Open Multimodal Performance and Evaluation Tools - Technical Summary

Open Multimodal Performance and Evaluation Tools Technical Summary November 2025 V01.00 Authors Luis Delgado / UoW Michal Weiszer / UoW Andrew Cook / UoW Tatjana Bolic / UoW Nominative authoring compliance The beneficiaries/consortium confirm(s) the correct application of the Grant Agreement, which includes data protection provisions, and compliance with GDPR or the applicable legal framework with an equivalent level of protection, in the frame of the Action. In particular, beneficiaries/consortium confirm(s) to be up to date with their consent management system. The beneficiaries/consortium has obtained the explicit, freely given, informed and unambiguous consent from the author(s) when indicated nominatively, otherwise, anonymous (using the name of the participating entity) is used in the Authoring and approval tables. Copyright statement © (2025) – MultiModX Consortium. All rights reserved. Licensed to SESAR 3 Joint Undertaking under conditions. Project Acronym: MultiModX Project budget combined amount: 1 750 380 € Duration: 30 months Project start date: 01/07/2023 Project end date: 31/12/2025 Partners: 6 partners from 5 countries Project coordinator: Bauhaus Luftfahrt EV Work programme: HORIZON-SESAR-2022-DES-ER-01 Grant agreement ID: 101114815 The University of Westminster, a UK participant, received funding from UK Research and Innovation under the UK government’s Horizon Europe funding guarantee No 10091678. Abstract This Technical Summary provides an overview of the open Multimodal Performance Framework and Multimodal Evaluation Tools developed under the SESAR 3 Joint Undertaking’s MultiModX project. It complements the open documentation and repositories with more details on the Performance Framework rationale and approach, and on the models inner workings and capabilities for the assessment of planned multimodal networks, replanned networks to consider the impact of disruptions and day of operations execution. Both the Multimodal Performance Framework and the Evaluators take a passenger-centric approach to assess not only the operational aspects (services, load factors, infrastructure usage) but the passengers’ door-to-door journey and multimodal network connectivity. This document is not a manual of use but complements the information on the open repositories. Executive Summary A Multimodal Performance Framework and a set of Multimodal Evaluation tools have been developed as part of MultiModX SESAR-Horizon Exploratory Research (ER) Project. The Multimodal Performance Framework builds on previous multimodal research projects and extends the work with collaboration with other SESAR Exploratory Research and Industrial Research initiatives. The modelling and evaluation tools are divided into three distinct capabilities: • The strategic multimodal evaluation of air-rail multimodal networks with a focus on passenger-centric metrics. MultiModX’s Strategic Multimodal Evaluator provides mobility metrics at the region, the infrastructure (airport, rail station), the operator and the passenger level, considering flight schedules, rail timetables, infrastructure characteristics and policies. This enables the evaluation of changes to any of these elements (schedules, infrastructure and policies) accounting for passengers’ preference over alternative itineraries. • The evaluation of replanning of operations under disruption. Building on some of the functionalities of the Strategic Multimodal Evaluator, the impact of replanning operations (e.g. delays, cancellations, adding services) on planned passenger itineraries can be evaluated. This evaluation provides passenger-centric metrics on the impact of the disruption considering different levels of rebooking flexibility. • The assessment of the execution of the operations under uncertainty and disturbances. MultiModX’s Tactical Multimodal Evaluator can simulate the operations as they unfold on the day of execution. This model tracks vehicles (flights and trains) and passengers, providing passengercentric metrics, e.g., missed connections and total delays. The Tactical Multimodal Evaluator can, therefore, assess the robustness of planned (and replanned networks) and how tactical mechanisms (e.g. fast-track at airports for multimodal passengers) could support the multimodal connectivity of passengers. This document provides a technical overview of the opensource Multimodal Performance Framework and the evaluation tools developed under MultiModX’s Solution 1 /SESAR’s Solution 399 (Multimodal Performance Evaluation). The Multimodal Performance Framework at https://nommon.atlassian.net/ wiki/external/MzA2ZTJmMjU5M DUyNDNlYzlkNDBmNTMwOTRl MDY4MGY The open-source models are available at https://github.com/UoW-ATM/ 3 3 5 Table of contents 6 Introduction 9 Multimodal Performance Framework 11 Strategic evaluation of networks 19 Evaluation of replanned networks (under disruptions ) 26 Tactical evaluation of networks 31 Conclusions 32 References 34 Glossary 35 List of acronyms 36 Appendix A – Performance indicators in digital catalogue List of figures 9 Figure 1. Performance Assessment – Multimodal Performance Framework Concept 11 Figure 2. Performance Assessment – Strategic Planned Network Evaluator Concept 12 Figure 3. Multilayer modelling approach 13 Figure 4. Functional diagram of the Strategic Multimodal Evaluator 18 Figure 5. Access and egress (including from/to multimodal journeys) for Valladolid airport (LEVD) for the three 18 Figure 6. Total mobility time from ES51 (Catalonia) to NUTS 3 regions for PP00 scenario 21 Figure 7. Passengers impacted by closure of Malaga airport (LEMG) across the replanning alternatives 21 Figure 8. Example of itinerary replanned in closure of Malaga airport (LEMG) 22 Figure 9. Example of itinerary replanned in PA05 change of infrastructure nodes with closure of Malaga airport (LEMG) 23 Figure 10. Passengers impacted by industrial action in LEMD 24 Figure 11. Distribution of itineraries status for PA04 and PA05 for industrial action in Madrid (LEMD) 24 Figure 12. Distribution of departure and arrival delays, and travel deviation for PA01 and PA05 for industrial action in Madrid (LEMD) 25 Figure 13. Passengers affected by the disruption and their status for PA01 and PA05 in case of industrial action in Madrid (LEMD) 26 Figure 14. Tactical Multimodal Evaluator input and output 27 Figure 15. Tactical Multimodal Evaluator model – agent architecture 29 Figure 16. Passengers with missed connections with fast-track mechanism at airport as a function of speed-up coefficient 4 4 List of tables 16 Table 1. Policy packages 17 Table 2: Number of passengers with multimodal journeys from/to the airports for the different scenarios. (% Variation with respect baseline) 20 Table 3. Passengers replanning alternatives 22 Table 4. Mean departure, arrival delay and travel time deviation of replanned journeys for Malaga airport closure 28 Table 5. Tactical indicators for the baseline scenario (PP00) in intra-Spain mobility 29 Table 6. Delayed flights and passengers with arrival delay larger or equal to the threshold for baseline (PP00) intra-Spain mobility 37 Table 7. Digital catalogue of indicators 5 1 Introduction The multimodal performance framework and assessment tools described in this document are part of MultiModX project . MultiModX is an Exploratory Research (ER) Project from the Single European Sky & Air Traffic Management Research (SESAR) programme. 