# Final scenarios for multimodal traffic management

Ludovic Vaillant, Bruno Dewailly, Marie Douet, Marit Natvig, Trine Marie Stene, Ida Norin, Elena Branchini, Nicola Cavagnetto

► To cite this version:

Ludovic Vaillant, Bruno Dewailly, Marie Douet, Marit Natvig, Trine Marie Stene, et al. Final scenarios for multimodal traffic management. European commission - DG Research and Innovation. 2023, pp.81. halshs-04090106

HAL Id: halshs-04090106

https://shs.hal.science/halshs-04090106v1

Submitted on 9 Apr 2025

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Orchestra

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Orchestra

www.orchestra2020.eu

ORCHESTRA Project Deliverable: D2.4

# Final scenarios for multimodal traffic management

Authors:

Ludovic Vaillant, Bruno Dewailly, Maire Douet (CEREMA),

Marit K. Natvig, Trine Marie Stene (SINTEF),

Ida Norin (HIP),

Elena Branchini (SEA),

Nicola Cavagnetto (DBL)

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## Deliverable Identification

<table><tr><td>Deliverable ID:</td><td>D2.4</td><td>Deliverable title:</td><td>Final scenarios for multimodal traffic management</td></tr><tr><td></td><td>Release Number:</td><td>1.0</td><td></td></tr><tr><td></td><td>Release Date:</td><td>2023-04-30</td><td></td></tr><tr><td></td><td>Deliverable Description</td><td colspan="2">The final version of the target vision multimodal traffic management ecosystem (MTME) and main trend scenarios for a socio-culturally accepted MTME.</td></tr><tr><td></td><td>Dissemination Level</td><td colspan="2">PU = Public</td></tr><tr><td></td><td>Deliverable Type</td><td colspan="2">R = Report</td></tr><tr><td></td><td>Due date (month number/date)</td><td colspan="2">Month 24 / 2023-04-30</td></tr></table>

## Release History

<table><thead><tr><td>Version</td><td>Date</td><td>Internal Review Milestone Reached (if relevant)</td><td>Summary of main changes introduced in this version</td></tr></thead><tbody><tr><td>0.1</td><td>2022-11-04</td><td>PCOS proposed</td><td>Initial version</td></tr><tr><td>0.2</td><td>2022-11-06</td><td>PCOS approved</td><td>Initial version</td></tr><tr><td>0.3</td><td>2023-02-27</td><td>Intermediate proposed</td><td>Introduction of the main sociocultural scenario for MTME (scenario 1).</td></tr><tr><td>0.4</td><td>2023-03-13</td><td>Intermediate approved</td><td>Approved if updated – no new review needed</td></tr><tr><td>0.5</td><td>2023-04-17</td><td>External proposed</td><td>Restructuration of the outline. Added description of the methodology and scenarios 2 and 3.</td></tr><tr><td>0.9</td><td>2023-04-26</td><td>Released version proposed</td><td>Added an introduction to the living labs chapter (4.1.4). New version of the conclusion.</td></tr><tr><td>1.0</td><td>2023-04-30</td><td>Released</td><td>Formal approval of Project Coordinator and partners has been obtained.</td></tr></tbody></table>
# About ORCHESTRA

The problem addressed by ORCHESTRA is that traffic caused by transport has many negative effects. There are congestions, delays, emissions and negative impacts on urban environments, and in case of disruptions, there may be huge consequences on the efficiency and timeliness. These challenges are hard to handle due to lack of coordination and collaboration between the different transport modes.

The long-term vision of ORCHESTRA is a future where it is easy to coordinate and synchronise the traffic management of all modes to cope with diverse demands and situations. The overall objective of ORCHESTRA is to provide European policy makers, public authorities, transport providers and citizens with new knowledge and technical and organisational solutions to enhance collaboration and synchronising of operations within and across transport modes.

The project will:

* Establish a common understanding of multimodal traffic management concepts and solutions, within and across different modes, for various stakeholders and multiple contexts

* Define a Multimodal Traffic Management Ecosystem (MTME) where traffic managements in different modes and areas (rural and urban) are coordinated to contribute to a more balanced and resilient transport system, bridging current barriers and silos

* Support MTME realisation and deployments, through the provision of tools, models, and guidelines - including the integration of connected and automated vehicles and vessels (CAVs)

* Validate and adjust MTME for organisational issues, functionality, capability and usability

* Maximise outreach and uptake of project results through strong stakeholder involvement

ORCHESTRA's main advancements beyond state-of-the-art are related to four focus areas:

* MTME facilitated by: 1) a Polycentric Multimodal Architecture (PMA) specifying how systems collaborate. 2) Flexible organizational and business models. 3) Simulation and training tools. 4) Policy and regulatory recommendations. 5) data governance and sharing framework

* Traffic orchestration supporting optimal traffic flows, adapted to current and foreseen situations and societal aspects. Real Time data on ongoing and planned transports as well as other issues that may affect the traffic will be monitored and used in decision support and to facilitate resilience

* Coordination across modes and networks bridging current silos, ensuring best possible utilisation of transport system as a whole

* Traffic management supporting more optimal multimodal transport services and fleet operations, those carried out by CAVs included. Transport operations will be guided and controlled according to pre-defined rules and trade-offs between different optimisation targets.

The project will validate and evaluate the multimodal traffic management concept and related tools in its two Living Labs, both in Norway and Italy, covering freight and person transports across road, rail, water and air.

# Legal disclaimer

This document reflects only the author's view, and the Agency is not responsible for any use that may be made of the information it contains.

# For more information

Project Coordinator: Runar Søråsen, runar.sorasen@its-norway.no

Dissemination Manager (WP7 leader): Trond Hovland, trond.hovland@its-norway.no
**Executive Summary**

This deliverable (D2).4 develops exploratory scenarios of the future of the MTM in a long-term perspective in Europe up to 2050. These scenarios are broadly designed to stimulate dialogue and debate on the future of MTM by depicting a few contrasting pictures of a data driven MTM.

The scenario building followed six steps. On the first step, 8 of the 12 KPAs defined in the D6.1 were considered as **influencing key-factors**, main drivers, regarding the MTME implementation. The 2nd step consisted of **collecting and analysing data** related to each of the key KPAs from policies reports, regulations, research articles, CoPs' workshops and experts' interviews. The 3rd step provided the **raw scenarios first description** to the experts and CoPs to validate them and to refine them, through workshops. During the 4th step, for each of the 8 KPAs, **a set of assumptions** were first designed and discussed through the experts of ORCHESTRA. The 5th step was dedicated to **an assumptions clustering to specify the three scenarios**. And last, **the story of each scenario was written** during the 6th step. Their abstracts are given below.

**Scenario 1, called "MTM becomes seamless and optimised"** is based on a vision of the future guided by the search for maximum optimisation in traffic management. This optimisation is made possible by the real-time sharing of data between stakeholders in passenger and freight traffic in all modes. This exchange can only work if the stakeholders have full confidence in the confidentiality of the data, its reliability and its secure storage. To this end, a major player has been introduced into the MTME designed up to now: the Data Intermediator (DI). It is the guarantor of the respect of these conditions, without which a complete deployment of the MTM cannot be envisaged in the future.

**Scenario 2 is a variant of scenario 1 in which "The Tech Giants became the main traffic data providers".** It corresponds to an extension of the basic trend since the early 2000s of the global supremacy of the Tech Giants in the collection and marketing of digital data. In this scenario, the development of what some sociologists and economists call "surveillance capitalism" significantly modifies the conditions in which the MTM is deployed: the fact that traffic data are stored and held by companies mainly outside Europe reinforces the need for the DI. The DI becomes the guarantor of the security of a minimum data base that allows traffic management according to basic modalities. A more expert, more personalised use of traffic remains possible, but in most cases, it requires the acquisition of data from the Tech Giants.

**Scenario 3 is a breakthrough scenario compared to the previous two. Its title "Welcome to a sustainable MTME"** describes a society concerned with the well-being of future generations. In this society, saving the planetary limits of both energy resources and raw materials necessary for the exchange of data and the transport of goods and people is an absolute priority. In this scenario, the democratic values of interpersonal relations are the most important in everyday life. The data needed for traffic management is considered as commons, and mobility is no longer seen as an indispensable factor for growth. The impacts of digital technology, like those of transport, are information that is constantly being delivered and MTM is only deployed where it is useful and where it has a proven positive impact. As a result, individuals and companies have a mobility quota that is considered in traffic management. Democratic values have made it possible to set up a particular governance of the ID: the participation of stakeholders in this governance, including people from civil society, reinforces the confidence given to it as regards the security and reliability of the data and the use made of it.
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# Table of Contents

<table><tr><td>Deliverable Identification</td><td>2</td></tr><tr><td>Release History</td><td>2</td></tr><tr><td>About ORCHESTRA</td><td>3</td></tr><tr><td>Legal disclaimer</td><td>3</td></tr><tr><td>For more information</td><td>3</td></tr><tr><td>Executive Summary</td><td>4</td></tr><tr><td>List of Abbreviations</td><td>9</td></tr><tr><td>1 About this Deliverable</td><td>12</td></tr><tr><td>1.1 Why would I want to read this deliverable?</td><td>12</td></tr><tr><td>1.2 Intended readership/users</td><td>12</td></tr><tr><td>1.3 Structure</td><td>12</td></tr><tr><td>1.4 Other project deliverables that may be of interest</td><td>13</td></tr><tr><td>1.5 Involvement in work</td><td>13</td></tr><tr><td>2 Introduction</td><td>14</td></tr><tr><td>3 Background</td><td>16</td></tr><tr><td>3.1 MTM concepts</td><td>16</td></tr><tr><td>3.1.1 Governance area (GA)</td><td>16</td></tr><tr><td>3.1.2 Traffic orchestration</td><td>17</td></tr><tr><td>3.1.3 Transport demand management (TDM)</td><td>17</td></tr><tr><td>3.1.4 Demand Capacity Balancing (DCB)</td><td>17</td></tr><tr><td>3.2 The MTM ecosystem stakeholders' roles</td><td>17</td></tr><tr><td>3.2.1 Traffic Orchestrator (TO)</td><td>18</td></tr><tr><td>3.2.2 Strategic Planning Manager (SPM)</td><td>19</td></tr><tr><td>3.2.3 Network Manager (NM)</td><td>19</td></tr><tr><td>3.2.4 Transport Service Provider (TSP)</td><td>19</td></tr><tr><td>3.2.5 Fleet operator (FO)</td><td>19</td></tr><tr><td>3.2.6 Network User (NU)</td><td>20</td></tr><tr><td>3.2.7 Transport User (TU)</td><td>20</td></tr><tr><td>3.3 Data sharing between stakeholder roles</td><td>20</td></tr><tr><td>4 Three contrasted sociocultural scenarios</td><td>22</td></tr><tr><td>4.1 Scenario 1: MTM became seamless and optimised</td><td>22</td></tr><tr><td>4.1.1 Data sharing as THE basis of the MTME</td><td>24</td></tr><tr><td>4.1.1.1 Technical components that cut across the different modes of transport</td><td>24</td></tr><tr><td>4.1.1.2 Data exchange in air traffic management</td><td>25</td></tr></table>

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 953618. This document reflects only the author's view and the Agency is not responsible for any use that may be made of the information it contains.
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<table><tbody><tr><td>4.1.1.3</td><td>Data exchange in road traffic management</td><td>27</td></tr><tr><td>4.1.1.4</td><td>Data exchange in rail traffic management</td><td>27</td></tr><tr><td>4.1.1.5</td><td>Data exchange in maritime traffic management</td><td>28</td></tr><tr><td>4.1.1.6</td><td>Data exchange in river traffic management</td><td>28</td></tr><tr><td>4.1.1.7</td><td>Cyber security</td><td>29</td></tr><tr><td>4.1.2</td><td>The conditions for the sharing of data necessary for the MTM</td><td>29</td></tr><tr><td>4.1.2.1</td><td>The Data Intermediator (DI)</td><td>30</td></tr><tr><td>4.1.2.2</td><td>Governance of traffic management data</td><td>30</td></tr><tr><td>4.1.2.3</td><td>Transparent decision-making processes</td><td>31</td></tr><tr><td>4.1.2.4</td><td>Hierarchisation of the MTM criteria according to traffic orchestration strategies</td><td>32</td></tr><tr><td>4.1.2.5</td><td>Digital transition</td><td>32</td></tr><tr><td>4.1.3</td><td>New services and policies</td><td>34</td></tr><tr><td>4.1.3.1</td><td>New traffic management services</td><td>34</td></tr><tr><td>4.1.3.2</td><td>New mobility services</td><td>34</td></tr><tr><td>4.1.3.3</td><td>New transport and mobility policies</td><td>35</td></tr><tr><td>4.1.3.4</td><td>The road to the generalized automated vehicle?</td><td>36</td></tr><tr><td>4.1.4</td><td>Focus on the Living Labs</td><td>36</td></tr><tr><td>4.1.4.1</td><td>Focus on an industrial area such as HIP</td><td>36</td></tr><tr><td>4.1.4.2</td><td>Focus on an airport use case</td><td>38</td></tr><tr><td>4.2</td><td>Scenario 2: The Tech Giants became the main traffic data providers</td><td>40</td></tr><tr><td>4.2.1</td><td>The underlying values of this sociocultural scenario</td><td>41</td></tr><tr><td>4.2.2</td><td>Addictive and costly policies</td><td>43</td></tr><tr><td>4.2.3</td><td>A traffic data market under a Big Tech straitjacket</td><td>43</td></tr><tr><td>4.2.4</td><td>Technologies with systemic impacts that raise questions</td><td>44</td></tr><tr><td>4.2.5</td><td>External costs socialisation generates inequalities and imbalances</td><td>44</td></tr><tr><td>4.3</td><td>Scenario 3: Welcome to a sustainable MTME</td><td>45</td></tr><tr><td>4.3.1</td><td>Fair and ecological policy and governance drive by combined, multimodal and mixed transport</td><td>47</td></tr><tr><td>4.3.2</td><td>Traffic management data have become commons</td><td>48</td></tr><tr><td>4.3.3</td><td>The MTM ensure universal basic mobility and does not burden future generations</td><td>49</td></tr><tr><td>4.3.4</td><td>Social acceptability and business practices facilitated by welfare</td><td>49</td></tr><tr><td>4.3.5</td><td>Yesterday's ports and airports have become true multimodal platforms</td><td>50</td></tr><tr><td>5</td><td>Methodology</td><td>51</td></tr><tr><td>5.1</td><td>Short description of the sociocultural scenarios design process</td><td>51</td></tr><tr><td>5.2</td><td>Selecting key-factors</td><td>52</td></tr><tr><td>5.2.1</td><td>KPAs as key-factors</td><td>52</td></tr><tr><td>5.2.2</td><td>KPAs' refinement regarding to the sociocultural approach</td><td>53</td></tr><tr><td>5.3</td><td>Collecting data</td><td>54</td></tr><tr><td>5.4</td><td>Raw scenarios first description</td><td>55</td></tr><tr><td>5.5</td><td>Formulating assumptions for the 3 scenarios</td><td>55</td></tr></tbody></table>
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<table><tr><td>5.5.1</td><td>Explanation of KPA1 assumptions: Politics, governance and regulation</td><td>57</td></tr><tr><td>5.5.2</td><td>Explanation of KPA2 assumptions: Data governance and sharing</td><td>57</td></tr><tr><td>5.5.3</td><td>Explanation of KPA3 assumptions: Smart infrastructure for automation and integration of CAVs</td><td>57</td></tr><tr><td>5.5.4</td><td>Explanation of KPA4 assumptions: Technology solutions</td><td>58</td></tr><tr><td>5.5.5</td><td>Explanation of KPA5 assumptions: Acceptance for traffic management operations and autonomy</td><td>58</td></tr><tr><td>5.5.6</td><td>Explanation of KPA6 assumptions: Operational practices and decision making</td><td>58</td></tr><tr><td>5.5.7</td><td>Explanation of KPA7 assumptions: Business and organisational aspects</td><td>59</td></tr><tr><td>5.5.8</td><td>Explanation of KPA8 assumptions: Operational aspects</td><td>59</td></tr><tr><td>5.6</td><td>Assumptions clustering</td><td>59</td></tr><tr><td>5.7</td><td>Story of each scenario</td><td>61</td></tr><tr><td>6</td><td>Conclusions</td><td>65</td></tr><tr><td>7</td><td>References</td><td>66</td></tr><tr><td colspan="2">Appendix</td><td>72</td></tr></table>
## List of Figures

