for cars on the road. By means of a V2V communication architecture trains could move synchronously in platoons which could be treated as a single convoy at junctions, so to improve capacity. On the other hand, the concept might introduce additional safety risks, especially at diverging junctions where points need to be switched and locked in between trains of a platoon. There is the need to understand whether capacity benefits provided by Virtual Coupling are sufficient to motivate the railway industry to invest in it, despite the unclear safety implications. This paper addresses such a need by assessing impacts of Virtual Coupling on railway capacity and potential benefits with respect to ETCS Level 3. For the first time in literature, operational principles and capacity occupation models have been developed and simulated to describe train operations under Virtual Coupling. An application to a case study in the UK shows the advantages that such a concept provides in terms of capacity utilization, as well as space and time train headways.@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

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

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

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

This deliverable contains the output of the activities performed for Task 4.3 “Guidelines on integrated traffic management architectures for safe and optimised moving-block operations” of the EC Shift2Rail PERFORMINGRAIL project. A real-time model for Moving Block traffic conflict detection and resolution is mathematically specified based on the formulation introduced in Deliverable D4.1. A mathematical formulation of the RECIFE-MILP rescheduling tool extended for Moving Block rail operations is reported together with a detailed description of the objective function and constraints. The proposed MB traffic conflict detection and resolution model is then verified for a given network layout and compared to the original RECIFE-MILP formulation for fixed-block to assess modelling and performance impacts of introduced MB constraints. A validation has successively investigated the applicability and effectiveness of the proposed MB traffic management algorithm by testing it on two real railway networks in France (Gonesse junction and a portion on the Paris-Le Havre line) for different traffic scenarios. The validation experiment shows that with respect to fixed-block, Moving block can reduce delay propagation especially: i) for junctions where trains are not stopping, ii) in denser peak-hour traffic and iii) when train rerouting decisions are taken. A detailed description is the provided for the early-warning MB hazard prediction modules introduced in PERFORMINGRAIL Deliverable D4.1. A specification of input/output data and main functionalities is reported together with practical examples illustrating the usefulness of those modules in mitigating potential MB safety risks in the short and medium term. A functional TMS architecture is then defined for a safe and optimised real-time management of MB traffic operations. Guidelines are outlined for the different functional TMS modules, specifying input/output data, main functionalities and interactions with other components within and without the TMS. A set of recommendations is eventually provided to support both science and the industry in further development and implementation of functional modules and an advanced TMS architecture for MB rail traffic. Recommendations particularly refer to novel modules introduced in PERFORMINGRAIL, namely the MB conflict detection and resolution and the early-warning MB hazard prediction models. In addition, indications are given for further development and practical implementation of the proposed functional TMS architecture for safe and optimised MB railway operations. The main highlighted points regard the need of interfacing current Traffic Monitoring systems with satellite-based train location systems as the removal of track-side train detection will compromise the use of existing train describers. That also leads to then necessity of the TMS architecture to include proposed modules for early-warning hazard prediction to mitigate safety risks which can arise for MB in locations with compromised GNSS or GSM-R signal availability. Another essential recommendation refers to data interface standardisation to enable seamless communication among functional modules within the TMS and with external supporting systems.@en

This deliverable contains the output of the activities performed for Task 4.3 “Guidelines on integrated traffic management architectures for safe and optimised moving-block operations” of the EC Shift2Rail PERFORMINGRAIL project. A real-time model for Moving Block traffic conflict detection and resolution is mathematically specified based on the formulation introduced in Deliverable D4.1. A mathematical formulation of the RECIFE-MILP rescheduling tool extended for Moving Block rail operations is reported together with a detailed description of the objective function and constraints. The proposed MB traffic conflict detection and resolution model is then verified for a given network layout and compared to the original RECIFE-MILP formulation for fixed-block to assess modelling and performance impacts of introduced MB constraints. A validation has successively investigated the applicability and effectiveness of the proposed MB traffic management algorithm by testing it on two real railway networks in France (Gonesse junction and a portion on the Paris-Le Havre line) for different traffic scenarios. The validation experiment shows that with respect to fixed-block, Moving block can reduce delay propagation especially: i) for junctions where trains are not stopping, ii) in denser peak-hour traffic and iii) when train rerouting decisions are taken. A detailed description is the provided for the early-warning MB hazard prediction modules introduced in PERFORMINGRAIL Deliverable D4.1. A specification of input/output data and main functionalities is reported together with practical examples illustrating the usefulness of those modules in mitigating potential MB safety risks in the short and medium term. A functional TMS architecture is then defined for a safe and optimised real-time management of MB traffic operations. Guidelines are outlined for the different functional TMS modules, specifying input/output data, main functionalities and interactions with other components within and without the TMS. A set of recommendations is eventually provided to support both science and the industry in further development and implementation of functional modules and an advanced TMS architecture for MB rail traffic. Recommendations particularly refer to novel modules introduced in PERFORMINGRAIL, namely the MB conflict detection and resolution and the early-warning MB hazard prediction models. In addition, indications are given for further development and practical implementation of the proposed functional TMS architecture for safe and optimised MB railway operations. The main highlighted points regard the need of interfacing current Traffic Monitoring systems with satellite-based train location systems as the removal of track-side train detection will compromise the use of existing train describers. That also leads to then necessity of the TMS architecture to include proposed modules for early-warning hazard prediction to mitigate safety risks which can arise for MB in locations with compromised GNSS or GSM-R signal availability. Another essential recommendation refers to data interface standardisation to enable seamless communication among functional modules within the TMS and with external supporting systems.@en