1.1 Towards European air-rail multimodality The topics of multimodality, passenger experience and inclusion, and the development of a European seamless mobility system that meets the goals of the Paris Climate Agreement are gaining traction [1] This multimodal vision requires airports to be multimodal nodes of the future, with coordinated planning and collaborative decision-making based on shared information and situational awareness across transport modes [2][3][4]. Railway can help expand airport catchment areas and shift passengers from feeder flights to high-speed rail (HSR) connections, reducing greenhouse gas (GHG) emissions and releasing airport capacity [5][6]. A shift to suitable HSR rail alternatives from air with short-haul bans is being implemented in several European countries, with France at the front [7] and Spain considering a similar policy [8]. Multimodal networks to ensure connectivity would be required for these policies to be impactful. The interactions between different modes of transport are complex as they can involve substitution due to competition, complementarity of services, and/or supporting each other by covering different segments of a door-to-door journey, as a feeder mode for one another. All these interactions need to consider how passengers are using the networks. Therefore, a passenger-centric vision is required considering total door-to-door (D2D) journeys. In this regard, the schedule synchronisation between different transport networks can optimise transfer times, significantly reducing D2D travel times [9], [10] Understanding how these multimodal networks perform on the day of operations and managing disruptions and missed connections is crucial when assessing the resilience and feasibility of multimodal trips [11]. Finaly, large disruptions in any of the networks might require, from the passenger perspective, finding suitable alternatives in the remaining replanned network. In this case, the passenger’s door-to-door journey needs to be considered [12]. Passengers might use suitable alternatives which could involve using different routes, different modes, and even starting and ending their journey at different infrastructure nodes, if the journey is possible and the total door-to-door time is appropriately considered. passenger’s door-to-door journey needs to be considered [12]. Passengers might use suitable alternatives which could involve using different routes, different modes, and even starting and ending their journey at different infrastructure nodes, if the journey is possible and the total door-to-door time is appropriately considered. Overall, there is a need for a better understanding of the impact on mobility (door-to-door) of multimodal mechanisms (e.g. integrated ticketing, optimised schedules), incentivisation (e.g. taxation of CO2 emissions) and enforcement (e.g. flight bans) policies, infrastructure changes (e.g. minimum connecting times), and other operational aspects (e.g. alliances between operators). These need to be evaluated with a common suitable multimodal performance framework and considering the planning (strategic), replanning (disruptions management) and execution (tactical) perspective. 1.2 Tools for evaluating multimodal air-rail networks 1.2.1 Multimodal Performance Framework The Multimodal Performance Framework builds on the work from previous multimodal research projects and extends the work with collaboration with other SESAR ER and IR initiatives, providing a catalogue of performance indicators (PI). The expected evolution of the SESAR Master Plan considers the inclusion of Passenger Experience as a new key performance area (KPA), in need of specific PIs and KPIs. The KPA expands the flight-centric vision of ATM to include the passenger perspective. This would a gradual inclusion of airport and mobility elements. Therefore, a shared multimodal performance framework is needed to define the appropriate multimodal indicators. This should reflect, for the particular case of SESAR, how SESAR KPIs impact multimodality, how multimodality could impact SESAR’s indicators and, and critically, how the SESAR Performance Framework could be extended to include some of these passenger-centric (multimodal) aspects. Besides that, there is a need for a catalogue of more exploratory door-to-door indicators which could be shared among ER projects so that adequate comparisons between mechanisms could be drawn. 1.2.2 Strategic Multimodal Evaluation MultiModX's Strategic Multimodal Evaluator uses a detailed representation of multimodal transport supply considering flight schedules and rail timetables. The model computes possible itineraries between origin and destination regions subject to infrastructure and policy constraints and disaggregates the underlying demand 6 between the regions on passenger flows and eventually itineraries (accounting for passengers’ preferences on total travelling time, cost and emissions, and services capacities). The Strategic Multimodal Evaluator is, therefore, able to consider the joint operations of air and rail services and compute performance indicators at different level (e.g. region, infrastructure, operator) to assess the effectiveness of the planned operations. 1.2.3 Evaluation of replanned networks under disruptions The transport network is subject to different disruptions. From day-to-day minor delays, infrastructure issues and congestion to more severe disruptions blocking elements in the system (e.g. weather which closes a rail link) or even system-wide disruptions (e.g. volcanic eruption which closes several airports). Air and rail operations can be replanned as a reaction to disruptions. These adjustments can include, among others, delays, cancellations, modification of routes (particularly for rail) and addition of new services. These changes can be produced directly by the operators (e.g. airlines deciding to cancel their flights), by a central manager (e.g. issuing air traffic flow management (ATFM) delay by the Network Manager), or from an optimisation process. If the disruptions are known with sufficient look-ahead time, alternative passenger itineraries could be offered. Building on the capabilities of the Strategic Multimodal Evaluator, a replanning of the network can be evaluated. This evaluation assesses the status of passengers’ itineraries after the replanning and finds suitable alternatives under different levels of flexibility for stranded passengers. Once again, passenger-centric performance indicators evaluate the effectiveness of the replanning. 