<table><tr><td>Figure 1: The Temple of MTME as a representation of the ORCHESTRA target vision</td><td>16</td></tr><tr><td>Figure 2: Multimodal Traffic Management Ecosystem (MTME) with System of Interest in its environment</td><td>18</td></tr><tr><td>Figure 3: The introduction of the Data Intermediator (DI) actor role in the MTME ecosystem</td><td>23</td></tr><tr><td>Figure 4: The DI in an MTME dominated by Big Tech companies</td><td>41</td></tr><tr><td>Figure 5: The DI within an MTME with considerations of sustainability and digital frugality at its core</td><td>46</td></tr><tr><td>Figure 6: Construction method of the prospective scenarios</td><td>51</td></tr><tr><td>Figure 7: The selected KPAs from the MTME program theory</td><td>53</td></tr><tr><td>Figure 8: Grid of all assumptions considered by KPA for the construction of the prospective scenarios</td><td>56</td></tr><tr><td>Figure 9: Constancies and variations in the implementation of assumptions</td><td>60</td></tr><tr><td>Figure 10: Grid of assumptions considered by KPA for the construction of the Scenario 1</td><td>62</td></tr><tr><td>Figure 11: Grid of assumptions considered by KPA for the construction of the scenario 2</td><td>63</td></tr><tr><td>Figure 12: Grid of assumptions considered by KPA for the construction of the Scenario 3</td><td>64</td></tr></table>

## List of tables

<table><tr><td>Table 1: List of abbreviations</td><td>9</td></tr></table>
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## List of Abbreviations

<table><caption>Table 1: List of abbreviations</caption><thead><tr><td>Abbreviation</td><td>Explanation</td></tr></thead><tbody><tr><td>AgID</td><td>Agenzia per l'Italia Digitale, ie. the Agency for Digital Italy (https://www.agid.gov.it/en/agency/about-us)</td></tr><tr><td>AI</td><td>Artificial Intelligence</td></tr><tr><td>AIaaS</td><td>Airport and aeronautical Information as a Service</td></tr><tr><td>AIP</td><td>Aeronautical Information Publications</td></tr><tr><td>ATM</td><td>Air Traffic Management</td></tr><tr><td>AV</td><td>Automated Vehicle/Vessel</td></tr><tr><td>CAA</td><td>Civil Aviation Authority</td></tr><tr><td>CAV</td><td>Connected and Automated Vehicle</td></tr><tr><td>CCAM</td><td>Cooperative, connected and automated mobility</td></tr><tr><td>CORUS-XUAM</td><td>Concept of operations for European UTM systems – Extension for urban air mobility. A very large-scale demonstration (VLD) H2020 project.</td></tr><tr><td>C-ITS</td><td>Cooperative Intelligent Transport Systems</td></tr><tr><td>CIRMAR</td><td>French Research platform for the analysis of marine traffic (https://www.projet-devport.fr/le-projet)</td></tr><tr><td>CoP</td><td>Community of Practitioners</td></tr><tr><td>CRA</td><td>European Cyber Resilience Act</td></tr><tr><td>DCB</td><td>Demand Capacity Balancing</td></tr><tr><td>DGA</td><td>European Data Governance and amending Regulation</td></tr><tr><td>DI</td><td>Data Intermediator</td></tr><tr><td>DoA</td><td>Description of Action</td></tr><tr><td>EAD</td><td>European Aeronautical Information System</td></tr><tr><td>EASA</td><td>European Aviation Safety Agency</td></tr><tr><td>EEZ</td><td>Exclusive Economic Zone</td></tr><tr><td>ERTMS</td><td>European Railway Transport Management System</td></tr><tr><td>ETSMS</td><td>Electronic Time Sheet Management System</td></tr><tr><td>EU</td><td>European Union</td></tr><tr><td>eVTOL</td><td>Electric Vertical Take-Off and Landing vehicles</td></tr><tr><td>FMS</td><td>Flight Management Systems</td></tr><tr><td>FO</td><td>Fleet operator</td></tr><tr><td>GA</td><td>Governance Area</td></tr></tbody></table>
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<table><thead><tr><td>Abbreviation</td><td>Explanation</td></tr></thead><tbody><tr><td>GAFAM</td><td>Google, Apple, Facebook, Amazon, Microsoft</td></tr><tr><td>GDPR</td><td>General Data Protection Regulation</td></tr><tr><td>GHG</td><td>Green House effect Gasses</td></tr><tr><td>GIZ</td><td>Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (German Society for International Cooperation)</td></tr><tr><td>GMDSS</td><td>Global Maritime Distress and Safety System</td></tr><tr><td>GPP</td><td>Green Public Procurement</td></tr><tr><td>HIP</td><td>Herøya Industrial Park</td></tr><tr><td>IMC</td><td>Information-Mobility's Common</td></tr><tr><td>IMCtt</td><td>Transport and Traffic Information-Mobility's Common</td></tr><tr><td>IMO</td><td>International Maritime Organization</td></tr><tr><td>ITS</td><td>Intelligent Transport Systems</td></tr><tr><td>IVI</td><td>In-vehicle information</td></tr><tr><td>KPA</td><td>Key Performance Area</td></tr><tr><td>KPI</td><td>Key Performance Indicator</td></tr><tr><td>LL</td><td>Living Lab</td></tr><tr><td>LRIT</td><td>Long-range identification and tracking</td></tr><tr><td>MAP</td><td>Map Data</td></tr><tr><td>MaaS</td><td>Mobility-as-a-Service</td></tr><tr><td>MTM</td><td>Multimodal Traffic Management</td></tr><tr><td>MTME</td><td>Multimodal Traffic Management Ecosystem</td></tr><tr><td>MS</td><td>Milestone</td></tr><tr><td>NAP</td><td>National Access Point</td></tr><tr><td>NATS</td><td>National Air Traffic Services</td></tr><tr><td>NATU</td><td>Netflix, AirBnb, Tesla, Uber</td></tr><tr><td>NGO</td><td>Non-Governmental Organisation</td></tr><tr><td>NM</td><td>Network Manager</td></tr><tr><td>NU</td><td>Network User</td></tr><tr><td>OEM</td><td>Original Equipment Manufacturer</td></tr><tr><td>OMA</td><td>Organising Mobility Authorities</td></tr><tr><td>PMA</td><td>Polycentric Multimodal Architecture</td></tr><tr><td>PI</td><td>Physical Internet</td></tr></tbody></table>
<figure><img src="image_11.png" /></figure>

<table><thead><tr><td>Abbreviation</td><td>Explanation</td></tr></thead><tbody><tr><td>PKI</td><td>Public Key Infrastructure</td></tr><tr><td>PRM</td><td>Persons with Reduced Mobility</td></tr><tr><td>SAE</td><td>Society of Automotive Engineers</td></tr><tr><td>SES</td><td>Single European Sky</td></tr><tr><td>SESAR</td><td>Single European Sky ATM Research</td></tr><tr><td>SOA</td><td>Service-oriented architecture</td></tr><tr><td>SPaT</td><td>Signal phase and timing</td></tr><tr><td>SPM</td><td>Strategic Planning Manager</td></tr><tr><td>SSAS</td><td>Ship Security Alert System</td></tr><tr><td>SUTP</td><td>Sustainable Urban Transport Project</td></tr><tr><td>SWIM</td><td>System-wide information management</td></tr><tr><td>TDM</td><td>Transport demand management</td></tr><tr><td>TEN-T</td><td>Trans-European Network - Transport</td></tr><tr><td>TFM</td><td>Traffic Flow Management</td></tr><tr><td>TINDAIR</td><td>Tactical Instrumental Deconfliction And in flight Resolution, a european research consortium</td></tr><tr><td>TO</td><td>Traffic Orchestrator</td></tr><tr><td>TSP</td><td>Transport Service Provider</td></tr><tr><td>TU</td><td>Transport User</td></tr><tr><td>TUMI</td><td>Transformative Urban Mobility Initiative</td></tr><tr><td>UAM</td><td>Urban Air Mobility</td></tr><tr><td>UAS</td><td>Unmanned Aircraft Systems</td></tr><tr><td>UTM</td><td>Unmanned aircraft systems Traffic Management</td></tr><tr><td>U-Space</td><td>Service-orientated concept to provide air traffic management (ATM) to drones and is described in detail in the SESAR roadmap</td></tr><tr><td>VTS</td><td>Vessel Traffic Management</td></tr><tr><td>ZNLA</td><td>Zero net land artificialisation, idea inspired by the ZAN concept of the French "climate resilience" law (promulgated on 22 August 2022)</td></tr></tbody></table>
# 1 About this Deliverable

## 1.1 Why would I want to read this deliverable?

In the continuity of deliverables D2.1, D2.2 and D2.3, this deliverable provides the last refined target vision for Multimodal Traffic Management (MTM). **D2.4 describes three contrasted so-called "sociocultural scenarios"** that correspond to three different contexts for the implementation and acceptance of the MTM concepts. Thus, each scenario details the main trends related to each context dealing with the related requirements, enablers and the mechanisms of change that are necessary for the MTM to be deployed. They provide the socio-technical environment that might be adopted as a starting point to draw the cases of Herøya (Norway) port and Malpensa (Italia) airport.

These three scenarios rely on three different combinations of coherent assumptions which refer to different economic, political and societal contexts. So, these scenarios will be relevant for the drafting of the white paper to policy makers (D2.5) and the development of contractual and administrative implementation (D4.4). They will also feed the evaluation process according to the defined Key Performance Areas (KPAs) and Key Performance Indicators (KPIs) (D6.2, D6.3), besides constituting the general frame of the Living Lab scenarios that are described in D5.3.

## 1.2 Intended readership/users

This deliverable is of interest for the European Commission, as well as beneficiaries of other European projects interested by the vision of the future traffic management, and the inclusion of Connected and Automated Vehicles (CAVs) in the traffic.

It is also of interest to all the ORCHESTRA project partners involved in:

* WP3 *Polycentric traffic management design*, as the scenarios are inputs to the work on the intermediate Polycentric Multimodal Architecture (PMA) for multimodal traffic management (D3.2).

* WP4 *Enabling toolkit, organization and business model*, as the implementation scenario described in the D2.4 provides inputs to the business model's design.

* WP6 *Evaluation and lessons learned*, as the implementation scenario described in D2.3 gives relevant data to answer the research questions for some of the KPAs.

Finally, it can be of interest for the practitioners that wonder how to manage MTM because it can help them to organize their roadmap towards the deployment of the MTME.

## 1.3 Structure

The report is structured in such a way that the reader can read about the imagined scenarios before discovering how they were constructed. This is a voluntary choice of the authors, deriving from the methodology followed to draw the three scenarios, which is quite complex in terms of data collection, processing and validation. The Appendixes also provide additional data, mainly related to the workshops and experts' interviews.

The description of the scenarios is punctuated by references in square brackets such as [KP Ax-Ay]. They refer to the table of assumptions detailed in Chapter 5 Methodology created to serve as a matrix for the scenarios.
1.4 Other project deliverables that may be of interest

In addition to this report, the reader is invited to read the other deliverables with which D2.3 is in connection. Inputs are used from:

D2.1 *Initial target vision for multimodal traffic management.* It defines the initial target vision of a multimodal traffic management ecosystem within the 2030 and 2050 horizons.

D2.2 *Pre-studies on environment analysis and drivers.* It defines the context in which multimodal traffic management will operate.

D3.1 *Initial use cases for multimodal traffic management.* It defines Multimodal Traffic Management concepts and concerns that the scenarios take into account.

D3.2 *Intermediate PMA for multimodal traffic management.* It describes the system of interest, stakeholders and concern; intermediate description of logical services, requirements, use cases, information models and identification interfaces.

D4.3 *Handbook on organisational, business and market models.*

D6.1 *Evaluation handbook.* It defines the evaluation approach including the research questions, KPAs and KPIs as well as the data collection methods and plans for data analysis.

D6.2 *Intermediate evaluation results from Living Labs*, specifying the research questions related to each KPA.

Outputs will be to:

D2.5 *White paper to policy makers for a European multimodal traffic management ecosystem*

D3.3 *Final Polycentric Multimodal Architecture (PMA) for multimodal traffic management*

D4.3 *Handbook on organisational, business and market models*

D4.4 *Handbook on contractual and administrative implementation*

D4.5 *Training modules*

D6.2 *Intermediate evaluation results from living labs*

D6.5 *Recommendations and lessons learned*

It should be noted that some of these deliverables were written simultaneously with D2.4 and that the corresponding deliverables fed into each other.

