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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In case of disturbed railway operations, traffic management can apply rescheduling measures to resolve conflicts while minimising delay propagation. This can be optimised by conflict detection and resolution (CDR) models. Usually based on alternative graph or mixed integer linear programming (MILP) formulations, existing models mostly refer to conventional signalling systems in which the track is discretised into blocks. Only at block entries, trains can receive a brake indication. Hence, train separation distances are based on a number of blocks. With the implementation of continuous braking curve supervision in distance-to-go (DTG) signalling systems such as the European Train Control System (ETCS), train separation is based on absolute braking distances. Consequently, an explicit relation between speed and train separation is required in CDR models for DTG signalling. Recently, we enhanced the CDR model RECIFE-MILP to DTG operations. The enhancements relate to track discretisation and speed-dependent train separation. In this research, we investigate the effect of track discretisation granularity on the performance of the enhanced model for next-generation DTG signalling systems: ETCS Level 3 Fixed Virtual Block and Moving Block. The performance is assessed regarding total delay and rescheduling decisions. The results indicate that, depending on the track and traffic scenario, a finer granularity can lead to different rescheduling decisions due to shorter train separation.@en

To support railway traffic management in taking optimised rescheduling decisions in case of disturbances, conflict detection and resolution models are being developed. The existing models mostly refer to conventional fixed-block signalling systems. In these fixed-block systems, minimum train separation distances are determined based on a preset number of blocks considering worst-case braking distances. In fixed-block distance-to-go signalling systems, such as the European Train Control System (ETCS), minimum train separation is based on absolute braking distances. Hence, a different dependency on train speed is required in conflict detection and resolution models for distance-to-go signalling than in models for conventional fixed-block signalling. In this paper, we propose enhancements for existing conflict detection and resolution models to describe distance-to-go operations. The enhancements include the introduction of speed profile options and the redefinition of train blocking times. As a proof of concept, we apply these enhancements to the state-of-the-art rescheduling model RECIFE-MILP, originally developed for conventional fixed-block signalling. The enhanced model is verified and compared with the original model in terms of delay and rescheduling decisions in two case studies of a complex junction and a corridor. The results indicate that the distance-to-go model can propose different rescheduling decisions than the conventional fixed-block one, exploiting distance-to-go operations for a better delay recovery.@en

Train passenger demand fluctuates throughout the day. In order to let train services, such as the line plan and timetable, match this fluctuating demand, insights are needed into how the demand is changing and for which periods the demand is relatively stable. Hierarchical clustering on both regular and normalized origin–destination (OD) data is used to determine for each workday continuous time-of-day periods in which the passenger demand is homogeneous. The periods found for each workday are subsequently used as input in a clustering algorithm to look for similarities and differences between workdays. The methods for finding homogeneous periods during the day and week are applied to a case study covering a large part of the railway network in the Netherlands. We find large differences between the periods based on regular OD matrices and those based on normalized OD matrices. The periods based on regular OD matrices are more compact in terms of passenger volumes and average kms travelled and therefore more suitable to use as input for designing a service plan. Comparison of different workdays shows that mainly the peak periods on Friday are far away from Monday to Thursday, and hence could benefit from an altered service plan.