1.2.4 Tactical Multimodal Evaluation During the day of operations the planned networks can be subject to disruptions of some of their elements (e.g. delays in the link between the rail station and the airport) and disturbances (e.g. day-to-day delays in the air system). It is crucial to evaluate how the multimodal networks perform under these conditions to assess, among other things, their robustness. In the context of multimodality it is particularly as important to model flights and rail services (with their delays) as passengers and their full itineraries. Passengers experience delays and disruptions differently from airlines (and flights) [13][14][15] and rail operators, simply due to the fact that their journey often involves multiple connections and/or modes of transport. MultiModX’s Tactical Multimodal Evaluator extends the capabilities of Mercury [14], an open-source flight and passenger mobility model developed over 10 years on several SESAR ER projects, to provide this tactical evaluation of the multimodal networks. 1.3 Document objective and intended audience This document provides a technical summary of the opensource models developed for the multimodal evaluation of air and rail networks. It complements the digital catalogue of multimodal performance indicators and the code of the multimodal evaluators available in GitHub (see Section 1.4) with a more detailed description of the methodology used to develop the performance framework and the inner workings and capabilities of the evaluators. The document showcases some of the capabilities of the different tools developed. Note that this is not a user guide for the tools and the most up to date information on the models, including manuals are available in the open repositories (see Section 1.4). The intended audience are stakeholders with interest in mobility, air and rail performance evaluation (e.g. SESAR JU, SESAR ER and IR projects, EU-Rail), and readers with a technical background who want to have a better understanding of the evaluators aiming at using or extending them. 1.4 Open-source catalogue of indicators and multimodal evaluation models The catalogue of indicators of the multimodal performance framework and the evaluation models are available as open documents and open-source code (under GPL-3.0 license). The Multimodal Performance Framework at https://nommon.atlassian.net/ wiki/external/MzA2ZTJmMjU5M DUyNDNlYzlkNDBmNTMwOTRl MDY4MGY The open-source models are available at https://github.com/UoW-ATM/ 7 1.5 Document structure The rest of the document is structured as follows. Section 2 describes the Multimodal Performance Framework including the digital catalogue of multimodal indicators. Section 3 details the Strategic Multimodal Evaluator for the evaluation of planned multimodal networks. Section 4 describes how the replanned networks under disruptions can be evaluated. The Tactical Multimodal Evaluator is described in Section 5. Conclusions and recommendations are given in Section 6. The document closes with references in Section 7, glossary in Section 8, a list of acronyms in Section 9, and Appendix A – Performance Indicators in Digital Catalogue. 8 2 Multimodal Performance Framework 2.1 Methodology The multimodal performance framework developed as part of MultimodX relies on a literature review of previous work in the field and feedback obtained from stakeholders and other related research projects. As shown in Figure 1, the framework identifies three levels of development of potential indicators: 1. Indicators at Level 1 are indicators currently part of the SESAR3 (S3) Performance Framework. These indicators have, by their nature, a stronger focus on the gate-to-gate (G2G) component of the passenger journey. 2. Indicators at Level 2 comprise indicators that are currently (or are planned) to be at least modelled by research projects. These mature some aspects of passenger experience and focus on multimodal considerations such as reliability. Within this level, the indicators can be categorised as Level 2.1 for indicators that are good candidates to be promoted to Level 1, i.e. included in the next version of the SESAR3 Performance Framework; and as Level 2.2, which contains the rest of the indicators. 3. Level 3 contains more ambitious indicators that aim to capture the total experience of passengers in their door-to-door journey. These represent indicators that can be more desirable but currently not feasible due to several limitations, such as data availability. It is worth noting how the same aspects could be found at different levels, e.g. interoperability is a multimodality exposure (at Level 2) while part of the ambitions for full door-to-door performance at Level 3. The work performed by MultimodX on the further definition of this multimodal performance framework represents an improvement to the EU mobility system as these aspects are currently not monitored and are out-ofscope of the system’s considerations. A workshop on ‘Multimodality and passenger experience in the SESAR Performance Framework’ was held in January 2025. It was attended by relevant stakeholders (e.g. SESAR Joint Undertaking, EUROCONTROL, ACI Europe) and members of ER and IR projects which relate to multimodality and passenger aspects: MultiModX, PEARL [16], AMPLE3 [17], SIGN-AIR [18], MAIA [19] and Travel Wise [20] projects. This workshop supported the validation of the methodology and the framework, extending its scope from pure multimodality to passenger experience, and from focusing solely on multimodal mobility performance to consider the relationship and integration with the SESAR3 Performance Framework. 2.2 Digital catalogue of indicators The digital catalogue of indicators is currently available in Confluence, a wiki-like collaborative site: https://nommon.atlassian.net/wiki/external/MzA2ZTJm MjU5MDUyNDNlYzlkNDBmNTMwOTRlMDY4MGY. A list of PIs in the Digital Catalogue is in Appendix A. These indicators are the basis for the assessment of the multimodal mobility networks. Figure 1 Performance Assessment – Multimodal Performance Framework Concept 9 and reducing multimodal connecting times (30 and 15 minutes for rail-to-air and air-to-rail connections); 2. Additional CO2 taxation of 0.15 EUR/Kg CO2; 3. Flight-ban eliminating flights when a high-speed rail (HSR) alternative of up to 3h is available. 3.4.3 Results for different policy packages 3.4.3.1 Demand satisfied and mode of transport usage In the baseline scenario (PP00), 389K passengers are assigned to services, satisfying 80.9% of the original underlying demand of 481K passengers between NUTS-3 regions in intra-Spain mobility. The average load factor for rail and flights is of 69.8% and 69.5% respectively. Most of the trips are rail itineraries (72.1%), 26.3% are flights, and only 1.6% (6.4K passengers) are multimodal trips. This is to be expected, as limited to intra-Spain mobility. Madrid, Valencia and Barcelona airports are used for most multimodal journeys which tend to start or end in peripheral regions and with a flight to the islands. In the incentivised case (PP10), similar results are observed. The total demand satisfied is equivalent. The volume of multimodal journeys increases slightly but remains at 2.0%. The load factor of rail also increases slightly to 70.1%. The enforced case (PP20) with the flight ban removes 104 flights out of 1,253 flights; mainly affecting flights between Madrid (LEMD) and Malaga (LEMG), Alicante (LEAL), Barcelona (LEBL), Pamplona (LEPP), Seville (LEZL), Valencia (LEVC). In this case, there is a small reduction in air share (23.8%), a slight increment of multimodal journeys (2.1%), but mainly an increase in rail trips (74.1%). This is reflected in a higher load factor for rail (71.3%). In some corridors the capacity might be fully utilised, as in the Barcelona-Madrid case where only 3.9K passengers are assigned in PP20 when close to 6K are assigned in PP00 and PP10. This leads to a slightly smaller total satisfaction of 381K passengers (79.2%). If we focus on region-pairs that in PP00 use any of the 104 flights banned in PP20, we can see that in PP00 between those regions, the mode share was 18.1%, 80.7%, and 1.2% for air, rail and multimodal, respectively, while in PP20 we observe 7.8%, 90.6% and 1.5%. So, passengers are mostly shifted to rail instead of multimodal journeys. In most cases, the multimodal alternative is significantly longer in time (on average 533 minutes instead of 220 and 320 minutes for rail and air, respectively). So, the average travel time between these regions does not change significantly due to the flight ban. However, the total demand served is reduced: from 94.5K passengers in PP00 to 86.6K passengers in PP20. 3.4.3.2 Travel patterns and infrastructure The volume of passengers directly affected by the flight ban (scenario PP20) is low: only 104 flights are removed and, as in this case, only intra-Spain mobility is considered, direct rail alternatives are available. However, some origindestination pairs generate new travel patterns even if they are not directly impacted by the flight ban. Examples of these newly formed paths are observed in the routes between Valencia and Barcelona to Madrid. With the flight ban, the direct flight to Madrid is no longer possible, but an alternative of connecting in Palma de Mallorca (LEPA) or Ibiza (LEIB) to then fly to Madrid (LEMD) appears. Only 5 passengers (0.2% of the assigned demand) are assigned to use the LEVC -- LEPA -- LEMD route (flying from Valencia to Madrid via Mallorca); and 152 passengers (3.9% of the assigned demand) use the Code Policy package Individual policies definition PP00 Reference (no particular policies) • Passenger rights and multimodality: No integrated tickets • Limitation of aviation: N/A • Environmental regulations: N/A PP10 Multimodality incentivised • Passenger rights and multimodality: Fully integrated (respecting alliances) • Limitation of aviation: N/A • Environmental regulations: CO2 tax applied to emissions PP20 Multimodality enforced • Passenger rights and multimodality: Fully integrated (respecting alliances) • Limitation of aviation: Shorthaul ban if rail available between regions served by flights and rail service faster than a given threshold (2h30) • Environmental regulations: CO2 tax applied to emissions Table 1 Policy packages 16 route LEBL -- LEIB -- LEMD, that is, flying from Barcelona to Madrid via Ibiza. In both cases, it is a minority, as the rail option remains the most suitable (96.1% of the passengers between Barcelona and Madrid are assigned to the HSR). However, it is interesting to see this pattern, as overall there is a lack of capacity in the rail system (1.9K fewer passengers are served between Barcelona and Madrid in the PP20 scenario with respect to the baseline (PP00)). The flight-ban policy does not affect some airports, increasing their relevance as connectivity nodes. In particular, the airports of the Balearic Islands increase the number of connecting passengers from 93 to 190 for Palma de Mallorca (LEPA), from 22 to 546 for Ibiza (LEIB) and from 0 to 28 for Menorca (LEMH). Some regional airports also gain relevance as part of multimodal journeys due to the flight ban. When the ban is not present, larger airports, which are also located in cities with better rail links, capture the demand. With the flight ban, alternatives that are less appealing are used, and regional airports can provide connectivity to adjacent regions. Valladolid (LEVD) is an example of this: with the flight ban, the number of multimodal journeys using the infrastructure grows (166.1% higher than in PP00 and a 105.4% increase with respect to PP10), as shown in Table 2. In absolute value, the number of multimodal passengers is still small, but it is worth noting that there are only 2 arrivals and 2 departures for LEVD in the day. This means that of a combined 369 seats arriving at LEVD, 202 (54.7%) continue their journey multimodally, and 104 (28.2% of the flight seating capacity) arrive at the airport as part of a multimodal journey in the PP20 scenario. Table 2 provides the number of multimodal connecting passengers at Madrid airport (LEMD). As shown, when the incentivised policies are in place (PP10), both airports increase the number of multimodal passengers in a similar way: 27.6% increase for LEMD and 29.6% for LEVD. This shows the impact of reducing the multimodal connecting times due to integrated ticketing. In contrast, for Madrid, when the flight ban is implemented, the number of multimodal passengers is reduced with respect to the incentivised case (from 4,330 to 3,907 passengers). This could be expected as international connections are not considered, and flights to main destinations within Spain are banned, making the rail services potential feeders for international destinations. The intra-Spain flows are rerouted to other alternatives. It is expected that with international connections, hubs like Madrid will significantly increase their multimodal journeys as the long-distance flights will be fed by rail alternatives; even if some spillage to other hubs can also occur (as now is the case with regional airports). Figure 5 shows for LEVD the rail stations from which (and to which) multimodal journeys connect in LEVD for the three scenarios. As depicted, when multimodality is incentivised (PP10) and the flight ban is applied (PP20), the number of regions (and passengers) that use LEVD as their local regional connector increases. 10 Airport Scenario Onward multimodal journey From multimodal journey Total Madrid (LEMD) PP00 1735 1658 3393 PP10 2,015 (16.1%) 2,315 (39.6%) 4,330 (27.6%) PP20 1,872 (7.9%) 2,035 (22.7%) 3,907 (15.1%) Valladolid (LEVD) PP00 78 37 115 PP10 95 (21.8%) 54 (45.9%) 149 (29.6%) PP20 202 (159%) 104 (181.1%) 306 (166.1%) Table 2 Number of passengers with multimodal journeys from/to the airports for the different scenarios. (% Variation with respect baseline) 17 Access PP00 Access PP10 Access PP20 Egress PP00 Egress PP10 Egress PP20 Figure 5 Access and egress (including from/to multimodal journeys) for Valladolid airport (LEVD) for three scenarios 3.4.4 Travel time and regions connectivity The final example of results that can be obtained with the Strategic Multimodal Evaluator is the estimation of travel times and the connectivity of the regions. Figure 6 shows the average travel time (door-to-door) from the Catalonia region (North-East Spain) (NUTS-2 ES51) to the rest of the regions in Spain (Canary Islands not shown). This is calculated by averaging the travel time from the four NUTS-3 areas which compose ES51. As Figure 6 Total mobility time from ES51 (Catalonia) to NUTS 3 regions for PP00 scenario shown, it is easy to identify the regions that are part of the HSR line, the connectivity with Madrid (by direct HSR services) and regions well connected by air services such as the Balearic Islands. 