1.5 Involvement in work

Partners involved in this report are CEREMA, SINTEF, IKEM, SEA, DBL, HIP and AA.

CEREMA has been the leader of the work and the main author of the deliverable. SINTEF ensured that the MTM concepts were properly taken into account. SEA carried out several proof readings and ensured that the links with the living lab were explicit. DBL provided inputs related to the data transition chapter. And all the mentioned partners gave inputs during the workshops.
<figure><img src="image_12.png" /></figure>

## 2 Introduction

> "If a statement said to be about the future makes sense to you, it is probably about the present and therefore not very useful. If it shocks or disgusts you, or seems ridiculous science fiction, it may be about the futures, and hence useful to you."

Pr. James Allen Dator

Technical change influences society by causing major changes in social interactions, organisations, institutions and even individuals themselves. Conversely, societal orientations can determine certain technical developments. Industrial history is full of examples of this. For instance, with the invention of the mechanisation of the weaving loom at the beginning of the 18th century, Jacquard had contributed to the evolution of society: New industrial structures, new norms, new trades, new power struggles.

What about the impact of Intelligent Transport Systems (ITS) in the society? Indeed, the future cannot be predicted, but alternative futures can be forecast, and preferred futures envisioned and characterized. Considering that one of the main assumptions of the ORCHESTRA project is that the sociocultural context may influence the implementation and the acceptability of the MTM, the present report intends design three possible or at least probable futures of sociocultural context for the MTME.

The three scenarios designed, are logical statements, contingent statements, or “if...then” statements. In the authors’ perspective they are not meant to be “truthful”, they are meant to be logical and useful to illustrate several possible futures that need to be taken into consideration before taking actions towards the adoption of a MTM system.

They are sociocultural scenarios. In this deliverable, sociocultural is defined as “that concerns a human group and its culture”. Indeed, several previous scientific works pointed out that the adoption – or not – of a technology depends on different factors amongst the sociocultural environment (Al-Momani et al. 2018, Bergeron et al. 2015, Manout et al. 2022, Sunitiyoso et al. 2022). Sociocultural is a qualifier that can encompass many formal and informal components: Political, ethical, legal, technical, etc.

D2.4 aims to investigate in a more detailed way some further aspects of the MTM context that previous work started to explore (see D2.3). Thus, three sociocultural scenarios are suggested in this report: One core scenario (scenario 1) which fits with the traffic management optimisation goals pursued by ORCHESTRA, and two alternative ones which emphasis on two main issues: the Tech Giants technical and financial supremacy (scenario 2) on one hand and, the environmental and social issues that lead to imagine a scenario of the frugality (scenario 3), on the other hand.

The report provides particularly answers from a sociocultural point of view to two of the research questions addressed in D6.2 (see p. 28):

- **RQ 2.1: How should data sharing and its governance be implemented?**

- **RQ 2.2 What are the societal aspects and concerns that should be considered related to data sharing?**
It is worth to remind here that none of these scenarios reflects a definite view of how the future will, in fact, develop. They are the authors' view of how the future might evolve in three different sociocultural contexts, at the time they were imagined (2022 and 2023).

After recalling the background of the MTME concepts within the ORCHESTRA project (chapter 3), the report presents the three possible sociocultural scenarios and describes their development (chapter 4). Finally, the method for the building of the three alternative futures scenarios is described in a short way and in a more detailed way (chapter 5).
# 3 Background

As a successful innovative technology, MTM needs to be adopted and accepted by society, and be relevant to intended user groups. The previous described target vision for MTM (D2.3, chapter 5) can only happen in an enabling environment. As already presented in D2.1, D2.2 and D2.3, many requirements of different natures are needed. They are related to technology aspects of MTM, frameworks and models that are context prerequisites and societal aspects which involve processes of acceptance. These requirements were summed up in the first months of the project under the symbolic image of the “temple of MTME” (Figure 1) and partly detailed in the D2.2 and D2.3 that followed.

<figure><img src="image_13.png" /><figcaption>Figure 1: The Temple of MTME as a representation of the ORCHESTRA target vision</figcaption></figure>

Source: D2.1, p. 57.

## 3.1 MTM concepts

### 3.1.1 Governance area (GA)

“A governance area defines the area or space with respect to some authority and is the zone in which the traffic orchestration takes place” (D3.1). A governance area (GA) is related to a single network and a single mode of transport, and the traffic within the GA is managed by one Traffic Orchestrator. The organisation and geographical size of a GA depends on the mode, the network segment(s) in need for monitoring and control, and its institutional perimeter of control. Thus, the structuring of GAs may follow different patterns to fulfil different needs. Some examples are provided below:

* GAs may vary in size. For road networks, a GA may for example cover a bridge, a tunnel, or a segment of road, or it may cover all roads within an area, e.g. a region or a municipality.
* GAs may be structured as non-overlapping areas. In sea transport, the coast of a national state may for example be divided into Vessel Traffic Management (VTS) areas. GAs may be structured in a hierarchy. In air transport, the GA of EuroControl covers the whole European air space, and it is superior to the GAs covering regions.

### 3.1.2 Traffic orchestration

The Traffic Orchestrator (TO) is responsible for the traffic orchestrations within a GA. The traffic orchestration goes beyond the traditional traffic management to mitigate and handle current and upcoming traffic situations. This also includes coordination with other networks and modes, and the measures taken are anchored in well-defined policies and strategies on how the traffic is to be managed with respect to (see further definitions below):

1. Transport demand management

2. Demand capacity balancing

### 3.1.3 Transport demand management (TDM)

Transport demand management (TDM) is a part of the transport orchestration. The aim is to maximize the sustainability (efficiency, climate neutrality, inclusivity, profitability in coherence/balance/relation) of the transport system by discouraging/restricting unnecessary vehicle use and promoting/enabling more effective, profitable, healthy and environmentally friendly transport across all modes and infrastructure owners/transport networks.

TDM may include different measures, e.g. measures for access control, priority, information dissemination, traffic calming, payment, etc. The measures are carried out according to well defined measure conditions and with connected vessels/vehicles, the measures can to a large extent be automated.

### 3.1.4 Demand Capacity Balancing (DCB)

Demand capacity balancing (DCB) is a part of the transport orchestration. The aim is to cope with a current or upcoming situation with imbalance between the transport demand and the capacity of the network. Measures are taken to re-establish the balance. DCB may include:

* Measures taken towards individual Network Users (NUs). They may for example be asked to wait, slow down, or to take another route.

* Measures taken to adapt the network capacity to the need. They may for example redefine the use of parts of the infrastructure, open/close parts of the infrastructure, etc.

* Use of transport demand management measures (see above), for example access control and priority to balance the capacity.

* Coordination with other modes and networks. In case of problems in one network, traffic may be transferred to other networks with available capacities.

## 3.2 The MTM ecosystem stakeholders' roles

The core transport and traffic stakeholder archetypes, called roles, were initially described in Chapter 5.1 of deliverable D3.1 and further refined in Chapter 5.1 of Deliverable 3.2. These stakeholder roles are the Traffic Orchestrator (TO), the Strategic Planning Manager (SPM), the Network Manager (NM), the Transport Service Provider (TSP), the Fleet Operator (FO) and the Network User (NU). Indeed, in the continuity of D3.1, these six stakeholder roles are directly related to the System of Interest within the MTME (see Figure 2).
Three preliminary remarks to keep in mind before the more detailed presentation of the roles:

* A real actor may fulfil the responsibilities of several roles (one person may have several hats).

* The **stakeholder roles do not overlap** (cannot wear more than one hat at a time).

* When talking about a stakeholder role, we **do not consider other roles that a person may have in addition to the role addressed** (we consider just one hat at a time).

<figure><img src="image_14.png" /><figcaption>Figure 2: Multimodal Traffic Management Ecosystem (MTME) with System of Interest in its environment</figcaption></figure>

Source D3.2, p. 28.

### 3.2.1 Traffic Orchestrator (TO)

The term "orchestration" is defined to be "an arrangement of events that attempts to achieve a maximum effect" (https://www.thefreedictionary.com). Dhanaraj and Parkhe (2006) address orchestration in the context of networks consisting of loosely coupled and autonomous firms, where a hub "orchestrate network activities to ensure the creation and extraction of value, without the benefit of hierarchical authority".

Based on the above definitions, we **define traffic orchestration to be informed decisions and actions that affect the traffic modes in a way that traffic flow is optimized thus resulting in a more efficient and resilient traffic management that finally also benefits the society. And, as there may be different values corresponding to a given society, there may also be different "traffic management optima"**.

The traffic orchestration is the result of the decisions taken by the Traffic Orchestrators of each single governance area, each represented by a specific and identified Traffic Orchestrator (TO), based on a holistic picture of the current and upcoming traffic situation. The GAs managed through the traffic orchestration are part of the multimodal traffic management ecosystem (MTME), allowing the traffic management within and between the different GAs to cooperate and "play together" to accomplish overall goals.
As stated in D3.2, the TO aims to arrange for sustainable transport from an environmental, economic/socio-economic, and societal point of view according to the directions of the Strategic Planning Manager (SPM) as well as operational laws and regulations. This is done through:

* Traffic management. The traffic flow and the movement of vessels/vehicles/pedestrians is guided or controlled to ensure safety, efficiency, and optimal utilisation of the network. This includes interactions with NUs related to transport demand management, capacity demand balancing, access control, traffic control/guidance, information sharing, etc.

* Transport network resource management. The access to and use of limited resources like parking spaces, loading bays, waiting areas, etc. is managed.

* Coordination towards other modes, networks, and GAs. Information is exchanged and decisions and actions are coordinated to contribute to a more optimal transport system as a whole.

### 3.2.2 Strategic Planning Manager (SPM)

The SPM may operate at scale spatial (local, regional, national or even international level). He is the one who takes strategic decisions related to traffic management and orchestration. Its prerogatives and responsibilities include plans and strategies for automated vs. manual measures, priorities, demand capacity balancing, transport demand management, access control, arbitration rules, etc. In line with the transport policy defined at the GA level, the SPM may also authorize the use of more permanent zones and measures to fulfil political goals (e.g. low emission zones). And least but not last, he is also responsible for the overall strategies regarding coordination and collaboration between modes, networks and GA, as well as overall strategies for use, or not, of technologies.

### 3.2.3 Network Manager (NM)

The NM is responsible for the management of the physical transport network infrastructure as well as the digital infrastructure with sensors, actuators, and equipment for data collection and communication with NUs.

### 3.2.4 Transport Service Provider (TSP)

The TSP provides transport services to the Transport User, and the scope of responsibility is the whole transport chain provided to the user, as illustrated in Figure 2. This includes customer support and information, decisions how the service is to be provided, and the follow up. In case of disruption, as exemplified in both living labs (LLs):

* in person transport, Transport User (passenger) usually selects a preferred choice thus influencing the alternatives proposed by the TSP (e.g., select the modes and routes to be used);

* in freight transport, the TSP quite often takes decisions on behalf of the Transport User (freight shipper).

The decisions will affect which Fleet Operator (FO) to use as an alternative. One or more FOs may be needed to cover the desired transport chain. The final transport operations will be managed by one or more FOs.

### 3.2.5 Fleet operator (FO)

The FOs of different types of transport manages transport operations, and the scope of responsibility is limited to the transport types/legs covered by these transport operations, as illustrated in Figure 2. One transport operation may carry passengers and/or cargo from several transport chains. Each FO manages resources like personnel, vehicles/vessels, space in vehicles/vessels, and load units. Time
schedules are planned, resources are allocated, and operations are followed up to ensure that they are
carried out according to rules, regulations, and agreements. The use of resources is planned to be as
optimal as possible. Optimal routes and possibilities for return loads are considered. When relevant,
the TSPs are informed about the progress.

3.2.6 Network User (NU)

The NU accomplishes a transport operation on one transport leg, and may be a person (e.g., a pedestrian), a person supported by systems (vehicle/vessel with driver/operator, micro mobility included), or a system (e.g. a Connected and Automated Vehicle - CAV). As stated in D3.1 and D3.2, the NU is responsible for an integration into the traffic in compliance with laws and regulations, and for safety issues related to behaviour and operation of the vehicle/vessel.

3.2.7 Transport User (TU)

TUs need transport from door-to-door, and can be a traveller or a cargo owner/shipper in need for
transport of cargo.

3.3 Data sharing between stakeholder roles

Based on the Description of Action (DoA) and on D2.3 *Initial scenarios for MTM*, and thus on the
KPAs related to the selected change mechanisms, we can assume that **data (in general) and its
sharing is a future necessity and one of the main requirements for MTM**, if not its central
requirement. Indeed, their identification, collection, processing and dissemination might become an
issue for the MTM. In addition, the nature of the data exchanged may influence the acceptance of the
MTM.

It may not be possible to describe all the data that will be exchanged in the future, but a first overview
of their scope can be summarized, according to the typologies defined in D3.2.

Input from transport part to traffic part may for example be:

* Data on plans for transport operations, on amount and type of cargo/goods/people (TSP and/or FO → TO).

* Data on ongoing transport operations (TSP/FO → TO and NU → TO), e.g. type of operation, type of vessel/vehicle, locations (start, stops, destination, ...), time schedule, special needs, etc.

* Miscellaneous real-time data (NU → TO/NM), to a large degree data from on-board sensors, e.g. data on transport operation (position, speed, heading, ...), data on the environment (temperature, humidity, friction, air quality, ...).

Output from traffic part to transport part may for example be:

* Information on traffic situations, predicted as well as occurring (TO → TSP/FO/NU).

* Information on regulations of the transport network (e.g. access restrictions, payment, ...), dynamic regulations included (NM → TSP, FO, NU).

* Information on planned and effectuated traffic management measures, adapted to regulations (TO → TSP/FO/NU).

* Advices/guidelines (TO → TSP/FO/NU).

* Measures that affect individual NUs and the traffic (TO → NU).
Coordination between different networks, modes and governance areas may for example be:

* Capacity information (TO → TO).

* Coordination of measures that have effects across network/modes/areas (TO ↔ TO).

* Coordination of guidelines to transport part regarding use of networks and modes (TO ↔ TO).
<figure><img src="image_15.png" /></figure>

# 4 Three contrasted sociocultural scenarios

The three scenarios presented below describe the possible environments in which MTME might be implemented by 2050, each scenario incorporates different societal values. In this document societal values are considered as the reference framework for values shared by individuals, firms and public authorities that apply them in actions and thoughts.

