Impact Assessment of Train-Centric Rail Signalling Technologies

Research output: ThesisDissertation (TU Delft)

11 Downloads (Pure)


As the deployment of new railway technologies requires official approval from local authorities and governmental agencies, a well-specified strategy can foster investment decisions for technological developments and the overall system migration process. Therefore, it is crucial to guarantee that the proposed railway technologies can enhance operational efficiency and ensure safety to passengers and freight transport. Next-generation train-centric signalling systems can provide substantial capacity benefits to railway undertakings. Moving Block (MB) or the European Rail Traffic Management System / European Train Control System Level 3 (ERTMS/ETCS L3) is a radio-based system without any trackside equipment. A Radio Block Centre (RBC) receives positions of each train continuously and computes a Movement Authority (MA) to each of them. In this signalling system, the track is not partitioned into fixed blocks as is the case in conventional railways but the trains operate under “moving blocks” with a safe distance in front determined by the absolute braking distances. As there is no available trackside equipment, it is vital that trains guarantee their integrity by means of a Train Integrity Monitoring (TIM) system. Virtual Coupling (VC) is one of the most advanced train-centric signalling concepts that drastically reduces train headways and allows trains to move synchronously together in platoons using Vehicle-to-Vehicle (V2V) communication. However, several uncertainties arise in the safety validation and feasibility (from the technical, financial and regulatory perspectives) of the VC technology, particularly when compared to MB.

This thesis aims at developing methodological frameworks to support science and the industry in analysing, assessing and developing new complex systems and next-generation rail technologies. The proposed frameworks use interdisciplinary approaches to address complex decision-making processes such as market potential analysis, impact assessment and roadmapping. In addition, a novel methodological framework is proposed to evaluate the safety and performance of technologies and complex systems.

We first investigate the market potentials and operational scenarios of VC for different segments of the railway market: high-speed, mainline, regional, urban, and freight trains. The research builds on the Delphi method, with an extensive survey to collect expert opinions about benefits and challenges of VC as well as stated travel preferences in futuristic VC applications. Survey outcomes show that VC train operations can be very attractive to customers of the high-speed, mainline, and regional market segments, with benefits that are especially relevant for freight railways. In particular, customers of regional and freight railways are observed to be unsatisfied with current train services and willing to pay higher fares to avail of a more frequent and flexible service enabled by VC. Operational scenarios for VC are then defined by setting market-attractive service headways and defining characteristics of the rolling stock, infrastructure, and traffic management. A SWOT analysis of strengths and weaknesses of this concept together with business opportunities and threats is carried out. The defined VC future scenario is set to induce a sustainable shift of customers from other travel modes to the railways.

Second, we examine the overall impact of next-generation train-centric signalling systems to identify development strategies to face the forecasted railway demand growth. To this aim, an innovative Multi-Criteria Analysis (MCA) framework is introduced to analyse and compare VC and MB in terms of relevant criteria including quantitative (e.g., costs, capacity, stability, energy) and qualitative ones (e.g., safety, regulatory approval). We use a hybrid Delphi-Analytic Hierarchic Process (Delphi-AHP) technique to objectively select, combine and weight the different criteria to more reliable MCA outcomes. The analysis has been performed for different rail market segments including high-speed, mainline, regional, urban and freight corridors. The results show that there is a highly different technological maturity level between MB and VC given the larger number of vital issues not yet solved for VC. The MCA also indicates that VC could outperform MB for all market segments if it reaches a comparable maturity and safety level. The provided analysis can effectively support the railway industry in strategic investment planning of VC.

Third, developments in the railway industry are continuously evolving and long-term transition strategies can enable an efficient implementation of signalling technologies that provide a significant increase in network capacity and operation efficiency. VC advances MB signalling by further reducing train separation to less than an absolute braking distance using V2V communication and cooperative train control within a Virtually Coupled Train Set (VCTS). This chapter proposes a method to develop scenario-based roadmaps based on the SWOT and hybrid Delphi-AHP MCA. Step-changes are identified and initially assessed in a Swimlane based on priorities and time order collected from stakeholders through a survey and further developed in a workshop. Optimistic and pessimistic scenarios are assessed regarding various factors and timelines. The step-changes are then enriched with the optimistic and pessimistic scenarios, and associated durations are estimated for each of the step-changes, which finally result into scenario-based roadmaps that can be used as an efficient tool for stakeholders to identify and solve potential criticalities/risks to the deployment of VC as well as to setup investment and development plans. The approach is applied to deliver implementation roadmaps of VC for different market segments with particular focus on mainline railways.

Fourth, although MB and VC rail signalling will change the current train operation paradigm by migrating vital equipment from trackside to onboard to reduce train separation and maintenance costs, their actual deployment is constrained by the need for methods to identify configurations which can effectively guarantee safe train movements even under degraded operational conditions. In this thesis, 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). 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 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 Report (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 the 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.

In summary, this thesis provides multiple scientific contributions to train-centric rail signalling technologies by developing several methodological frameworks to support decision-making towards the development of complex railway systems. With a rapid growth of the railway demand, this thesis serves as a guidance for practitioners to develop more advanced transportation systems while ensuring an improved evaluation of safety and performance.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
  • Goverde, R.M.P., Supervisor
  • Quaglietta, E., Advisor
Award date6 Oct 2023
Electronic ISBNs978-90-5584-333-6
Publication statusPublished - 2023


  • Railway signalling
  • Virtual Coupling
  • Moving Block
  • Impact assessment
  • Roadmapping
  • Modelling

Cite this