3.5 Future evolution of the Strategic Evolution of Networks Future work should focus on consider adding costs and emissions associated with the access and egress phases, as for those journey segments only time are currently considered. For air transport it would be important considering international hub competition for longdistance trips and include in the modelling the possible use of long-distance bus routes. 18 4 Evaluation of replanned networks (under disruptions) 4.1 Scope When disruptions impact the network in a significant manner, the air and rail operators will modify their services (e.g. cancelling flights, delaying services). This replanning of operations can be done reactively (just to adjust to the disruption, e.g. by shifting the schedules by the planned ATFM delay), or proactively, for example, by replanning operations to maximise the supplied demand to fulfil passengers' itineraries. This optimised replanning is the objective of MultiModX’s Disruption Management Solution 3 , the outcome of which is a set of new flight schedules and rail timetables, which modify some of the originally planned operations. The strategic evaluation of network presented in the previous section (Section 3) provides a set of capabilities which enables the assessment of the impact on passengers of replanning operations due to large disruptions. In particular, replanned networks can be evaluated utilising the capabilities of modelling air-rail multimodal networks as multiplexes to being able to compute potential itineraries between given origin and destination, and the possibility to assign passengers to itineraries considering the available capacity of the services. 4.2 Methodology The evaluation of the impact of replanned operations on passengers’ itineraries is performed in the following steps: 1. Network modification 2. Identification of status of passengers’ itineraries 3. Estimation of available capacity 4. Computation of possible itineraries in replanned network 5. Reassigning passengers 6. PIs computation 4.2.1 3 Disruption management solutions can optimize the flight and rail services to account for the disruption, and the passenger flows needs, such as In SOL401/SOL3 of MultiModX Network modifications Firstly, the planned (prior to disruption) network's supply (flight schedules and rail timetables) is modified. When disruptions impact the network in a significant manner, the air and rail operators will modify their services (e.g. cancelling flights, delaying services, rerouting). These modifications are applied on the planned network to create a replanned network. The first step, is therefore to create a new representation of the multimodal mobility network with the updated schedules. 4.2.2 Identification status of passengers’ itineraries The next step is to identify the status of passenger itineraries in the replanned network. Some itineraries might still be feasible (not affected or delayed) while others might need to be reassigned. These are passengers whose service has been cancelled or whose connection is no longer possible due to delay. 4.2.3 Estimation of available capacity The passengers with still feasible trips in the replanned network follow their original itineraries and therefore still utilise the capacity of their planned services. The passengers who cannot perform their planned itineraries in the replanned network are removed from all their original services and the spare capacity is then computed. 4.2.4 Computation of possible itineraries in replanned network For each origin and destination pair for the passengers needing reallocation, possible itineraries are computed in the replanned network using the services which still have some capacity available. Different alternatives are possible for this computation, as a function of the flexibility that is allowed for reallocating passengers: from restrictive conditions when passengers can only us very similar itineraries as originally planned (e.g. using the same operators and path originally considered), to full flexibility where even departing before initially planned is available. These levels of flexibility can be adjusted in the evaluator (i.e., allowing or not the use of different paths, modes, starting and ending infrastructure node and departing earlier than originally planned or not). For simplicity, these alternatives are grouped into five meaningful alternatives shown in Table 3. 19 Passengers replanning alternative Short description PA01 Close to planned PA02 Allow different path PA03 Allow different path and mode swap PA04 Allow different path, mode swap and different operators PA05 Allow different path, mode swap, different operators and leave ‘home’ earlier than initially planned Table 3 Passengers replanning alternatives 4.2.5 Reassigning passengers Once the possible itineraries for each origin-destination which has passengers that need to be reallocated are computed, the next step is to assign passengers to individual possible itineraries, i.e., to the individual services (flights and trains), taking the capacity into account. A lexicographic optimisation is used to perform the reassignment of passengers to potentially suitable itineraries. How this optimisation is performed could be adjusted if desired. The optimisation is performed sequentially, first maximising the total number of passengers reaccommodated, then minimising the arrival time to their final destination, maximising the itineraries following the same path as planned before the disruption, and finally maximising the number of itineraries starting and ending in the same infrastructure. 4.2.6 PIs computation The final step is the computation of performance indicators which reflect the impact of the replanning of the network with a passenger-centric view. Example of indicators that are computed are number of stranded passengers, mismatch between capacity and demand per origin-destination impacted by the replanning (i.e., by the disruption), passengers delay (arrival and departure), etc. 4.3 Capabilities As presented in Section 4.1, any network which has been replanned could be evaluated with the methodology described in Section 4.2. This means that a wide range of disruptions can be assessed, such as: • closure of rail link, by cancelling trains using the affected link, • closure of an airport, by cancelling flights operating in the affected airport, • ATFM regulations at an airport e.g. due to industrial action, which include some flights cancelled while other delayed, • reduction of capacity at an airport, which would imply different cancellation rates. • All these disruptions can be directly evaluated by modelling their impact on the operations (i.e., without needing to optimise the remaining services). 