**The Scenario 1 called "MTM became seamless and optimised"** (section 4.1) is the main sociocultural scenario which corresponds to the **ORCHESTRA's achievement**. It refines the implementation scenario as described in the D2.3 (chapter 6). The main difference with D2.3 is the introduction of the Data Intermediator (DI) as a key stakeholder of MTME's acceptance. **The related shared value is the optimisation** of traffic management inside each mode of transport and in the combination of different modes. It corresponds to a European society in which the mobility of citizens and goods is not questioned because it is seen as a support for economic growth and social development.

Depending on socio-political and economic variables, this main scenario could **evolve and shift in other directions**. Indeed, some of its assumptions are likely to be discussed, even be discarded and replaced by others. Two alternative main trends were focussed during the assumption's identifications.

One alternative direction is given by the **Scenario 2 called "The Tech Giants became the main traffic data providers"** (section 4.2). It emphasizes on the strategic place that the so called "Tech Giants" could take in the MTME, but without fundamentally changing the functionalities of the MTM in Scenario 1. The **future** of MTME is thus implemented in a European society dominated **by the technology of the Tech Giants seen as a source of wellbeing and freedom**.

The other main alternative is **Scenario 3 called "Welcome to a sustainable MTME"** (section 4.3). It suggests a sustainable MTM in which the planet boundaries and the social issues justify all the MTM decisions. This third scenario suggests that a radical change will be occurred by 2050 in the mobility governance in Europe and breaks with the two previous scenarios. It consists of a MTME implemented within a **frugal society, in which strong sustainability requirements always influence MTM decisions**.

These two last alternative trends (**Scenario 2 and Scenario 3**) could be seen as the result of choices made by society, corresponding respectively to various balances between **public regulation, private initiatives and civil society involvements**. Thus, these other two scenarios can be seen as the result of choices that fundamentally change the deployment contexts of the MTM.

**Each of the three scenario is written in a storytelling mode.** This means that **the situation described is the one of 2050 and is described in the present tense**. Intermediate timeframes (2030 or 2040) are specified when needed.

In this Chapter 4 "Three contrasted sociocultural scenario", the following sections and related subsections **contain references to the KPAs which are listed and explained in Chapter 5 "Methodology"** and its related sections and sub-sections.

## 4.1 Scenario 1: MTM became seamless and optimised

In 2050, mobility takes place in a connected environment: People, goods, vehicles and infrastructures are wrapped in digital data. Real-time exchange and processing of this data supports MTM's solutions. The **implementation of ORCHESTRA concepts makes possible traffic management**
<figure><img src="image_16.png" /></figure>

optimisation in all circumstances and makes travel, even through multimodal chains, safer and
with fewer negative externalities (loss of time in traffic jams, missing connections between modes,
land use by infrastructures etc.). Energy and materials consumption, and GHG direct and indirect
emissions remain nevertheless a partially unsolved concern.

The stakeholders (companies, individuals, network managers and public authorities) have understood the value of sharing their data to provide seamless door-to-door transports. In line with the European Strategy for data, the data sharing is facilitated by the **Data Intermediator** (DI) that, whether international, national or local, besides **guaranteeing the respect of data confidentiality**, commercial secrecy, ethical data processing, also guarantees to each stakeholder the use of the data according to their respective choices. The DIs can be national or local and collaborate in a **decentralized framework** that can consider the local issues. A part of this shared data is based on open access. Section 4.1.2.1 better describes the role of the DI.

<figure><img src="image_17.png" /><figcaption>Figure 3: The introduction of the Data Intermediator (DI) actor role in the MTM ecosystem: *The DI as a central stakeholder of the MTME*</figcaption></figure>

The EU has succeeded in curtailing the hegemony of the Tech Giants, which has led to the emergence of a **diversity of private and public operators** around the collection, storage and processing of data needed for MTM.
### 4.1.1 Data sharing as THE basis of the MTME

The MTM needs data sharing to work properly: It fully depends on people, infrastructures, and objects that generate data. In this context, this sub-section draws up a summary of the state of each of the modes of transport in terms of the maturity of data sharing and its projection to 2050.

After presenting the technical developments relating to the exchange of data common to all the modes of transport (Chapter 4.1.1.1), the outlook for 2050 is detailed mode by mode (Chapters 4.1.1.2 to 4.1.1.6).

#### 4.1.1.1 Technical components that cut across the different modes of transport

In the EU, a large majority of NUs are already connected long before 2050. Only a small proportion of NUs remains, either continuously or temporarily, unconnected. By the 2030s, the generalisation of user connections to European transport information networks has become the overwhelming majority. This is the case for road (Directive 2010/40/EU and related Regulations 2013, 2015, 2017 and 2021) and non-road (SES I, II and II+ and Regulations in 2004, 2009 and 2014; ERP Directive 2012/34/EU and Regulations from 2014 to 2022).

At the same time, data science and artificial intelligence (AI) have matured. This level of technological and regulatory deployment makes it possible to envisage efficient, optimised and secure multimodal traffic management (MTM) that is economically profitable to all at the level of the European Union and neighbouring countries. These advances in ITS facilitate multimodal mobility by interfacing, integrating and combining information from each mode of transport.

The air, maritime, and inland waterway sectors already have long experience with information exchange systems (SARPs Annex 10 of ICAO and SERA and the European ATM/ANS IR in the air sector, AIS made mandatory by the IMO since 2004 for the maritime sector and e-navigation initiative, S-200 series of standards, etc).

As computing power increases and communication modes and networks allow for the rapid exchange of large volumes of data, AI supports decision-making, automation, coordination and, in the end, the optimisation of MTM (multimodal traffic management).

This MTM requires a formalised exchange of data in such a way that it can allow, at given GA-scales, the implementation in real time of predefined traffic regulation policies at European, national, and local governance levels. The implementation of a real time and interoperable MTM on a continental scale has therefore required a series of equipment adapted to the exchange of the necessary information (on-board, at infrastructure, and even in the traffic orchestration). The level of networks equipment depends on the level of orchestration expected for each GA (dense urban centre, urban periphery, motorway, dense rural areas or very loose mobility routes, motorway, tunnel, etc.) and on the kind of vehicle/vessels. On-board equipment is now standardised and mandatory by transportation mode and type, as it was in aircrafts and trains for many decades.

The properties of each vehicle or vessel, as well as each transport offer and operation, are acknowledged by the use of unique standardised and electronic certificates. The same applies to each operation or event related to the transport infrastructure. The implementation of such certificates has required the updating or creation of standards.

At the level of each GA, these standardised data, together with the traffic management data of the TO and the TOs of the surrounding GAs (i.e. the GAs linked to the GA concerned), provides an information base for machine learning and algorithm refinement. The use and generalisation of these standards and algorithms allow for the continuous improvement of efficient implementations of
multimodal TDM solutions and optimisation of service offers. At the same time, optimisation involves better balancing of demand and capacity, which is also made possible by data science practices and the use of appropriate AI.

The decision support choices are based on minimising the consequences of risks, i.e. on the effects of negative consequences on the traffic flow. Thus, all the solutions selected offer a very high level of resilience. They ensure traffic fluidity both in normal situations and in the face of abnormal but predictable situations. In the case of unforeseen events, the responses vary according to the nature and scale of the event. However, access to the data associated to the event, as well as data on the traffic situation in general in surrounding networks, and historical data about similar events help the decision support to propose solutions according to the rules and priority rules defined by competent authorities in order to take immediate decisions adapted to the context. The establishment of secure real-time backups of data over a defined period of time is essential.

The infrastructures enabling the semi-automated vehicles (i.e. automated and connected vehicles only under specific and limited conditions, SAE level 3 and 4) are based on safety considerations. Security solutions and protocols for data exchange are defined by standards (CCAM/C-ITS for road, ATM for air, S-200 for sea, ERTMS for train, ...). They guarantee the safety between vehicles and vessels and the safety and security of goods and passengers transported at the scale of all digital applications and the entire communication networks of the ecosystem. Thus, in terms of functionality, the MTM takes advantage of technological solutions that guarantee the security of all the stakeholders in the ecosystem. The MTM is designed so that the most robust technology and the most reliable protocols in terms of data and exchange security can be applied to it in an evolutionary manner.

### 4.1.1.2 Data exchange in air traffic management

In Air Traffic Management (ATM), the European Aeronautical Information Service Database (EAD), implemented and continuously updated by EUROCONTROL, serves as a reference for good anticipatory traffic management at national, community, continental and international levels.

EAD is a centralised reference database of quality-assured aeronautical information that enables users to retrieve and download Aeronautical Information data in real time. It provides instant access to the most up-to-date digital aeronautical information from the ECAC and ECAC+ areas, from Notices to Airmen (NOTAMs), pre-flight information bulletins (PIBs), briefing facility services and the Aeronautical Information Publications (AIP) library. The EAD is the world's largest aeronautical information management (AIM) system.

By 2050, U-space and SWIM (System-wide information management) evolutions are fully deployed, and allow global interoperability of airspace systems, opening the door to a whole range of services (EUROCONTROL 2021, 43) and to new airspace users. Thus, to support the introduction of drones in civil airspace, the UTM<fnref n="1" /> and UAM<fnref n="2" /> concepts have been developed through U-Space CORUS-XUAM and TINDAIR projects, among other things, with requirements for strategic conflict resolution (SCR) and tactical conflict resolution (TCR).

Traffic flows are continuously assessed to ensure that supply and demand capacity are balanced through the Enhanced tactical flow management system (denoted ETSMS – Electronic Time Sheet Management System). Thus, safety, regularity, efficiency of air navigation, and the economy of its ecosystem are ensured. In parallel, the deployment of flight or airport data processing systems such

<footnote n="1">¹ Unmanned aircraft systems Traffic Management</footnote>

<footnote n="2">² Urban Air Mobility</footnote>
as Coflight and DENOT has been generalised on the basis of EUROCONTROL Digital NOTAM specifications. Designed in a dynamic and interactive approach, these systems are based on the global architecture of SWIM, an ATM interconnected network component (Frequentis, 2021). Airport and aeronautical Information as a Service (AIaaS) now have cloud-based airport data sharing methods and a service offering based on SOA principles. They are informed in real time of traffic management events and connect information providers and consumers.

The Single European Sky (SES) initiative, launched in 2004 to improve the overall performance of Air Traffic Management (ATM), is now totally deployed. Completed in 2035, it has already achieved tangible results in terms of cost and safety. The previously fragmented ATM infrastructure is now integrated and streamlined thanks to the SESAR (Single European Sky ATM Research) project (EUROCONTROL 2021, 28), which was the technology pillar of this initiative (European Court of Auditors 2017). The different Functional Airspace Blocks became the core GA unit for air traffic regulation all over Europe.

In terms of cybersecurity, the exchange of data within SWIM is encrypted and ensured using a system of public keys and certificates, as well as local and common KPIs (SESAR Deployment Manager, 2021, 13).

Since 2030, battery-powered vertical take-off air vehicles are introduced. They are operated on the basis of European regulations reserved for helicopters and require dedicated runways or landing and take-off areas (FATO). Similarly, unmanned and remotely piloted drones are subject to almost similar rules, defined by European Aviation Safety Agency (EASA). All these flights are managed by the Standardised European Rules of the Air (SERA) and some national regulations. With a low carrying capacity, their costly use is based on niche markets with high added value (infrastructures' monitoring, emergencies, difficulties of access, luxury, etc.). This is why, in absolute terms, their number remains very limited compared to the total number of vehicles to be managed by the MTM. Nevertheless, the safety and security issues that they generate confines their use to very limited corridors and air ceilings which require careful traffic management.

In 2050, UTM provide integrated traffic management in a standardised manner also for drones and driverless aerial vehicles. These services now allow electric vertical take-off and landing vehicles (eVTOL), unmanned aircraft systems (UAS) and other airspace users (manned and unmanned) to operate safely and efficiently in controlled and integrated airspace, i.e. without impacting on the operations currently managed by ATM.

Nevertheless, these traffic operations are performed in flight corridors in order to limit nuisances (noise, visual, privacy...). More, the widespread deployment of very large fleets of battery-powered vertical take-off air vehicles is still not an option as the load/cost ratio has not changed significantly despite improvements. The management of their flows by the MTM therefore remains easy. However, since their use with a driver requires little space on ground, the costs and impacts in terms of infrastructure are lower than for road, rail or waterways. They are therefore real alternatives for serving particularly sparsely populated areas, or for emergency situations or urgent deliveries. In this field of close-to-ground air travel above dense areas, even with Traffic Flow Management (TFM), safety and security are still an issue because traffic is increasing. Nevertheless, the corridors remain limited in number and by time slots in order to contain the nuisances. In particular because these aerial mobilities, in urban areas, have a real cost in terms of space that should not be externalised on populations without valid public utility recognition.
### 4.1.1.3 Data exchange in road traffic management

In the road sector, solutions inspired by the Traffic Message Channel (RDS-TMC) make it possible to overcome border and language barriers or misunderstandings related to the dissemination of information (CCAM, C-ITS, navigation systems, ...). Transport stakeholders continue to upgrade on a regular basis to ensure their data exchanges, and in particular those with traffic information service operators.

Also, in the field of roads, a quasi-generalization of in-vehicle communication systems has been achieved, mainly as a function of the speed of renewal of both the professional and private car fleets. In terms of automotive connectivity, the deployment of 5G networks even more than the communication devices via the 4G network, facilitates the exchange of data related to functionalities stemming from the Internet of Things. By 2030 all urban areas and major terrestrial transport paths in Europe have, first, uninterrupted 5G coverage and later 5G Advanced and 5G mmWave. By 2040, 6G technology for automated vehicles is only used very occasionally, in specific areas and when necessary.

In 2050, almost all vehicle fleets are made up of connected vehicles, mainly composed by vehicles with driving automation (SAE level 3 and 4) capable of exchanging a great deal of information with other vehicles (V2V) in the vicinity as well as with information networks based on fixed infrastructures (V2I), or with network (V2N), with pedestrian (V2P) or all of them (V2X). However, for such a level of generalisation, deployment times vary according to transport modes and networks.