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This paper presents a method for estimating Well-to-Wheel (WTW) energy use and greenhouse gas (GHG) emissions attributed to the advanced railway propulsion systems implemented in conjunction with different energy carriers and their production pathways. The analysis encompasses diesel-electric multiple unit vehicles converted to their hybrid-electric, plug-in hybrid-electric, fuel cell hybrid-electric or battery-electric counterparts, combined with biodiesel or hydrotreated vegetable oil (HVO) as the first and second generation biofuels, liquefied natural gas (LNG), hydrogen and/or electricity. The method is demonstrated using non-electrified regional railway network with heterogeneous vehicle fleet in the Netherlands as a case. Battery-electric system utilizing green electricity is identified as the only configuration leading to emission-free transport while offering the highest energy use reduction by 65–71% compared to the current diesel-powered hybrid-electric system. When using grey electricity based on the EU2030 production mix, these savings are reduced to about 27–39% in WTW energy use and around 68–73% in WTW GHG emissions. Significant reductions in overall energy use and emissions are obtained for the plug-in hybrid-electric concept when combining diesel, LNG, or waste cooking oil-based HVO with electricity. The remaining configurations that reduce energy use and GHG emissions are hybrid-electric systems running on LNG or HVO from waste cooking oil. The latter led to approximately 88% lower WTW emissions than the baseline for each vehicle type. When produced from natural gas or EU2030-mix-based electrolysis, hydrogen negatively affected both aspects, irrespective of the prime mover technology. However, when produced via green electricity, it offers a GHG reduction of approximately 90% for hybrid-electric and fuel cell hybrid-electric configurations, with a further reduction of up to 92–93% if combined with green electricity in plug-in hybrid-electric systems. The results indicate that HVO from waste cooking oil could be an effective and instantly implementable transition solution towards carbon–neutral regional trains, allowing for a smooth transition and development of supporting infrastructure required for more energy-efficient and environment-friendly technologies.

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For effective railway operations, real-time railway traffic management is crucial. In case of disturbances, traffic management can apply rescheduling and rerouting measures to resolve conflicts while minimising the propagation of delay. To support human dispatchers in taking optimised decisions, conflict detection and resolution (CDR) models are developed. Predominantly based on alternative graphs or mixed integer linear programming (MILP), the existing models mostly refer to conventional signalling systems in which the track is discretised into fixed blocks. Only at block entries, trains can receive a brake indication. Hence, train separation is based on a number of blocks. With the implementation of continuous braking curve supervision in distance-to-go (DTG) signalling systems such as the European Train Control System (ETCS), train separation is based on absolute braking distances. Consequently, an explicit relation between speed and train separation is required in CDR models for DTG signalling. Recently, we proposed enhancements for existing CDR models
to describe DTG operations. The enhancements relate to track discretisation, speed profile options and train separation. We applied these enhancements to RECIFE-MILP, resulting in a tailored CDR model for DTG signalling. In this research, we consider the model for the more advanced DTG signalling systems of ETCS Level 3: Fixed Virtual Block and Moving Block. In these train-centric signalling systems, train position and integrity are monitored onboard
– as opposed to conventional trackside train detection. We update the DTG model accordingly, and we investigate the impact of track discretisation granularity on the model performance. The impact is assessed in terms of delay recovery and rescheduling decisions. The results indicate that, depending on the track and traffic scenario, a finer granularity can lead to different rescheduling and rerouting decisions due to shorter train separation.@en

Supervision, data analysis and communication algorithms monitor trains, exploiting most of their available computational power. On-board eco-driving algorithms such as Driver Advisory Systems (DAS) are no exception, as the computational power available limits their complexity and features. This was the case of Roltijd, the in-house developed DAS based on coasting advice of NS, the main Dutch passenger railway undertaking. This platform calculated the coasting curves at every second by integrating the equations of motion numerically, assuming that the track is flat. However, generating more complex driving advice required replacing this coasting curve calculation by a more computationally-efficient algorithm. In this article we propose a new coasting advice algorithm based on the analytical solutions of the train motion model, assuming that gradients and speed limits are piecewise constant functions of the train location. We analyse the qualitative properties of these solutions using bifurcation theory, showing that bifurcations arise depending on the value of the gradient and the applied tractive effort. We validate the proposed algorithm, finding that our algorithm is accurate and can be 15 times faster than the previous method. This allowed NS to implement our algorithm on their trains, contributing daily to the sustainable mobility of 1.3 million passengers.