4.4 Examples of capabilities for the evaluation of replanned networks 4.4.1 Closure of LEMG airport In this scenario the closure of Malaga airport (LEMG) in the intra-Spain mobility case is considered. All flights departing and arriving to LEMG are cancelled. Figure 7 shows the status of the disrupted passengers. With PA01 alternative, a total of 9,525 passengers are stranded, but as the passengers need to keep their mode, path and operators, no alternatives are possible (as the airport is closed). Therefore, no passengers are reaccommodated. In PA02, when alternate paths are possible, e.g., a different intermediate airport can be used, 15.7% of disrupted passengersare reassigned. This is because passengers can find alternative routes with the same mode and operator by flying to and from different airports or connecting at a different node. 20 Figure 7 Passengers impacted by closure of Malaga airport (LEMG) across the replanning alternatives Figure 8 Example of itinerary replanned in closure of Malaga airport (LEMG) Figure 8 shows an example of this possibility of finding alternative airports to use. The passengers were originally planning to travel from Malaga (LEMG) to Madrid (LEMD). However, as shown, the NUTS of origin is ES612 (Cádiz); therefore, the alternative found is to travel to Seville airport (LEZL), instead of Malaga, and to fly to Madrid via Almeria (LEAM). The total trip is longer than the initially planned one (connecting itinerary instead of direct flight), but it is possible and the passenger is no longer stranded. Allowing operator change (PA04) increases the number of reaccommodated passengers significantly to 41.7%. LEMD LEAM LEZL LEMG 21 Finally, enabling earlier than originally planned departure (PA05) ensures that 1,179 additional passengers are served (with a total of 54.1% of disrupted passengers reaccommodated). As shown in Table 4, the possibility to change mode represents a larger increment in the total travel time of disrupted passengers, as slower and multi-leg alternatives could be used. Figure 9 shows an interesting example from PA05 where, in the original itinerary, passengers were flying from Malaga airport (LEMG) to Asturias (LEAS). However, the passengers were, in reality, travelling from ES611 (Almeria). Therefore, an alternative route is fly from Alicante (LEAL) to Madrid (LEMD) and connect to Asturias (LEAS). The passenger has a different itinerary and access as the origin airport is changing from Malaga to Alicante and then needs to connect in Madrid. This might mean an earlier than originally planned departure, as allowed by PA05. The example is illustrative of the effect of airport access/egress connectivity. Figure 9 Example of itinerary replanned in PA05 change of infrastructure nodes with closure of Malaga airport (LEMG) 20 Malaga airport (LEMG) closure PA01 PA02 PA03 PA04 PA05 Mean departure delay N/A 185.2 178.2 90.7 -295.2 Mean arrival delay N/A 215.1 189.6 190.7 -141.5 Mean total travel time deviation N/A 29.9 11.4 100.0 153.8 Table 4 Mean departure, arrival delay and travel time deviation of replanned journeys for Malaga airport closure 22 LEMD LEAL LEAS LEMG 4.4.2 ATFM due to industrial action in Madrid Barajas (LEMD) This scenario is illustrative of the replanning of a network for a significant, expected, reduction in capacity due to industrial action. This is modelled by simulating a cancellation for flights departing arriving to Madrid of 7.5% and applying a probability and amount of delay due to ATFM to the remaining flights following an analysis of historic ATFM regulations due to industrial action. A total of 13,953 passengers are affected by the replanning of operations due to the ATFM regulation. As shown in Figure 10, most of them are due to delays on their flights (9,644, or 69.1% of affected passengers). These are passengers who are delayed on arrival at their final destination. Interestingly, 541 passengers (3.9% of affected ones) are impacted by the regulation (some of their flights are delayed) but still arrive on time to their destination. These are most likely connecting passengers whose inbound flight is delayed but they still made the connection (either to another on-time flight or rail service). In PA01, when the same operator, mode and path must be maintained, 1,258 (9.0% of affected passengers) can be rebooked on alternative flights. Enabling the use of alternative paths (PA02) has a small improvement (12.6% of passengers are reassigned), enabling the mode swap but maintaining the operator as originally planned (PA03) only improves a few multimodal passengers who can be allocated just to rail. When changes between modes are allowed for all passengers (PA04) 21.5% of affected passengers are replanned and only 5.5% remain stranded. See, however in Figure 10 how the number of passengers rebooked across modes is still very limited. Finally, if the disturbance is known enough ahead of time, passengers can be reaccommodated into earlier than originally planned services, in this case (PA05), only 376 passengers end up stranded (2.7% of the originally affected ones). As depicted in Figure 10, most of these replannings are achieved by ensuring that air connections are still possible by selecting earlier flights. Figure 11 represents for PA04 and PA05 the status of different itineraries affected. Once again, as the flexibility to re-accommodate passengers increases, the number of stranded passengers decreases. Passengers are rebooked mostly by keeping their initial intended mode, even if there is flexibility to change it (if needed) in PA05. Interestingly in PA04 from the 2,996 rebooked passengers, 2,466 (82.3%) maintain their initial mode and the 530 remaining (17.7%) change mode; however, when the flexibility to depart before originally planned is introduced in PA05, more passengers are rebooked overall (3,392) but only 268 (7.9%) change mode. Departing earlier allows passengers to use an earlier service maintaining their connection without having to change mode. Figure 12 contrasts the delays per type of impact on passengers for PA01 and PA05. As previously discussed, Figure 10 Passengers impacted by industrial action in LEMD 23 Figure 11 Distribution of itineraries status for PA04 and PA05 for industrial action in Madrid (LEMD) Figure 12 Distribution of departure and arrival delays, and travel deviation for PA01 and PA05 for industrial action in Madrid (LEMD) 24 PA04 PA05 PA01 PA05 passengers with cancellations and those with missed connections are the ones with higher variations with respect to their original plan. Passengers with single flights (delayed) kept their originally planned total travel time, and passengers who can keep their connection increase their travelling time but overall present low departing and arrival delays. This Figure showcases once again the difference between flight and passenger-centric metrics that can be estimated. Finally, Figure 13 presents for PA01 and PA05 the number of passengers affected that can make their journey, and those who got stranded (either because there were no alternative options for them or because there was no spare capacity). As shown, in PA01, as the most restrictive replanning, passengers must maintain their operator, mode and path. Therefore, a significant number of passengers are stranded due to a lack of alternatives. In the PA05 case, as passengers can use different operators, modes and even depart earlier than planned (possibly using a previous service that ensures their connection), all affected passengers have potentially an alternative option. The lack of capacity is the only reason that some passengers still remain stranded. 