In terms of the amount of information exchanged and computing capacity, the requirements of the MTM per vehicle are much more limited than those for road safety related to the use of driverless road vehicles in all circumstances (SAE level 5). However, they do require very high-speed data exchange and considerable computing power, especially to be able to integrate different impact assessment criteria and make effective choices in real time. SAE 3 vehicles and above are all capable of communicating with the TOs (including their position and unique identifier). The MTME actors in the whole ecosystem (TSPs, FOs, NUs, ...) have access to part of these data in real time within the framework of pre-established protocols.

### 4.1.1.4 Data exchange in rail traffic management

Rail transport mode had long remained behind the other modes, particularly in terms of interoperability between the European countries. Interoperability has been gradually deployed at a European scale over several decades starting in 1996 with the European Rail Traffic Management System (ERTMS) and from the year 2000 onwards with the Global System for Mobile Communications – Railway (GSM-R) standard.

Before 2050, thanks to technological investments by the rail NMs, FOs and TSPs, the 14 old national standard train control systems in use across the EU have disappeared. ERTMS covers the entire European network on the basis of a generalized deployment of Eurobalise and Euroloop sensors, except on small tourist and local heritage lines.

Thus, the information subsystems and interoperability components deployed on all fleets (on-board) and on the rail network (trackside) comply with all expected standards in terms of safety, reliability and availability, health, environmental protection, technical compatibility and accessibility (European Commission 2019).

By 2050, this leads to increased optimisation of rail network capacity for NMs, improved train regularity for TSPs, and reduced operating costs for FOs. Thus, the modal shares of the freight and passenger rail networks have increased. Moreover, the railway TOs are able to produce new traffic
graphics very quickly in the event of disruptions (e.g. train breakdown) so that the return to normal
is much quicker than before.

4.1.1.5 Data exchange in maritime traffic management

Vessel Traffic Services (VTS) Management and eNavigation as standardised by International Maritime Organization (IMO) have helped to increase awareness, safety and security of commercial shipping. They have also evolved with the increase in the number of certified unmanned vessels (i.e. remotely piloted or assisted vessels or fully autonomous vessels). Of these, by 2050, fully autonomous vessels (at all times without human intervention) remain in the minority, and even more so for large ships or ships operating on risky<fnref n="3" /> sea routes. The navigation of the latter has been made possible by the unmanned vessels' certified reliability. These vessels are equipped with an automated reporting and support system for information integration and decision support and redundant back-up systems. The vessels also have an immediate and full stop capability as well as their own visual and Automatic Identification System (AIS) identifiers. AIS is a ship borne radio data system, exchanging static, dynamic and voyage related vessel data between equipped vessels and between equipped vessels and shore stations (EC, 2021).

In addition, the generalisation of AIS Class A transponders on vessels over 300 tons has increased the efficiency of VTS and the safety of navigation, notably with the help of Automatic Radar Plotting Aid (ARPA). However, ARPA has been strengthened for unmanned autonomous vessels of any tonnage. In addition, the number of persons on board is a dynamic information now transmitted.

In European waters, the rules of navigation for fully autonomous vessels vary according to national
maritime regulations, with some countries forcing these vessels to use their own routes and spaces.
The extensive use of standardised routes and Traffic Separation Schemes (TSS) for conventional
ships, and autonomous routes for the automated ship, now allows for increased traffic predictability
and safety. With more and better information available to ships, the role of the VTS has evolved
towards information integration management. One of their new roles and skills is to move from
resolving ad hoc situations to taking tactical responsibility for planning and avoiding conflicts
upstream (Relling et al. 2022). Indeed, they now manage traffic according to time slots and a variety
of permissions for different situations.

The European Commission had called for the creation of a Common Information Sharing Environment (CISE) (EC Directive 2014/100) to facilitate the safety and management of maritime traffic. Since then, data from VTS, AIS, LRIT, GMDSS and SSAS systems have been integrated and unified, under the leadership of the European Maritime Safety Agency (EMSA), in a single Vessel Traffic Monitoring & Information Systems (VTMIS). At the same time, research platforms initiatives - as CIRMAR - have been set up in port areas to generate vast databases and, beyond that, big data-type processing and learning to facilitate knowledge of maritime traffic, its impacts, its management in real time and the definition of public policies at different scales.

4.1.1.6 Data exchange in river traffic management

By 2050, the River Information Service (RIS) has facilitated the development of and open access to
a harmonised knowledge base for inland navigation (Directive 2005/44/EC). At the European level,
it has been leading the harmonisation of information technologies in the sector and extends with
multimodal management objectives. Modern traffic management systems allow the exchange of data
in real time between vessels and the shore. RIS experts have been working on the continuous

<footnote n="3">³ Varies according to the kind of risk.</footnote>
development of these tools for interoperability, e.g. Vessel Tracking and Tracing System (CESNI/TI/VTT), Electronic Chart Display and Information System for Inland Navigation (CESNI/TI/Inland ECDIS), International Electronic Information System (CESNI/TI/ERI), and Information Exchange System with the Inland Navigation Industry (CESNI/TI/NtS). The RIS data also integrates the CIRMAR platforms.

Similarly, improvements in AIS technology have positively affected the level of safety in inland navigation and became yet another impetus for the creation of solutions enabling (semi)-autonomous sailing.

The period from 2020 to 2050 has mainly consisted of bridging the gaps in RIS deployment between the different river basins and between the different inland navigation companies.

In this perspective, by 2050, and on the scale of the EU territory, only a generalised deployment of semi-automated vehicle type functionalities exists, i.e., respectively, the inland waterway equivalent of SAE road Level 3 in general and SAE level 4 or 5 for particular defined and adapted mobility areas.

### 4.1.1.7 Cyber security

Multimodal traffic management requires an information and data exchange system that is resilient to computer attacks. To achieve this, all the software and hardware components that make up the network must be protected.

The implementation of this protection involved two main areas:

- A quality cybersecurity system with performance levels that are maintained and even improved over time.

- A level of knowledge, understanding and even practice of digital information on the part of all citizens and organisations that can promote the advent of a genuine culture of secure digital exchanges.

During spring 2023, the Commission initiated the first European Cyber Resilience Act (CRA). In the years that followed, security standards for connected objects sold and used in the European Union were defined. The regulation concerned all product manufacturers and the publishers of embedded software. Connected products (hardware and software) should eventually be certified by a trusted third party located in the EU. The requirement to use such a third-party organisation is all the more relevant as it will make it possible to release the authors and publishers of the free and open-source software used with the connected products from any liability. In doing so, this mechanism will ensure that the free and open-source non-commercial community is not put at undue risk.

Practically, the deployment of Cooperative Intelligent Transport Systems (C-ITS) had to prevent any vulnerability to potential cyber-attacks. In terms of V2I exchanges - between connected or automated vehicles and transport infrastructures (such as Roadside Units) - these ITS technologies have relied on encryption techniques (e.g. PKI- Public Key Infrastructure).

### 4.1.2 The conditions for the sharing of data necessary for the MTM

In this Scenario 1, **data sharing** relies not only on efficient technologies but also on voluntary or more or less constrained cooperation of actors. This **requires a broad governance and regulatory framework supported by public authorities**, first and foremost by Europe. However European directives and regulations are not enough: their legislative implementation into the laws of member countries is essential.
4.1.2.1 The Data Intermediator (DI)

With the advent of the MTM, **traffic** on linear transport infrastructures (even those under concession), in territorial waters (or even in the Exclusive Economic Zone (EEZ)) and in Community airspace **is now conditional on the standardised sharing of certain data** by the actors in the ecosystem (TSP, FO, NU, NM, ...). To facilitate MTM operation and efficient management of traffic, data are handled by **Data Intermediators** (i.e. a trusted<fnref n="4" /> “data intermediation services provider” as the Data Governance Act calls for) **who ensure its confidentiality**. To this end, **each of the stakeholders** in the mobility of goods and people wishing to access European transport networks **agrees to share a minimum set of data in real time**, which is used to optimise multimodal traffic management.

As recommended by the Data Governance Act (EC, 2022), the provision of data to TOs, and in particular proprietary data, by stakeholders in the ecosystem – such as TSPs, FOs and NUs - is made possible through the implementation of **certified intermediation service providers: Data intermediators (DIs)** [KPA2-A3]. Gradually, the number of DIs is increasing, and they are increasingly able to provide TOs with access to the data necessary for their traffic management while guaranteeing each of the stakeholder’s ownership and non-disclosure of their confidential data, as required by the general data protection regulation (GDPR) [KPA7-A3]. A local DI may exist at the level of a GA and articulate and communicate with other local DIs of other GAs. The set of local DIs in the same country "interlock" and are coordinated via a data national access points (NAPs) (Commission Delegated Regulation (EU) 2017/1926 of 31 May 2017 supplementing Directive 2010/40/EU on ITS). As data management requires specific skills and data from multiple sources at different spatial scales, the DI and TO are two separate entities at the scale of a given GA [KPA7-A2] & [KPA8-A3].

4.1.2.2 Governance of traffic management data

In 2050, all transport stakeholders produce, possess and share a lot of data related to the mobility of people or freight. This **volume of data has increased** due to the digitalisation of the economy. A **limited amount of this data is shared in an open way**. Most of it comes from public actors, in particular via **organising mobility authorities**<fnref n="5" /> (OMAs) and the NAP for mobility data. The **vast majority of data is private**. It is collected and produced by private actors (manufacturers, TSPs, FOs, OS or application providers) and is much less often shared freely. **Much of it is traded.**

Thus, in terms of data governance and sharing, MTME stakeholders now agree to share data that are useful for efficient traffic management. To this end, the **procedures for using and controlling data have been gradually harmonised**. The interoperability of real-time NU data has **required increasing standardisation and convergence of the data necessary for the quality** data exchange and their multi-mode linking [KPA2-A4]. By 2030, i.e. in the wake of the re-evaluation and review of the European Data Governance Act carried out from late 2025 to 2027, governance and data

<footnote n="4">⁴ “This Regulation should aim to develop further the borderless digital internal market and a human-centric, **trustworthy** and secure data society and economy” [highlighted by us], Data Governance Act, 2022, p. 2.</footnote>

<footnote n="5">⁵ In the spirit of the definition proposed by the Center on Regulation in Europe (Crozet et Coldefy 2021) but extended and adapted beyond MaaS. For these authors, “Mobility goes beyond public transport, it also encompasses all urban mobility methods that require use of public spaces: car use, bicycle, walking, e-scooters, taxi, etc. Public authorities should therefore shift to a more holistic mobility perspective than their traditional public transport focus” and AOMs “must acquire new skills and position themselves as a ‘trusted third party’, one which can incorporate all data into a public platform for all stakeholders, while ensuring equal rights and obligations for all parties” (Ibid., p. 7).</footnote>
exchange had begun to build on the beginnings of a **federated infrastructure through an IMC<fnref n="6" /> test period. By 2050**, this infrastructure has improved significantly in quality and efficiency, facilitating data discovery, collection, editing, interoperability and sharing [KPA2-A2].

The **data access procedures for TOs** and authorities territorially competent in security matters are associated with variable (emergency) levels **depending on the situation**: normal management, management of foreseeable events, management of contingencies.

For this data governance to be as effective and sustainable as possible, **data sharing** must **be understood, not suffered**, and practised consciously and with confidence by the majority of users, if not by all. A minimum base of data to be communicated is defined in the legal texts. Beyond this minimum data base, each NU, and more generally each stakeholder, is constantly in a position to make a choice about the additional past, present and future data it agrees to share. This freedom of choice requires clear sharing rules and, more generally, **the building of a knowledge base, or even a data culture, among EU populations** [KPA2-A5 & A6]. It also requires securing the data of Europeans regarded to foreign operators in order to avoid, as far as possible, situations that help them to exploit Europeans' data without strictly complying with EU regulations and thus help them to benefit from competitive advantages. The existence of such situations already favours the Tech Giants and could lead to partial governance by European stakeholders.

#### 4.1.2.3 Transparent decision-making processes

Transparency is an essential concept for the acceptance of MTM by the TOs. The public is involved, or at least has access at all times, to the decision-making processes of the TOs. Particularly, the implementation of AI to support the decision-making process depends on the explainability and coherence that can be provided to an AI outcome. So coherent Explainable AI systems (XAI) are implemented.

The **TO's decision-making processes are transparent** to stakeholders and the public. The TO never takes decisions autonomously outside of predefined frameworks and priority rules, without the new decision routines having been validated by the competent authorities beforehand. Furthermore, the decision-making processes are tested and approved by the stakeholders of the GA as well as by other involved GAs, such as a neighbouring city. Cooperation between stakeholders is a fact [KPA1-A3].

MTM's **decision-making processes, priority rules, traffic management, are constantly being improved according to codified procedures.** In this way, stakeholders are involved in the evaluation of the TO's activities on an ongoing basis, as part of a continuous learning and improvement process [KPA6-A3].

Similarly, the TO applies **the rules for prioritising the predefined data** to be communicated to the stakeholders as predefined by them. In particular, vehicle priority rules and the advantages they give to some over others are defined locally, between the stakeholders. These priority rules are tested and then defined in advance, in simulated training exercises (using board games and simulators) or in real conditions (full-scale exercises) and thus perfectly adapted to local conditions. The aim is to remain efficient in all circumstances, i.e. to find optimal MTM solutions in the most complex situations, and thus to ensure the **resilience of transport systems**.

<footnote n="6">⁶ Information-Mobility's Common
This project has received funding from the European Union's Horizon 2020 research and innovation
programme under grant agreement No 953618. This document reflects only the author's view and the
Agency is not responsible for any use that may be made of the information it contains.</footnote>
#### 4.1.2.4 Hierarchisation of the MTM criteria according to traffic orchestration strategies

As explained in D2.3 (pp. 32-40), the management of normal operations and of foreseen disturbances include the discovery of the upcoming situation, decisions on how to deal with the situation through demand capacity balancing (DCB), dynamic use of transport mitigating actions to limit the negative effects, direct support to the transport actors (TSP, FO...), and coordination with other network and modes to ensure that they are prepared.

As mentioned before, in this main sociocultural scenario, the MTM performance criteria follow a list of priorities. The strategies are partly made at an international level (EU policy, international rules for sea/air transport, etc.), and partly at a national, regional and local level. A city may for example be allowed to decide that pedestrians and cyclists should have a higher priority than cars in some places.

At the level of each GA, this list is primarily a function of the situations the TO has to deal with (normal operations, foreseeable events, unforeseen events). For each normal and foreseeable situation, there is also an adapted list of predefined priorities to follow.

In this Scenario 1, the criteria are prioritised as follows:

- Transport safety.