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This chapter presents applications, challenges, and opportunities for the integration of artificial intelligence in rail transport, based on the current results of the European project Roadmaps for AI integration in the rail sector (RAILS). Past and ongoing research directions are briefly outlined, and then the regulatory landscape is presented as well as the main barriers to overcome. Some technical aspects are addressed to provide some valuable references, and a high-level description of ongoing research work is given, spanning from innovative studies on smart maintenance, collision avoidance, delay prediction, and incident attribution analysis to visionary scenarios such as intelligent control and virtual coupling.@en

The periodic train timetable is one of the most widely used train timetables in passenger railway systems worldwide. However, how to expand well-coordinated daily train services from a single-period timetable pattern is rarely studied. This paper develops a multi-objective mixed integer programming model to solve the periodic train timetable expansion problem, aiming to optimize passenger waiting time and rolling stock operation costs. In our model, the time-varying passenger assignment, train service selection, and rolling stock circulation planning are integrated. Moreover, the rolling stock circulation is considered with a flexible composition mode for efficiency. An ε-constraint method is introduced to explore the Pareto frontier for the multi-objective model and analyze the trade-offs between the service quality and operating cost. The model and analyzing theorem are validated via the real-world case Guangzhou-Shenzhen Intercity Railway transit system.@en

The dynamics of a moving train are usually described by means of a motion model based on Newton's second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict train dynamics accurately if the mentioned train characteristics are carefully calibrated. The model constitutes the core element of a broad variety of railway applications, from timetabling tools to Driver Advisory Systems and Automatic Train Operation. Among the existing train motion model calibration techniques, those that use operational data are of particular interest, as they benefit from on- board recorded data, capturing the train dynamics during operation. In this literature review article we provide an overview of the train motion model calibration techniques that have been published in the scientific literature between January 2000 and December 2021 and either use operational data or can be minimally adapted to use it. To this end, we present a critical overview of the existing train motion model calibration approaches, distinguishing online calibration that analyzes data on- the-go and offline calibration that analyzes historical data batchwise. We propose a research agenda and highlight some potential goals to be tackled in the near future: from devising accurate online calibrators for eco-driving applications to quantitizing the physical sources of parameter variation. Last, we discuss practical recommendations for practitioners and scholars inferred from the current state of the art.

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Conflict detection and resolution models are being developed to support railway traffic management in taking optimised rescheduling decisions in case of disturbances. Existing models mostly concern fixed-block signalling systems, in which minimum train separation distances are determined based on a preset number of blocks representing worst-case braking distances. In a moving-block signalling system, minimum train separation is based on absolute braking distances and hence depends on train speed differently from how fixed-block conflict detection and resolution models. In this paper, we propose a conflict detection and resolution model that approximates moving-block operations. The model enhances the state-of-the-art fixed-block rescheduling model RECIFE-MILP. The enhancements include a reconsideration of the discretisation of the infrastructure, the introduction of a speed profile alternative and a redefinition of the blocking times. We verify the model by comparing the solutions of the moving-block version with the fixed-block version for a specific scenario. The results indicate that the moving-block model can propose different rescheduling decisions than the fixed-block model with a better delay recovery.@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.

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This paper reviews the concept of self-organisation as defi ned in diff erent fi elds and attempts to provide a defi nition of goal-oriented self-organization that can be applied in the context of railway traffi c. Based on the provided defi nition a modelling approach for self-organising rail traffi c is then proposed to set the basis for future research and exploration of such a concept which could revolutionise the current rail transport to meet long-term capacity and competitiveness goals envisaged by the railway industry.@en

Running time calculation is an essential ingredient in train timetabling. Traditionally, the technical minimum running times are computed in detail after which a running time supplement is added to obtain the scheduled running times. This running time supplement must be translated into lower cruising speeds or coasting regimes to cover the entire scheduled running time for on-time running. How this is done determines the exact time-distance train paths and the energy consumption of the trains. This chapter explains how train trajectory optimization can be used to compute energy-efficient train trajectories between two stops, over multiple stops including the optimal allocation of running time supplements between the stops, and over corridors considering the track occupation of multiple trains. It is argued that microscopic train timetabling based on energy-efficient train trajectories and blocking time theory is required to design robust conflict-free timetables that enable energy-efficient train operation. The theory is illustrated with many examples under realistic conditions, such as varying gradients and speed restrictions.