4.5 Future evolution of the evaluation of replanned networks The capabilities presented open the door to the possibility of planning ahead the impact of significant disruptions to assess the effect of these on passenger itineraries and accommodate them accordingly in a preventive manner. Future work could incorporate passenger agency, conduct parametric sensitivity analysis. Figure 13 Passengers affected by the disruption and their status for PA01 and PA05 in case of industrial action in Madrid (LEMD) 25 PA01 PA05 7 References [1] European Commission, 2020. Sustainable and smart mobility strategy: Putting european transport on track for the future. [2] SESAR JU, 2020. Strategic Research and Innovation Agenda – Digital European Sky. Technical Report. [3] ACARE, 2017. Advisory Council for Aviation Research and Innovation in Europe Strategic Research and Innovation Agenda - 2017 update. Technical Report. 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D3.2 Demand and Supply Scenarios and Performance Indicators. Technical Report. [11] Weiszer, M., Delgado, L., Gurtner, G., 2024. Evaluation of passenger connections in air-rail multimodal operations, in: Proceedings of the 14th SESAR Innovation Days. [12] Delgado, L., Bolic, T., Cook, A., Zareian, E., Gregori, E., Paul, A., 2023a. Modelling passengers in air-rail multimodality, in: 11th EUROSIM Congress, Amsterdam, The Netherlands. [13] Montlaur, A. and Delgado, L. (2020). Flight and passenger efficiency-fairness trade-off for ATFM delay assignment. Journal of Air Transport Management, Volume 83, 101758. https://www.sciencedirect.com/science/article/abs/pii/S0969699719303011 [14] Gurtner, G., Delgado, L. and Valput, D. (2021). An agent-based model for air transportation to capture network effects in assessing delay management mechanisms. Transportation Research Part C: Emerging Technologies, Vol. 133, 103358. https://doi.org/10.1016/j.trc.2021.103358 [15] Delgado, L, Gurnter, G., Cook, A., Martin, J., and Cristobal, S. (2020). A multi-layer model for long-term KPI alignment forecast for the air transport system. Journal of Air Transport Management, Volume 89, 101905 https://www.sciencedirect.com/science/article/pii/S0969699720304889?via%3Dihub 32 [16] PEARL – Performance Estimation, Assessment, Reporting and simulation – https://cordis.europa.eu/project/id/101114676 [17] AMPLE3 – SESAR3 ATM Master Planning and Monitoring – https://cordis.europa.eu/project/id/101114738 [18] SIGN-AIR – Implemented Synergies. 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M., Delahaye, D. and Mancel, C., “Simulation optimisation-based decision support system for managing airport security resources,” in EUROSIM 2023, 2023. 29 33 8 Glossary Agent Based Modelling (ABM): A simulation approach where individual agents (e.g. passengers, flights, trains) act according to defined behaviours, allowing the emergence of complex system dynamics. Archetype: A representative model of passengers or regions sharing similar behavioural or structural characteristics used for modelling purposes. Disruption Management Solution (DMM / SOL401): A decision support tool that optimally adjusts air and rail schedules during disruptions, minimising passenger impact and operational costs. Door-to-door journey: The full passenger journey from origin to final destination, including access, main travel, transfers, and egress. Infrastructure node (Nli): A location in the transport network where passengers can access, transfer between, or exit mobility layers (e.g. airports, rail stations). Kerb-to-Gate / Gate-to-Kerb Time (KS / SK): Time required for passengers to move between the infrastructure kerbside and service gate, including processes such as checking, security, or baggage collection. Mobility layer: A mode specific transport network (e.g. air, rail) that can be interconnected to form a multimodal system. Mode swap: A change in the mode of transport (e.g. from air to rail) during a journey or in response to a disruption or policy measure. Multimodality: An integrated approach to transport where different modes (e.g. air, rail, bus) work together to optimise efficiency, sustainability, and passenger experience. Multimodal Performance Framework: A structured set of indicators and methods for assessing the efficiency, resilience, and passengercentricity of multimodal networks. Pareto Equivalent clusters: Groups of itineraries offering similar levels of performance in terms of key indicators (e.g. time, cost, emissions), representing equally efficient alternatives. Passenger-centric approach: A methodology that prioritises passenger needs, experience, and outcomes when designing and manag ing transport systems. Performance indicator (PI): A quantitative measure used to evaluate performance aspects such as travel time, emissions, cost, or connectivity. Schedule Design Solution (SOL400): A tool that jointly optimises air and rail timetables to improve connectivity, reduce waiting times, and maximise network efficiency. Strategic Multimodal Evaluator: A model assessing long term multimodal network performance at pl0anning level, considering schedules, demand, and policy conditions. Tactical Multimodal Evaluator: A fast time stochastic simulation tool that evaluates real time multimodal operations and passenger outcomes under nominal or disrupted conditions. Transfer time (TR): The time required to move between two nodes within or across mobility layers. 34 9 List of acronyms ABM Agent Based Modelling AOC Airline Operations Centre APOC Airport Operations Centre ATFM Air Traffic Flow and Capacity Management CO ₂ Carbon Dioxide DMM Disruption Management Model / Solution (SOL401) ECAC European Civil Aviation Conference ER Exploratory Research (SESAR Programme) GTFS General Transit Feed Specification IR Industrial Research (SESAR Programme) KPI / PI Key Performance Indicator / Performance Indicator KS / SK Kerb-to-Service / Service-to-Kerb time LEMD Madrid – Barajas Airport (ICAO code) LEMG M á laga Airport (ICAO code) LEVD Valladolid Airport (ICAO code) MCT Minimum Connecting Time MND Mobile Network Data NUTS Nomenclature of Territorial Units for Statistics OD Origin – Destination OP Operator SESAR Single European Sky ATM Research SJU SESAR Joint Undertaking SOL1 / SOL399 Performance Assessment Solution SOL2 / SOL400 Schedule Design Solution SOL3 / SOL401 Disruption Management Solution UTC Coordinated Universal Time 35 Appendix A – Performance Indicators in Digital Catalogue This annex provides a table with the PIs in the digital catalogue. Note on the KPAs: All indicators could be considered part of the 'Passenger Experience' KPA. However, as indicated in this table, they can be further disaggregated into different, more specific KPAs. https://nommon.atlassian.net/wiki/extern