- Balancing of greatest number of NUs onto the various transport network. Optimising the capacity of the various transport networks.

- Priority regimes granted to NUs. These are traffic priority rights arising from

o The status of the NU (ambulance, emergency services, police, Persons with Reduced Mobility (PRM), etc.).

o The number of passengers, the volume of goods, the type of vehicle: With lower energy consumption, lower CO2 and air pollutant emissions, the eco-design of the vehicle, for example

o Clientelism: it is possible to pay for priority, as long as it complies with the laws.

- The financial costs of data collection, processing and protection are considered to be outweighed by the benefits to the beneficiaries (private and public). Otherwise, the system is not set up.

- The environmental costs of data collection, processing and protection are irrelevant here, or are not the priority for MTM. The priority consideration of these aspects by MTM stakeholders is discussed in section 4.3 below entitled "Scenario 3: Welcome to a sustainable MTME".

#### 4.1.2.5 Digital transition

By 2050, the digital transition within the traffic management – as for all the other sectors of activity- gives rise to new ways of working. A first step towards 2050 is constituted by the implementation of the Orchestra’s concepts imagining how they can affect the job of the traffic management stakeholders. Some examples are given below.

Within the Innovation Management Board of the Project, some user personas of the operational roles affected by the introduction of the MTME have been developed. In particular, some tasks that are supported by the introduction of MTME have been identified.

For passengers' transport, based on the Malpensa Airport Living Lab, the identified operational roles and related tasks that are affected by MTME were:

- The Airport Duty Manager
* Monitoring flows of people and information within the airport

* Managing flows of people within the airport

* Taking decision about people flows within the airport

* Interacting with handlers, airlines, responsible for Airport Security, responsible for Airport Operations Management Applications

* Using as key tools open data platforms and the airport collaborative decision-making system

* **The Airport Accessibility Manager**

* Ensuring seamless entrance and transits within the airport forecourt

* Operationally managing entrances and transits peaks within the forecourt

* Interacting with Transport Service providers, police, and airport personnel

* Using as key tool the Access Control System

* **The Train Manager**

* Guaranteeing the correct functioning of the train

* Managing the train

* Interacting with transport users

* **The Airport Flow Manager**

* Monitoring flows of flights and information

* Managing flows of flights

* Taking decision about flights from/to the airport

* Interacting with the airport, the airlines, the responsible for airport operations management applications, and network managers

* Using as key tools the airport collaborative decision making and the ECTRL CHMI

For freight transport, based on the Herøya Industry Park (HIP) Living Lab, the operational roles and related tasks that will be affected by MTME identified were:

* **The Road Traffic Centre Operator**

* Opening new traffic lanes

* Defining rules and procedures

* Avoiding network interruptions

* Guaranteeing efficient vehicle flows in the network

* Managing traffic flows within municipalities

* Managing disruptions

* Monitoring areas incident-based

* Interacting with other Road Traffic Centre Operators, municipalities, counties, state roads, and forecasters

* **The Industrial Park Gate Keeper**

* Ensuring a seamless flow of vans/vehicles entering the park

* Guaranteeing an optimised vehicles' entrance

* Interacting with harbour managers, public road administrations, coastal administrations, municipalities around the park, forecasters

* Using as key tools the live status monitoring of the different networks connected to the park

More broadly, by 2050 MTME has developed, or even generated, new kinds of jobs with reference to the following existing professional figures: Digital data managers, data analysts, algorithm designers, cybersecurity and artificial intelligence engineers, data processing lawyers, digital risk
insurers, etc. Consequently, some jobs have been automated, such as: traffic controllers, taxi drivers in some places, last-mile delivery drivers.

### 4.1.3 New services and policies

In this sociocultural main scenario, the exchange of data, as well as the access to them, related to the traffic and its environment (state of the infrastructure, weather conditions, regulatory constraints that apply to traffic, etc.) is perfectly fluid and secure. It then offers TOs the possibility to design and deploy adapted and optimal TDM and DCB solutions.

But beyond traffic management, the data collected and shared also offer the possibility of seeing new transport and mobility services emerge, as well as new policies.

#### 4.1.3.1 New traffic management services

In the event of an unforeseen occurrence that considerably increases the traffic, reduces the capacity of the road network, or even blocks it for a while, the situation is immediately assessed using all available information (digital and human). If the situation requires it, the management and arrival of the emergency services takes priority. Thus, the traffic on the various networks is prioritised to facilitate their arrival on the scene. Consequently, the stakeholders (TSP, FO, NU) cooperate by adapting their offers or routes according to the recommendations of the TO.

In case of a particularly delicate situation, the transport actors as well as the TSPs allow the TO to access a larger amount of their data so that the TO is able to analyse the situation with more precision and to re-evaluate it in real time.

At this stage, the question is: *Can the TO role itself become a market?* A priori yes. In HIP Living Lab case, it could, for example, entrust the role of TO to a private operator to whom it imposes its rules. *Could the same thing be envisaged for Malpensa airport? Could the air traffic controllers be placed under a private company?* Yes, in principle, if we refer to the SES (Single European Sky).

#### 4.1.3.2 New mobility services

In this sociocultural main scenario, a multitude of service offers have emerged, carried by **multiple operators which use the data collected. Some of them have been accredited** by certification bodies, so they can justify that they comply with GDPR.

Service offers based on accessible data are made available to all on certain portals - and thus enter into a give-and-take logic.

Depending on personal needs, individual persons can subscribe to more or less elaborate information services, temporarily or permanently. Individuals have access to public data, like everyone else, but they can also pay for access to specific data. For example, an individual with the role of TSP for travellers in the city can subscribe to information about events that have an impact on mobility: a concert or a sport event that will generate an influx of tens of thousands of people, with the scope of producing adapted transport offers, complementary to pre-existing offers.

Several levels of services are offered: from a basic service that is free and open to all, to services that are only available to those who pay for them.

The service market that exploits the resources offered by the MTM is made up of a multitude of service providers (e.g. Mobility-as-a-Service – MaaS) that are in competition with each other, which stimulates innovation and the matching of services to market needs. However, competition can also lead to arrangements between providers and to the exclusion of some (a MaaS could, for example,
favour one carrier over another, out of interest) [KPA7-A3]. In Scenario 1, the regulation in place allows all service providers to exist and to develop due to the fact that part of the data needed for the MTM is open data. This represents a fundamental difference from the scenario associated with Scenario 2, which leaves the market alone, and it can be assumed that the Tech Giants will offer services based on their privileged partners [see Scenario 2: *The Tech Giants became the main traffic data providers*].

Some examples of transport services born from the MTM data have become reality, by 2050:

- **Platooning in freight:** Lining up trucks with a short distance between them, which can be reduced to 15 metres or even 10, allows the following vehicles to benefit from the speed of the leading truck and save fuel. Each vehicle has automated systems that allow the lead truck to guide the others along the route. The trucks are closer together, taking up less space on the road and reducing traffic jams. Another advantage is that the drivers lose less time in traffic jams. More, in challenging winter weather condition, platooning can prevent safer passage, and avoid jams caused by one truck. Drivers can also do administrative tasks when his truck is taken in charge by the leading truck or even have their mandatory break. This concept also applies to sea-going vessels, particularly in short sea shipping within Europe. FOs benefit from this innovation because of the reduction of energy consumption.

- **Alternative transport services in case of disruptions on a mode:** TSPs/FOs thanks to real-time notifications from the TO can re-plan and arrange for alternative transports, e.g., a train to the airport is delayed and the connection to some flights is compromised. Substitute buses are automatically put into service for the passengers concerned, thanks to the prior coordination agreed between the GA, TSP, FOs concerned). For the connected NU – which has agreed to share its data – this has no financial impact as the stakeholders have set up a re-payment scheme between the defaulting operator and the substitute operator.

- **Services that link MTM and energy resource management:** Traffic management forecasting data can be used to establish local energy consumption scenarios (electric vs. fossil). Flows can then be regulated in real time according to the energy reserves available at a given moment and according to the means of transport used. In the same vein, data about use of capacity in the vehicles could also be used for optimal use of vehicle and, all things considered, generate less traffic volume and less energy consumptions.

#### 4.1.3.3 New transport and mobility policies

By 2050, the sharing of digital traffic data related to the different modes pave the way to produce **new knowledge on mobility and offers new opportunities for the evaluation of public policies.** So, the transport and mobility policies have become more focused, more efficient and above all more intermodal.

Thus, for instance, the collected data make it possible to analyse, forecast and predict mobility behaviour in many different contexts. Within each mode of transport, big data helps to guide infrastructure investments thanks to a more efficient use of the existing infrastructures, particularly through TDM and DCB operated by the TOs. Correlatively, reducing space for roads and carparks makes it possible the design of more pleasant public spaces and cities more liveable. Furthermore, the increased knowledge of the intermodal practices in people as well as in goods transportation, has enhanced the design of the multimodal hubs. Also, intermodal ticketing and incentives for carpooling are introduced. In the same idea, a quota (ration) carbon-based mobility became easy to introduce with an increasing taxation policy based on multi-criteria assessments of the mobility of each NU.
#### **4.1.3.4 The road to the generalized automated vehicle?**

At the time of writing (spring 2023) there is no consensus on the widespread introduction of automated vehicles, even in the distant future. The automated vehicle (AV) is a later stage, and probably much more complex than the connected NU. The conditions to be met are numerous and it is not possible to conclude today that it will be generalised. At most, it can be envisaged in own sites. This is already the case for some public transport (metro, drones). But the presence of AVs in unmarked public space is, in some respects, utopian. The development towards AVs may be an iterative process. New functionality for driver support will be introduced step by step and improved in the following years. The implementation of the AVs may be achieved in many different ways in countries and locations.

**The deployment of the automated vehicles remains to be qualified.** Indeed, given the complexity of the socio-technical systems specific to automated vehicles, the sensitivity of the subject in terms of acceptability, the volume of the necessary investments and the costs involved, the implementation of fully automated vehicles (SAE level 5) capable of driving on all types of public roads and in all circumstances, the generalisation of the automated vehicle cannot reasonably be envisaged [KPA3-A3].

Technological advances in this field have nevertheless made it possible to implement such mobility solutions on their own specific sites or within the limits of particularly well-defined and adapted areas or routes. Thus, on the scale of port or airport infrastructures, and more generally on the scale of logistics or passenger hubs, such solutions are deployed with the aim of facilitating intermodality and taking full advantage of certain modal shifts via the multimodal platform available.

The rate of introduction of automated vehicles depends on the rate of renewal of the fleets. For example, in the rail sector, trains are purchased for a fifty-years life span. As the trains available on the market today are not autonomous, it is not possible to envisage the widespread introduction of autonomous trains by 2050. It would therefore be necessary to retrofit selected trains in circulation. The selection of the trains to be retrofitted also depends on the equipment of the infrastructure: not all the railways are suitable for the circulation of these trains. This can only be envisaged on a limited part of the rail network by 2050.

### **4.1.4 Focus on the Living Labs**

This section focuses on the deployment of the MTME concept as they could be implemented, within the framework of the Scenario 1, to HIP and Malpensa Living Labs in 2030 and then in generic European industrial-port and airport areas (sea-land and air-land hub-infrastructures) by 2030 – 2050 horizons.

The Living Labs' team has hypothesized a first step towards the building of a MTME by implementing it in simulated scenarios and contextualized in the HIP and Malpensa airport Living Labs, thanks to new operational and simulation tools developed to meet the ORCHESTRA concepts. It mainly relates to data sharing between service providers and GA authority in the freight transport ecosystem to, inside and from HIP, and data sharing between service providers in the passenger transport ecosystem to, through and from Malpensa airport. Thus, imagining them as evolutions arising from the experiences gained thanks to ORCHESTRA.

#### **4.1.4.1 Focus on an industrial area such as HIP**

This section provides a focus on the MTM context of an industrial park developed around a port, such as the Herøya Industrial Park (HIP). The traffic management is carried out internal to industrial park
taken as a GA, including the private ports, in coordination with the surroundings GA. Traffic is managed in an intermodal way involving sea/road/rail freight traffic.

## Experience from HIP LL

By 2030, the implementation of the automated **pilot car** to guide the trucks inside HIP is fully operational. On the request of the truck driver, the pilot car goes and picks up the truck at the port entrance. When the carrier has given his authorisation to share the data relating to the goods to be delivered (nature of the goods, time constraints, possible handling precautions, destination, etc.), the pilot car takes this into account and warns the handling services or the shipper concerned. Advance ordering has become possible in recent years. If needed, the truck driver has requested the automated pilot car before arrival. This may be useful for just-in-time goods deliveries.

HIP is privately owned. It performs the role of TO, and therefore certified by the European or Norwegian accreditation bodies. The exchange of data is carried out in compliance with public rules. HIP TO coordinates with the roads TOs (using information from the NPRA via Datex format) and the seaside TO. Data exchanged can be roadworks, accidents, traffic cameras and weather stations.

Among the data intermediation services that have developed in Norway since the data governance Act, the data exchange through the stakeholders involved in HIP traffic management is warranted by a national platform. This platform is the "Transport portal": https://transportportal.atlas.vegvesen.no/en/.

Supported and financed by Norway government, even before the Data Governance Act, the platform is responsible for collecting and managing data for public transport and micro-mobility, including data about parking, charging, on-demand transport. Initially, the platform did not collect data about traffic on the transport infrastructures. But, because of the need of data exchange between the stakeholders of MTME, this platform has taken steps to broaden the scope of the data collected to all modes of transport, particularly those necessary for traffic management within the industrial-port area. All the data collected is open. So that the TO can access to all the needed data, in respect of the data protection laws (e.g. GDPR). It can access data from the industrial park GA area as well as data from the others GA with which traffic flows are in connection, e.g., the connected dry ports.

Several public bodies entrusted this data intermediation service: All the public network managers and traffic managers, the public maps service providers, the public digital transport network managers, the regulation and standardisation bodies, and the weather forecast bodies.

## MTM industrial parks ecosystem in Europe by 2050

By 2040 or later, all movements within the industrial port areas are carried out in a totally digitalized environment: all vehicles circulating within the industrial-port area are connected or even automated. Individuals circulating in the industrial port area (employees, visitors, service providers, maintenance workers, etc.) are also asked to share some of their personal data for security purposes (their location, possible disabilities, etc.). This, in case they use a car or even if they are pedestrians or use bicycles allows the TO to perfectly manage the risks in terms of both safety and security. Automated vehicles are also used to guide and transport people in case of evacuation.

