In response to the escalating demand for rail transport, the concept of Virtual Coupling (VC) train operations is progressively gaining ground within the railway sector. The concept of VC aims at reducing safe train separation to less than the absolute braking distance by letting trains move synchronously in radio-connected convoys. One of the major concerns associated with VC is ensuring safe train separation considering realistic risk factors, such as heterogeneous train braking performances and varying track conditions. To address such a safe train separation problem under VC, this paper proposes a novel train control model based on the Artificial Potential Field (APF) method to safely supervise the complete braking process of trains moving in a VC convoy. The proposed model uses a homogeneous strip representation of train length and a Dynamic Safety Margin (DSM) to take into account accurate train dynamics as well as potential risk factors, due to different train acceleration/braking rates, communication delays, unexpected emergency train braking applications, and position measurement errors. The method has been applied to the case of a high-speed line in China. Results show that the APF-based control method can effectively adapt to real-time variations in train dynamics and the operational environment to safely supervise the complete train braking process and avoid collisions even in the case of unplanned emergency braking applications. The proposed APF-based approach shows promising real-time performance which can further contribute to advancing the state of the art on safe train control under VC signalling.

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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

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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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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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 this chapter, applications of artificial intelligence (AI) in railway traffic planning and management (RTPM) are discussed. To begin, a definition of AI is offered with a particular emphasis on its relationship with RTPM. This is followed by a systematic literature review of the state-of-the-art of AI in RTPM covering strategic, tactical, and operational challenges. Next, a transferability analysis is conducted of AI approaches for traffic planning and management from related sectors to railways, specifically from aviation and road transport. The results show that the majority of AI research in RTPM is still in its infancy. Several future research areas that are important to academic and professional communities in AI and RTPM are identified based on reviews and analysis of transferability.@en

Railway, as one of the most energy-efficient transport, plays an essential role in improving the world’s energy and environmental sustainability. Statistics about rail share of transport activities and the corresponding energy consumption will demonstrate the energy efficiency of railway and indicate the potential of developing railway transport. Therefore, this chapter will provide an overview of the railway's energy consumption and traffic volume shares. Statistics presented in this chapter show that railway energy consumption decreased in these decades while its transport volume kept stable, and the traffic volume share of the railway is extremely large in urban transport. To achieve the goal of carbon neutralization, the European Union and many countries have conducted research projects on railway energy conservation. The technologies developed in these projects include energy-efficient train driving, integrated timetabling, using regenerative braking energy, etc. A summary of these technologies is also given, along with their potential energy savings, which range from 1 to 25%. This book will analyse and illustrate the whole systems processes of train operation with optimisation solutions. The structure of the following chapters will be presented at the end.

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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

Disruptions occur frequently in railway networks, requiring timetable adjustments, while causing serious delays and cancellations. However, little is known about the performance dynamics during disruptions nor the extent to which the resilience curve applies in practice. This paper presents a data-driven quantification approach for an ex-post assessment of the resilience of railway networks. Using historical traffic realization data in the Netherlands, resilience curves are reconstructed using a new composite indicator, and quantified for a large set of single disruptions. The values of the resilience metrics are compared across disruptions of different causes using Welch's ANOVA and the Games-Howell test. Additionally, representative resilience curves for each disruption cause are determined. Results show a significant heterogeneity in the shape of the resilience curves, even within disruptions of the same cause. The proposed approach represents a useful decision support tool for practitioners to assess disruptions dynamics and propose best measures to improve resilience.

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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

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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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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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

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

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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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

Hydrogen fuel cell multiple unit vehicles are acquiring a central role in the transition process towards carbon neutral trains operation in non-electrified regional railway networks. In addition to their primary role as a transport mean, these vehicles offer significant potential for applications in innovative concepts such as smart grids. Compared to the pure electric propulsion systems, fuel cell technology allows for cogeneration processes by recovering generated heat in addition to the provision of the electrical power. This paper presents the analysis of fuel cell hybrid-electric multiple unit vehicle employed in regional railway transport during regular service, and in vehicle-to-grid application during the off-service hours, providing the electrical and thermal energy for stationary consumers in terminal stations. The system dynamics are modelled using a backward-looking quasi-static simulation approach, with implemented real-time optimization-based control strategy for managing the power flows between different components. In a case study of selected vehicle and railway services in the Netherlands, the fuel cell system showed average hydrogen consumption of 0.4 kg/km, with the overall electrical efficiency of 38.89%. In vehicle-to-grid scenario, the system satisfied complete stationary power demand, and provided about 327 kWh of thermal energy during 2-h operation, reaching the overall cogeneration efficiency of 66.81%.

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Hydrogen fuel cell multiple unit vehicles are acquiring a central role in the transition process towards carbon neutral trains operation in non-electrified regional railway networks. In addition to their primary role as a transport mean, these vehicles offer significant potential for applications in innovative concepts such as smart grids. Compared to the pure electric propulsion systems, fuel cell technology allows for cogeneration processes by recovering generated heat in addition to the provision of the electrical power. This paper presents the analysis of fuel cell hybrid-electric multiple unit vehicle employed in regional railway transport during regular service, and in vehicle-to-grid application during the off-service hours, where it provides the electrical and thermal energy for stationary consumers in terminal stations. The system dynamics are modelled using a backward-looking quasi-static simulation approach, with implemented real-time optimization-based control strategy for managing the power flows between different components. In a case study of selected vehicle and railway services in the Netherlands, the fuel cell system showed average hydrogen consumption of 0.4 kg/km, with the overall electrical efficiency of 38.89%. In vehicle-to-grid scenario, the system satisfied complete stationary power demand, and provided about 327 kWh of thermal energy during two-hours operation, reaching overall cogeneration efficiency of 66.81%.@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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