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A large variety of supervision, data analysis and communication algorithms monitor trains, exploiting most of their available computational power. On-board eco-driving algorithms such as Driver Advisory Systems are no exception, as the computational power available can limit their complexity and features. This was the case of Roltijd, the in-house developed Driver Advisory System based on coasting advice of Nederlandse Spoorwegen (NS), the main Dutch passenger railway undertaking. This platform cal-culated the coasting curves at every second by integrating the equations of motion numerically, assuming that the track is fl at. However, the plans of NS regarding gener-ating more complex driving advice require replacing this coasting curve calculation by a more computationally-effi cient algorithm. In this article we propose a new coasting advice algorithm based on the analytical solutions of the train motion model’s diff er-ential equations that assumes the gradients and speed limits as piecewise constant functions of the train location. We analyze the qualitative properties of these solutions using the theory of dynamical systems, showing that bifurcations arise depending on the value of the gradient and the applied tractive eff ort. We validate the proposed algorithm by comparing its performance and accuracy against the previous method and a train trajectory optimizer based on a pseudospectral method, fi nding that our algorithm is accurate and can be 15 times faster than the previous method. This allows NS to implement the proposed method on the trains running in the Dutch railway network, contributing daily to the sustainable mobility of 1.3 million passengers.@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 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

Railway industry is developing advanced signalling systems like moving block to improve network capacity. In traditional fixed-block systems, safe train separation is determined based on a fixed number of block sections representing worst-case braking distances. In moving-block systems, the train separation is reduced to absolute braking distances. The introduction of moving-block signalling requires a change in operational rules and hence in real-time conflict detection and resolution methods in case of disturbances. Existing conflict detection and resolution models are mainly based on fixed-block signalling and the available models for moving-block signalling do not sufficiently represent the dynamic relation between safe train separation and actual train speeds.
To address this gap, we propose a conflict detection and resolution model that approximates moving-block operations. The model enhances the state-of-the-art fixed-block model RECIFE-MILP. The enhancements include a reconsideration of the discretisation of the infrastructure, the introduction of a speed profile alternative and a redefinition of blocking times. With this, the model is able to include speed-dependent occupation times, train separation based on absolute braking distances and continuous braking curve supervision.
We present the reformulated MILP (mixed integer linear programming) model and apply it to two French case studies: the Gonesse junction and a part of the Paris-Le Havre line. For various one-hour periods, rescheduling strategies and disturbance scenarios, we compare the optimal solutions of the enhanced and the original RECIFE-MILP model in terms of total train delay and rescheduling decisions. The results show that the enhanced model can propose different rescheduling decisions than the original model, with a better delay recovery exploiting the moving-block system.@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

Automatic Train Operation (ATO) is a technology to support or automate train driving for increasing service punctuality, energy efficiency and rail infrastructure capacity. Conflict-free train path planning is crucial to the effective deployment of ATO, which allows ATO-equipped trains to operate according to schedule with different train driving strategies. As different train driving strategies lead to various passing times, current planning practice is inadequate to avoid route conflicts as it only sets target arrival or passing times at stops or major junctions. Therefore, conflict-free train path planning needs the definition of a Train Path Envelope (TPE) that contains time targets or windows defined at discrete locations called timing points to tolerate schedule deviations due to different driving styles. The number and location of the timing points, as well as the associated time targets or windows, is a decision problem. This paper proposes a framework to design a robust set of timing points and their associated time windows in a TPE to enable operational conflict-free train path planning against the driving strategies utilised. This framework relies on a Train Path Slot model which extends the definition of TPE from time windows at a discrete set of locations to an integrated blocking time stairway pattern continuously defined across all locations over a train route. The Train Path Slot model considers three relevant train driving strategies, i.e., energy-efficient driving with or without coasting as well as minimum-time driving considering slight delays. A Linear Programming model is formulated to compute the conflict-free Train Path Slots as constraints for train operation. To meet the optimised Train Path Slots, we analyse several possible sets of timing points in a TPE that are only located at stops or signal positions along the train routes. Those timing point sets are then compared in terms of total Train Path Slot overlap time, capacity, energy efficiency and driving performance indicators. Our research supports infrastructure managers in resolving the imminent problem of timing point determination and TPE computation to reach their capacity goals. At the same time, it allows sufficient driving flexibility for railway undertakings.@en