The data intermediators became numerous all over Europe. Not only public entities but also private organisations entrusted this data intermediation service: FO, TSP (Truck, train, ship, i.e. all goods deliverers), shippers/factories, TOs, society/people around the industrial park because they are interested in what goes on and they need to know particularly about security issues related to the industry.
Thanks to the access to the data, TSP – ships as well as trucks and trains – are able to correct prediction of arrival/departure times at the industrial park. This imply that infrastructure capacity and related hazards/risks can be forecasted. Therefore, the industrial park TO is able to give priorities according to goods, logistics constraints, and weather conditions (some goods can only be loaded when not raining or snowing). Furthermore, disruptive situations in each transport mode can be managed easily.

### 4.1.4.2 Focus on an airport use case

- **Experience from Malpensa LL**

The introduction of MTME affects the scenario developed mainly by opening new communication channels and data exchange between all the actors involved in traffic management to and from the airport. Within the simulated scenario for instance, a train disruption can easily be managed thanks to the emerging figure of the TO, by rerouting the disrupted passengers based on prioritisations deriving from the time-constrained flight departure times. Exchanging real-time data and projecting people flows in defined time ranges, gives the airport TO the possibility to start taking preventive actions to assist the disrupted passengers. This includes keeping free the most suitable parking slots for the substitutive buses and organising a prioritised path for the passengers through the airport. When possible, opening new security check lanes will help the process of checking passengers' baggage, smoothing operations and ensuring no delays are accumulated due to a disruption in another network (e.g., the rail network), or ensuring that no passengers will lose their flights, provoking an environmental damage by keeping empty airplanes' seats. Concurrently, in the second simulated situation, the actors are affected by the opening of communication channels and data exchange through the MTME. Within the second scenario, the TO initiates communication with the TSPs associated with the airport to verify the availability of transportation options. The purpose of this effort is to aid a group of passengers who were negatively impacted by a disrupted flight landing at the airport at the untimely hour of 02:00 AM, rendering them unable to reach Milan city centre. In this manner, a customized public transportation plan is devised to optimize the conveyance of the passengers from the Airport to Milan.

- **MTM Airport Ecosystem in Europe by 2050**

As major passenger and freight traffic hubs, European airports have adapted to the changes brought about by various European directives, including the TEN-T directive. The European global network is now complete, offering efficient freight and passenger connectivity on a continental scale.

In parallel with functional redevelopment, airport areas continue to develop their service offerings. Changes in air transport conditions (end of short-haul flights) have pushed the main European airports to develop their transport offer and to become true multimodal hubs better connected to different scales of territory (local, regional, international). The MTM supports these strategies by facilitating and optimizing the management of incoming traffic, which passes through these particular GA hubs, air-land interfaces.

These hubs remain privileged points of passage for long-haul intercontinental travel. Medium-haul distances in Europe are now also largely covered by rail lines (high-speed network for passengers and freight corridors) in addition to their older road (freight) and air (passenger) connectivity. This is why major European airports have intensified the development of air-rail interconnections with high-speed rail lines. Policies to decarbonize transportation, improve accessibility to these hubs,
concentrate activities (ZNLA<fnref n="7" />, etc.) and consult with stakeholders in the host and service areas have **strengthened their integration into urban and peri-urban areas**. In the same way, the multimodal and multiscale diversification of transport services has broadened the spectrum of customers, **reinforcing the interest and attractiveness of such structures**.

Around the issue of data sharing necessary for the proper functioning of the MTM, the concerned **stakeholders are now voluntarily coming together to entrust a single entity** with the capacity to ensure the intermediation of their data (open, public and private) necessary for the orchestration of traffic in **compliance with the GDPR, intellectual property and commercial data confidentiality** and with the necessary **security guarantees**.

The management of this new-born local data intermediation structure is sometimes delegated to the authority in charge of administering **the national one-stop shop for digital data**. In a first phase, this local new-born entity finds its financial support thanks to the ecosystem of actors based on a four-year commitment. During this time, the MTM airport actor's ecosystem is also responsible for evaluating and negotiating the future financial arrangements. Many **stakeholders are involved in the governance of the data sharing**: Public Authority (Municipality, Transport Ministry, Regional Transport Authorities, Civil Aviation Authority – CAA, etc), all the other Transport Stakeholders: Airports, Airlines, ANSPs, Railways Infrastructure Manager, Railways Service Providers, Road Authorities, Public Transport Organisation, Car-sharing companies, Ports, Shipping Companies, etc.

Initially, and to increase knowledge discovery via contextual data analysis, this structure is working on a finalized **formatting of the data provided by the stakeholders by combining integration and aggregation procedures**. The **new-born local entity** also integrates the data from the airport's internal sensors in a **logic of fusion that facilitates the development of a decision support AI**. Thus matched, all these data can be merged because of shared attributes and variables. Nevertheless, the whole source of each data typology is kept separate.

Based on an annual agreement between the stakeholders, this experimentation is annually re-evaluated during the first years of its implementation. Originally based on multi-criteria analyses, the evaluation criteria gradually evolved. After a few years, they are implemented according to a more advanced cost-benefit analysis (CBA) method recommended by the OECD (2018). Over the years, the CBA is completed according to the quality of the data shared.

During normal operation of these **multimodal-air platforms**, the TOs of the different GAs (airspace, airport airside, roadside, and railway space) are committed to inform the TO of the multimodal transfer node (airport landside: TOs, role often played by the accessibility manager) in real time of current and upcoming arrivals and departures from their respective GAs. Informed of the destinations of the inbound and outbound travellers and their position, the TOs redispatches the information to the other modal TOs.

This **new type of information sharing combines data** from air, road and rail modes of transport, and even from active mobility. It brings together data from all flights operating to and from airport (long distance and medium range flights and drones), as well as data from the rail network and the companies operating on the lines (high speed Intercity, Eurocity, InterRégio, NightLine and freight trains, TLI, tramway...), road TSPs (cabs and VTCs, buses, carpooling, etc.) FO and NU who agree to share their data in real time.

<footnote n="7">⁷ Zero net land artificialisation, idea inspired by the ZAN concept of the French "climate resilience" law (promulgated on 22 August 2022</footnote>
As a result, thanks to this new type of information sharing concept, in normal or planned operation, **all connected NUs** (vehicles, passengers, airport workers, etc.) can know in real time the evolution of the **estimated or measured time needed** from a location and a date-time to reach the different stages of their journey with regard to the characteristics of their departure or arrival flight or destination locations. The **actors of the transport offer** (TSP, FO, ...) also have **upstream and real time knowledge of the volumes of travellers** by origin-destination-time. They can therefore re-evaluate and adapt their service offers, notably according to demand (TDM) but also, for some, according to the capacity of the networks in order to seek to readapt their offer (DCB).

At **this time of 2030**, and during a degraded operation linked to an unforeseen event particularly impacting on traffic, the uncertainty factors increases. They depend on the nature of the event(s). In the case a decision support functional AI is adopted, the AI is able to propose choices and solutions that will be validated by the competent authorities. Other situations, probably very rare, can occur in which the AI is out of order.

After this first implementation phase, **the service gradually improves with time** by integrating a series of additional information that can specify the traffic and the travel time: number and nature of passengers (age, possible handicap...) traveling together, congestion (number and weight-size of luggage...), traffic preferences (mode, position, speed, environment...).

With the implementation of such a functioning and processed data, and in a horizon **between 2030 and 2040**, the DI develops the continuous statistical learning of an AI. This AI, of the reinforcement learning type based on a kind of Dijkstra-type algorithm, relies on a priority queue clearly defined by the SPM<fnref n="8" /> and on fluidity indices based on predefined passage points (entry points, cf. *Airport transfer node example*). Therefore, the increase in the volume of data over time - day after day, week after week, season after season, year after year - refines the quality of decision support and optimization of traffic management.

In this perspective, and on the condition that measures and protocols can guarantee the security of the data, we can assume that **by 2050**, the MXP LL experience developed by the ORCHESTRA Project in 2023/2024 will constitute, along with other experiences, one of the initial examples of the implementation of a European-wide MTME.

## 4.2 Scenario 2: The Tech Giants became the main traffic data providers

In this scenario to 2050, the **data relating to the traffic** circulating on the various transport infrastructures are **mostly collected and processed by the Tech Giants**. Similarly, servers, storage and digital data exchange infrastructures are mostly owned by Tech Giants. This is a continuation of the basic trend of data technological progress observed since the early 2000s. Since then, the Tech Giants have been investing in hardware and software at a much higher rate than the European public authorities and firms have been able to do. Even if the states and the European Union have tried to curb this supremacy, they have only partially succeeded in containing their hegemony. Thus, **the economic framework of traffic data exchange became an oligopoly at the hands of Tech Giants**.

Thus, the Tech Giants have become key providers of mobility, transport and traffic data. They also collect a great amount of personal data from several personal devices. **Data** are often stored outside

<footnote n="8">⁸ Priorities predefined in consultation with the PPs and in accordance with the relevant territorial public policies. The order of priority of this queue is a function of the 3 modes of operation (normal, planned events, unforeseen events) and a variety of sub-modes depending on the nature of the event.</footnote>
the EU, therefore **not enough subject to data protection European law**. The main differences with Scenario 1 are that the **traffic data is more centralized**, and the **Data Intermediators (DIs)** are **not able to manage all the required data for MTM** because of Tech Giants domination.

The MTM **stakeholders** (TO, TSP, NU, FO) have to **negotiate with the Tech Giants for providing MTM solutions** that involve the handling of huge volume of data in real time. Thus, the access to most traffic data is subject to a fee. So, only a few cities and large centres have been able to develop MTM, reinforcing territorial inequalities. In this context environmental footprint of MTM stayed out of scope.

<figure><img src="image_18.png" /><figcaption>Figure 4: The DI in an MTME dominated by Big Tech companies</figcaption></figure>

**Tech Giants** deploy their conglomerate by positioning some of their companies (created or acquired) as operating the roles of FO, TSP or SP related to mobility. **Present on all the value and service chains,** they have comparative advantages, which allow them to further strengthen their data mastery. As a result, the competitiveness of their brands is reinforced **at the expense of a more balanced ecosystem.**

### 4.2.1 The underlying values of this sociocultural scenario.

By 2050, the ideology that had prevailed at the advent of the Tech Giants in Silicon Valley (GAFAM and NATU as they were called in the early 2020s), has spread widely around the world: an ideology
that 'combines the independent spirit of the hippies and the entrepreneurial zeal of the yuppies' (Barbrook, Cameron; 2000). Correlatively, this vision, which places great ambitions in digital technology, has been easily associated with a **libertarian vision of society**, which tends to emancipate itself from the state framework and to maintain that the market regulates itself and that public authorities should intervene as little as possible.

This hegemony can be explained by the high level of capitalisation that characterises the digital industry. **The digital economy is indeed particularly conducive to the emergence of economies of scale and network effects.** On the one hand, the production of digital products or services often has large fixed costs, which are reduced as the quantities produced increase. On the other hand, the consumption of these products and services often induces network effects, as consumers are attracted to a company that will provide access to a large network, thereby strengthening its market position. The concentration of markets that is common in the digital field often leads to a "winner-takes-all" phenomenon, where the first company that manages to position itself and distinguish itself acquires a dominant position. In addition to these dynamics, there is another related trend, namely strong conglomerate strategies in sectors related or not to the initial activity. This diversification takes the form of the development of new products and services, but also the acquisition of companies, particularly start-ups, and leads to the creation of veritable "ecosystems" aimed at controlling the entire value chain, and more particularly the elements with the highest benefit.

An example of this is Google, which has pioneered **hyperscale computing**, thanks to the "largest network of computers on Earth" (Borker, 2018). In 2050, this company and a few other Tech Giants (those from Silicon Valley but also those that have emerged on other continents, including Europe) have been able to maintain their dominance. Indeed, they have remained unrivalled in their ability to invest in data collection, storage and transport infrastructures and to concentrate innovation capacities, particularly in artificial intelligence, by attracting talent from all over the world thanks to large research and development budgets. **Machine intelligence experts have been able to apply their genius to the knowledge of the human experience by transforming it into digital data.** This data has become a real source of wealth through the creation of services, services that can predict, influence and sometimes control behaviour. These services are mainly developed based on continuously collected personal data, and are the foundation of a "surveillance capitalism, defined as a new form of capitalism that translates human experience into behavioural data in order to produce predictions that are then sold on the markets of future behaviour" (Zuboff, 2019).

The development of surveillance capitalism has taken place in Europe with periods of **conflict between Tech Giants and public authorities** (European Union, member countries and local authorities). Thus, the public authorities have tried to reduce the impact of Tech Giants on the economy of the countries, but regulation has always been an afterthought, as technological and service innovations have been so rapid. Moreover, in their relations with states, the digital giants have deployed considerable resources in lobbying actions to defend their interests in the process of drawing up public policies, particularly in terms of data exchange, standardisation and business regulation.

These solutions to these conflicts have made it possible to partially containing the supremacy of the Tech Giants in Europe. However, despite the entry into force of the General Data Protection Regulation (GDPR) in 2016, **the collection of personal data in 2050 continues to be based on complex and partially opaque techniques** that create significant barriers for users in controlling their data.

The resulting **oligopoly of Tech Giants has taken control of the data market in 2050**, in several sectors of activity. As a result, the conditions for competition in the data market in Europe have been severely limited. Moreover, as intermediaries, such platforms have taken on the role of gatekeepers
and controlled users' access to digital content, products and services. This new form of capitalism has led to the further disempowerment of individuals as Zuboff (2019) points out. This disempowerment of individuals is particularly evident in the environmental footprint of their journeys and the data attached to them.

Thus, in 2050, the actual deployment of the MTM could take place according to the concepts foreseen by ORCHESTRA. However, while the data exchange technologies and the functionalities of the MTM described in Scenario 1 remain the same, the conditions under which they are performed in Scenario 2 are significantly different. In this context, data relating to mobility, transport and traffic on the infrastructures of the various modes of transport have not escaped the hegemony of the Tech Giants, which hold most traffic data - and more broadly mobility data - and trade in it.

These differences are explained in the following text in relationship to the KPAs and the related assumptions.

### 4.2.2 Addictive and costly policies

The low consideration of the environment by the Tech Giants has led to a consumption of the digital without real questioning of the modes of production and consumption of digital goods and services. Although digital technology may have been a vector for improvement and progress, it has remained a source of negative environmental impacts. Thus, the society of 2050 has only partially succeeded in responding to the guidelines of energy control, control of carbon impact set out by the IPCC and the Green Deal, and control of the consumption of natural resources [KPA1-A2].