al/MzA2ZTJmMjU5MDUyNDNlYzlkNDBmN TMwOTRlMDY4MGY 32 36 Table 7 Digital catalogue of indicators KPA PI Id PI short name PI Definition Type Highest Maturity Level Source Subpage - Indicator definition Operational Efficiency (OPS) OPS_PE1 Passengers processing time at infrastructure Minutes taken to do the process from the kerb to the gate at the airport. The same opposite indicator should be considered ‘Gate to kerb’ and ‘Gate to gate’ for connecting passengers. Strategic L2.2 ‘Multimodality and passenger experience in the SESAR Performance Framework’ workshop https://nommon.atlassian.net/wiki/external/Nj kxZGM1NGJkYjBjNDhhYjlkMWIxMGQzNDFkMjJ mMmM OPS_PE2 Total journey time Strategic based on published schedules, actual based on the recorded performance on the day of operations. Strategic L3 MultimodX https://nommon.atlassian.net/wiki/external/O GU0OTk2M2ZjYWQ0NGRlYzk3OGUzM2UzNmJi MDBmZDE OPS_PE3 Passengers time efficiency Best possible journey time (from schedules)/actual time travel (from actual operations) [percentage] Strategic L3 ‘Multimodality and passenger experience in the SESAR Performance Framework’ workshop https://nommon.atlassian.net/wiki/external/M TU2MTcxNzVjOWJiNDlkYjkyZjEyMjYyZWJiMWY xZDQ OPS_PE4 Buffers in itineraries Amount of time used as buffers in connections in itineraries. Strategic L3 MultimodX https://nommon.atlassian.net/wiki/external/ZT RkNTJkN2Q4MzMzNGU4MTg3YTQ1ZmU4NjFkZ WI3MjA OPS_PE5.1 Arrival delay (flights) Arrival delay (at thresholds 15, 30, 45 and 60 min). Tactical L2.1 ‘Multimodality and passenger experience in the SESAR Performance Framework’ workshop https://nommon.atlassian.net/wiki/external/N mNhMzNhZTNiOTkwNDQyMmI0NjI0ZGVhYjU0 YzUxZmY OPS_PE5.2 Total arrival delay at final destination Difference between planned and actual arrival time at final destination. Could be disaggregated (all passengers, passengers who Tactical L3 ‘Multimodality and passenger experience in the SESAR Performance https://nommon.atlassian.net/wiki/external/N DE1MjZlZTdhNzY2NDI0OWEwY2ViMzdiOWQ4 MzAyMzk missed connections, passengers rerouted, etc.). Framework’ workshop OPS_PE6 Cancellations due to ATFM Percentage rate. Tactical L2.1 ‘Multimodality and passenger experience in the SESAR Performance Framework’ workshop https://nommon.atlassian.net/wiki/external/Y mRlOTMxMGRkMTEzNDk2MzgwNzRiMmZiMG FlMzgwODc OPS_PE7 Reactionary delay Minutes of delay of reactionary delay due to ATFM. Tactical L2.1 ‘Multimodality and passenger experience in the SESAR Performance Framework’ workshop https://nommon.atlassian.net/wiki/external/N WI0MmI3MmE2MzQzNGRiYTgxNGVjZTA5NjE1 MThmNzk OPS_PE8.1 Stranded passengers Percent of pax not reaching their destination. Tactical L2.2 MultimodX https://nommon.atlassian.net/wiki/external/Y mVmNTI5YzYwYTIzNDU2MTkyYzRmMGJkZmN mODQ1MTA OPS_PE8.2 Ratio of stranded passengers Ratio of passengers stranded over number of passengers missing their connection. Tactical L3 ‘Multimodality and passenger experience in the SESAR Performance Framework’ workshop https://nommon.atlassian.net/wiki/external/M jMzZGViY2QxMjQ5NGExMGFjMzYzZGNiN2ZlMj M5NDE OPS_PE8.3 Stranded passengers Percentage of passengers not reaching their destination due to replanning. Replanning L3 MultimodX https://nommon.atlassian.net/wiki/external/M jY4YjdiYWIxODViNGRlZWJjMGQ3ZThiZTI3MTU1 MGE OPS_PE9 Missed connections Percent of pax with missed connection (can be disaggregated by type of connection inter/intramode) Tactical L3 ‘Multimodality and passenger experience in the SESAR Performance Framework’ workshop https://nommon.atlassian.net/wiki/external/M DExYzEyYTEyNzM3NDAyYWIzYzk5MmRlNjgxN2 RlMTc Interoperability (INT) INT_PE1 Modal share Share of transport modes in passenger itineraries on a specific origin-destination pair [percentage per mode(s)] Strategic L3 MultimodX https://nommon.atlassian.net/wiki/external/ZT U4ZGRlMTk2MTkxNDgxZmE1MDU4ZWZlOWE4 MjBmYTg INT_PE2 Seamless of travel (time) Journey transition time (between modes and stops) [mins] Strategic L3 MultimodX https://nommon.atlassian.net/wiki/external/N WYyNGE3OWVjNTdlNGY0ZTg5ODc2MmUzNW Q1NGMyZjM INT_PE3 Infrastructure connectivity Volume of passengers that are connecting at a given infrastructure node Strategic L2.2 MultimodX https://nommon.atlassian.net/wiki/external/N GQxM2Y3MDI0NTNmNDUwYzk0YTlhYjZhYTIyZ DU0YWQ Flexibility (FLX) FLX_PE1.1 Resilience alternatives Number of strategic (planned) and tactical (actual) alternatives available for a given origindestination pair. Strategic L3 ‘Multimodality and passenger experience in the SESAR Performance Framework’ workshop https://nommon.atlassian.net/wiki/external/N TBlMzQ5NmQ4ZDFmNDQyMzk1YWRhMzg5OD c5ZjFlZTU FLX_PE1.2 Passenger Resilience replanned Difference between planned and replanned itineraries Replanning L3 MultimodX https://nommon.atlassian.net/wiki/external/N WY5MzExZWM4N2JhNGExNmJiZTg5NDc5MDRl ZTUzMWU FLX_PE2 Diversity of destinations Number and type of destinations which can be reached from an origin Strategic L3 MultimodX https://nommon.atlassian.net/wiki/external/Y mJiNzYyNDJlMDJhNGFjZDk5MzgyNTVhMjE5Yz U2MTI FLX_PE3 Resilience replanned Captures applied changes/adaptations in the replanned schedules: -rerouted train services -diverted flights -replacement services -retimed/delayed services Replanning L3 MultimodX https://nommon.atlassian.net/wiki/external/N TkxMjBiN2ZmM2Y5NDYxNDlmN2Y5Y2RmOWI1 MzhlOTg CostEfficienc y (CEF) CEF_PE1 Direct operating cost per user Operating cost per trip per user [Euros] Strategic L3 MultimodX https://nommon.atlassian.net/wiki/external/M GI2ZmFjMzllZmUxNGFjZGE0ZmI3NWFkOTE1YT RmNWM CEF_PE2 Load factor Number of passengers over number of seats per operator / mode / total Strategic L3 MultimodX https://nommon.atlassian.net/wiki/external/O GIzMDc4ZTJmY2YwNDEwMmI4YWRmY2JlYWM 2ZGY0NTc Capacity (CAP) CAP_PE1 Demand served Number of passengers that are assigned to itineraries over the total number of passengers who want to travel between a given origin-destination pair. Strategic L3 MultimodX https://nommon.atlassian.net/wiki/external/Yj YxYjcwZmFhYzUyNGQ2MmE5MTYyMTE0YzE3M jBmZTc CAP_PE2 Catchment area of airports Distance of ground mobility (e.g. rail) from which an airport captures demand. Could be normalised by passengers using that distance. Strategic L3 ‘Multimodality and passenger experience in the SESAR Performance Framework’ workshop https://nommon.atlassian.net/wiki/external/ZD UxMDljMjUxYWMzNGYxNDliMWZhZDhlN2ZiZD c1YWU CAP_PE3 Capacity available Number of seats available between a given origindestination pair. Strategic L3 MultimodX https://nommon.atlassian.net/wiki/external/O DFhMmYwZGFmOTNkNDIwOWFiMjEzOTM1Yz A0YWU0NGM Environment (ENV) ENV_PE1 CO2 emissions Total emissions of services used (could be normalised by passenger-km considering the demand served) Strategic L3 MultimodX https://nommon.atlassian.net/wiki/external/M WFiZWY2Yjc1ZDc5NGFlMWE5ZmY2OWY2ZDcz MWJlNTU Predictabil ity (PRED) PRED_PE1 Variability Share of passengers arriving late (within pre-defined time slot) [percentage] Tactical L3 MultimodX https://nommon.atlassian.net/wiki/external/M zk1Mzg1MWRlZTFjNDNmYWEyOGRjYTVhZWUy MTIzYmQ