The railway industry needs to investigate overall impacts of next generation signalling systems such as Moving Block (MB) and Virtual Coupling (VC) 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 (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.@en

The present document constitutes Deliverable D4.2 “Cost-Effectiveness Analysis for Virtual Coupling” in the framework of TD2.8 of IP2 according to the Shift2Rail Multi-Annual Action plan (MAAP). This deliverable introduces a Multi-Criteria Analysis framework for assessing impacts of train-centric signalling in the operational, technological and business domains. Specifically, Virtual Coupling (VC) and Moving Block (MB) signalling are compared in terms of eight key criteria and benchmarked with respect to the current state of practice for the different rail market segments identified by the S2R MAAP (i.e. high-speed, main-line, regional, urban and freight). Quantitative criteria include total costs, infrastructure capacity, system stability, travel demand, and energy consumption. In addition, qualitative criteria include public acceptance, regulatory approval, and safety. Consolidated mathematical techniques and engineering methods have been used to assess each of the quantitative criteria while a Delphi approach has gathered values for the qualitative criteria based on extensive Subject Matter Expert (SME) interviews and workshops. A Multi-Criteria Analysis (MCA) has been setup by implementing a hybrid Delphi-Analytic Hierarchic Process (AHP) technique to weight and combine the different criteria in final performance scores of MB and VC signalling. The adopted Delphi-AHP technique has been proven to enhance collaboration among experts in selecting and weighting the criteria by means of an iterative feedback loop ending when consistent weights of relative criteria importance were achieved. The individual analyses of single criteria show that VC outperforms MB for all market segments in terms of infrastructure capacity, system stability, energy consumption and travel demand. VC enables trains to follow each other at a distance shorter than an absolute braking distance, which can reduce headways significantly, especially if trains can move cooperatively in virtually coupled platoons. This is also reflected in terms of system stability and energy given that the advantage of running at a shorter safe separation while continuously being informed about the speed of adjacent trains improves the capability of mitigating delay propagation and enhancing energy efficiency. An increased modal shift to railways is observed for VC, especially for the regional and freight markets where a more flexible train service would better satisfy customer needs currently poorly addressed on those segments. Deployment of VC will be slightly more expensive than MB mostly due to the need of installing ATO and V2V communication while operational costs for the two systems will be comparable. Issues and priorities identified for regulatory approval and public acceptance were judged by SMEs to be very similar for MB and VC. In terms of safety, VC scores lower than MB given the different technological maturity level and the larger number of vital issues yet to be solved. The SMEs assigned a very high importance weight to the safety criterion, which therefore affects greatly the final result of the MCA. The MCA score is hence in favour of MB for all market segments, despite the better performance of VC forsingle criteria like capacity, stability, energy consumption and travel demand. A fairer comparison can be obtained when assuming the same maturity level of MB and VC in a future point in time. In that case, VC clearly outperforms MB for all market segments and for freight and regional in particular, given that the provided train service flexibility would facilitate larger modal shifts of the customer demand. @en