The traffic management criteria defined in Scenario 1 (see section 4.1.2.4) have remained the same and give priority to the search for safety and traffic fluidity. However, the authorities, at all territorial levels, are permanently negotiating with the Tech Giants -as solution providers- so that the latter consider traffic regulation policies in the digital services they deploy [KPA1-A3] & [KPA7-A2].

### 4.2.3 A traffic data market under a Big Tech straitjacket

Europe has succeeded in establishing its own sovereign cloud for the management of personal and corporate data based on the principles of interoperability, transparency and trust. The digital strategy has been financed by Europe, through the Digital Europe Programme. However, Europe's digital sovereignty has been limited by the huge investments that the Tech Giants have been able to deploy [KPA2-A2].

In this scenario, the existence of DI for data exchange is more relevant than ever. Indeed, the partly opaque collection of personal data by the Tech Giants has made MTM stakeholders reluctant to share some of their data. Exacerbated by the practices of some unscrupulous Tech Giants, stakeholders are demanding stronger guarantees of privacy for the data they share. The DI is therefore exercising its role as guarantor of the GDPR on a small part of the data shared freely or imposed by states because it is data of general interest. But most of it remains under the control of the Tech Giants who trade in it.

The European Directives have succeeded in preserving a minimum base of data necessary for open data MTM. This allows operators to produce a minimum traffic management service in all circumstances, including unforeseen events. However, whenever additional data is required to make forecasts or find solutions to complex traffic management situations, authorities, private operators or users are now obliged to contract with Tech Giants to obtain the necessary data [KPA2-A3].

The exchange of data between the MTM actors takes place in standardised and interoperable formats. Europe has established a metadata dictionary in a structured way. It defines a minimum set of
common metadata, including their description, types and obligation levels. It takes into account all data and information domains of the EU Directive and the respective delegated regulations. It covers multimodal travel data and services, in line with Delegated Regulation (EU) No 2017/1926. The difference with Scenario 1 is that these standards have been partly influenced by the Tech Giants [KPA2-A4] & [KPA3-A4].

### 4.2.4 Technologies with systemic impacts that raise questions

Bill Gates' prophecy has come true: “I believe we'll reach a tipping point with autonomous vehicles within the next decade” <fnref n="9" />. Indeed, the deployment of connected and autonomous vehicles has taken place thanks to the technological advances made by the Tech Giants and the partnerships they have been able to forge with car manufacturers and even with manufacturers of other modes of transport (rail, river, sea and air). For reasons of liability and safety, authorisations for the use of these driverless vehicles have been restricted in Europe to private areas or dedicated sites [KPA3-A3].

AI has developed in Europe thanks to private R&D investments and public support schemes. However, from the beginning of the 2020s, the first developments quickly raised ethical questions, which led to the creation of an *ad-hoc* governance at the international level in order to establish common guidelines and promote the responsible use of artificial intelligence by bringing together experts from industry, civil society, governments and academia. Here again, **the Tech Giants took part in the discussion on these common standards.** Consequently, unlike some foreign states, Europe wished to retain a maximum level of human intervention in the design of AI and in the control of its products [KPA4-A4].

The issue of the Tech Giants' involvement in the governance structures of the MTM has been a difficulty of the acceptation of the MTM. This has led to a transformation of the relations and modes of coordination between public and private institutions and has opened the possibility of a shared regulation of traffic between public authorities, network managers and the digital service company. Thus, in 2050, public authorities participate in the determination of the services produced by these Tech Giants in a direct or indirect way.

Thus, for instance, the involvement of stakeholders in the governance of the TOs was a difficult issue in this scenario. Indeed, the decisive role of the Tech Giants in the provision and exchange of data raised difficulties regarding the quality and control of data handling. On the one hand, the actors in charge of the MTM wanted to keep full control over the prerogatives and improvements of the TOs, including at the local level. On the other hand, the Tech Giants sought to standardise - on a global scale - the algorithms and data needed for MTM and to offer their own MTM services. To maintain state sovereignty, the Tech Giants' ambitions have been contained in Europe, but they have maintained a significant influence on TOs, if only through the data they make available and the corresponding business models. In some places, subsidiaries of the Tech Giants perform traffic management on the behalf of the TOs [KPA6-A3] & [KPA7-A2] & [KPA7-A3] & [KPA8-A3].

### 4.2.5 External costs socialisation generates inequalities and imbalances

Acceptance of MTM and autonomy mainly depends on informing users about the financial, environmental and social criteria. In this future dominated by Tech Giants, the gains brought by the

<footnote n="9">⁹ Bill Gates' blog, March 29, 2023: https://www.gatesnotes.com/Autonomous-Vehicles?WT.mc_id=20230330100000_Autonomous-Vehicles_BGTW_&WT.tsrc=BGTW&utm_source=newsletter&utm_medium/email&utm_campaign=newsletter_axioswhatsnext&stream=science</footnote>
MTM in terms of costs and time are clearly put forward. But those relating to the environment or to social well-being, which are further from the values they carry, are incomplete and remain the subject of controversy [KPA5-A4].

Even more than in Scenario 1, the society of Scenario 2 is witnessing profound changes in the world of work. Tech Giants implement a lot of automation solutions in place of many repetitive tasks that lighten the workload of the traffic management. In this business sector as in many others, the replacement of human work by automation is almost always possible from their point of view [KPA6-A4]. Thus, one can see:

- an increase in the number of highly skilled programming-related jobs.

- an increase in very low-skilled jobs, the labourers who will execute the orders given by the algorithms written by the previous ones.

- a decrease in intermediate occupations.

The added value offered by Tech Giants is further enhanced by the fact that the cross-referencing with other data collected from individuals or companies, or relating to the environment of transport infrastructures, allows them to carry out advanced analyses and to propose personalised solutions.

In this scenario, civil liabilities have become complex as they often involve both public and private stakeholders. This has resulted in higher insurance costs [KPA7-A1] & [KPA7-A4].

### 4.3 Scenario 3: Welcome to a sustainable MTME

In this third scenario, the **MTM** is **considered as one of the tools which contributes to maintain** estimated **global warming below +2°C** in accordance with the 2015 Paris agreements in line with the implementation of the IPCC Shared Socio-economic Pathways (SSP) 1-1,9 scenario it correspond to a very low level of GHG emissions (IPCC 2022).

In line with the IPCC recommendations methodology (Shukla et al. 2022), the approach uses an **"avoid-shift-improve" method** (A-S-I):

- **Avoid** certain practices and types of **consumption** considered **too costly** and no longer adapted to the challenges.

- **Shift** from a degenerative economy paradigm **to appeased and more inclusive** one. Practices that retain a majority of uses but expressed in a less conflictual and more sober manner. This translates into more shared transport practices, less emissive modes and solutions, and the considering of real costs (i.e. also external) in order **to satisfy society more equitably**.

- **Improve** to support the demand, but with more **justice, more appropriate techniques** and **technology**.

To reach the 2050 horizon in such circumstances, it is **necessary to accept certain constraints** and to evolve under certain conditions. This is why, public policies are thought in a **political economy of frugality framework** with references such the **Planetary boundaries** (Rockström et al. 2009) and **Social limits** (UN SDGs, 2015). The Planetary boundaries constitute a scientific framework to describe the limits of the impact of human activities on the Earth system **beyond which the risks of systemic runaway** and positive feedback put the future of our societies in **serious danger**. While the Social limits refer to the **social floor**, i.e. the **minimum social levels** to be considered to ensure the **well-being** of everyone and the future of a **shared prosperity** (Raworth 2012, Laurent 2012, 2017 and 2022). As a result, the notion of **wealth is redefined** on the basis of a **new paradigm** that is very
different from the prism of an inadequate GDP<fnref n="10" /> for assessing real wealth (Kallis et al. 2022). **GDP** and its associated logic of **unlimited growth are abandoned**. The economy is no longer perceived as a competition but rather as a co-construction or even a cooperation.

<figure><img src="image_19.png" /><figcaption>Figure 5: The DI within an MTME with considerations of sustainability and digital frugality at its core</figcaption></figure>

In this perspective, **MTM and mobility measures** are always implemented **in the light of life-cycle analysis and demanding multi-criteria assessment in terms of people's well-being**. Particularly, decisions consider the costs in terms of resources, materials, energy, space, and negative externalities (CO₂, biochemical cycles disruptions, fine particles, ...). This is why some **technologies** became banned when they were too environmentally aberrant, otherwise they are **taxed according to the level of direct and indirect environmental costs**. More, **data primarily serve the politics of well-being and shared prosperity**, not individual or corporate capital. At the same time, the risk of unjust economic domination is excluded. Furthermore, **MTM data are managed through the concept of Commons** (Ostrom 1990).

<footnote n="10">¹⁰ GDP which currently includes in its evaluation of "wealth" both the production and destruction of war as well as the production of cigarettes and the entire system of care for cancers linked to cigarette consumption, but which ignores the virtues of a tree in terms of biodiversity, soil quality, shade and coolness, CO2 storage, etc.</footnote>

<footnote>This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 953618. This document reflects only the author's view and the Agency is not responsible for any use that may be made of the information it contains.</footnote>
In this society, to achieve social and spatial justice, each **citizen** gets an **annual "mobility credit"** defined in a concerted and transparent manner. **DIs are trusted third parties.** They collect and harmonise all the data supporting LCA and social assessments, so that the **TO is able to carry out all the assessments and operate MTM decisions that fits with environmental and social issues.**

### 4.3.1 Fair and ecological policy and governance drive by combined, multimodal and mixed transport

Thus, the implementation of EU **Green Deal policy has been highly successful** by surpassing initial expectations, **as well as** the advent of the **European Sustainability Reporting Standards (ESRSs)** required by the Corporate Sustainability Reporting Directive (CSRD's EU directive 2022/2464) [KPA1-A2]. These policies have been supported by EU **Green Public Procurement (GPP)** for different areas of transport, which has been a powerful stimulus for eco-innovation [KPA1-A4] & [KPA6-A4].

**Transport and mobility are considered from a socio-spatial justice perspective, in which sustainability and frugality are always preliminary to policy measures** (including technological and digital ones), so also to the MTM. This means that before being allowed to be deployed on any public space and device, the **MTM must demonstrate upstream** that what it will bring in terms of **wealth is greater than the global costs** it will generate as a whole for the society and the future generations [KPA3-A5]. Sustainability and the sharing of prosperity come at the cost of this frugality (Parrique 2019 and Hickel 2021).

Thus, **transport systems and modes, as well as mobility, are considered** from the point of view of all the impacts of their life cycle as well as all their benefits in terms of well-being for the populations concerned (i.e. **multi-criteria evaluation including LCA** and not just a cost-benefit analyse) [KPA1-A2] & [KPA4-A6].

From there, the **non-carbon energy infrastructures deployed** can efficiently support the MTM. In this perspective, aware of the need not only to **optimise the efficiency of mobility** by using the MTM but also to **limit the costs of the real impacts of trips**, a large majority of **MTME stakeholders** decided to **join forces** in order to propose a traffic management ecosystem that **supports human and planetary conservation policies**.

Data exchange and processing technologies as well as transport technologies are **energy and material efficient and social and fully respectful of social welfare**. In the EU, stakeholders play the game regarding the harmonization of data enforcement and controls. **The rules for sharing data are clear and trustful.** The governance rules allow that at any time NUs (or their owners) can easily make a choice about their past, present and future personal data [KPA2-A6].

Nevertheless, the selected MTM solutions still offer a high resilience to hazards and risks. In this scenario, society reorganises itself spatially to reduce the number of trips. The administration of the MTM by traffic orchestrators (TOs) incorporates **technologies and functionalities that can assess the environmental impact of each trip at any time**, according to an ambitious sustainability standard [KPA4-A6]. European transport policy actively promotes multimodality and combined (freight-passenger) transport for the purpose of optimisation and the ambitious ecological transition. Traffic can be reduced by significantly reducing the number of individual trips in oversized vehicles and offer comfortable, adapted transport connections. Goods transport is optimised, resulting in significantly less traffic and better distribution. **Rebound effects are rare and limited,** mainly due to paradigm shifts and changing perceptions and attitudes.
The adhesion of populations to this dynamic of change have required fairer, **more incentive-based and inclusive policies**, as well as quality and continuous **education and information capable of empowering** the greatest number. Wherever possible, internalisation of the true costs of externalities is required.

In order to **achieve and win the support of citizen-users** for this vision and to help implement it in an efficient and sustainable way, the authorities in Europe have acquired a new tool [KPA1-A5] & [KPA5-A4].

### 4.3.2 Traffic management data have become commons

With the advent of the MTM within the European area of freedom, security and justice (AFSJ) (Article 67 of the TFEU), **traffic is now conditional on the standardised sharing of a set of data** (static infrastructure data, dynamic infrastructure data, traffic data, meteorological data, impact data, etc.) and **acquires the status of a European common good** (Ostrom 1990) under public guarantee (Bollier, 2013, Larroche, Peyrelong et al. 2018) and is managed by **DIs trusted third parties who ensure its confidentiality** [KPA2-A3]. To this end, **each of the stakeholders** in the mobility of goods and people wishing to access European transport networks **agrees to share a defined set of data in real time**, which is used to optimise traffic management and to evaluation movement costs to each NU.

In the public space, and in certain predefined private spaces (such as ports or airports, if necessary), **free movement is associated with the constitution of a European traffic and transport data commons (IMCtt)**, itself part of an Information-Mobility's Common – IMC [KPA1-A4]. These commons of data guarantee citizens against the risks of an oligopoly of mobility offers based on excessive data domination and exploitation on the basis of exclusive rights (see section 4.2 above: Scenario 2, p. 40).

In this perspective of constituting traffic and transport data set as a common good (IMCtt), the EU and its partners legislate, regulate and standardise to **define rules and common pools of data** by category of actors necessary for the realisation of the MTM (for the functioning of TO's priority programs). In this way, all modes of transport are considered, facilitating the implementation of the **ambitious European policy** on combined and multimodal transport, which can be **appropriated at local level** [KPA7-A2] & [KPA8 A3].

This work of setting up an IMCtt is based on the participation of all stakeholders (including users) in defining the governance of mobility. Among them, civil society actors are more aware of, or even more knowledgeable about, the issues, needs and effects of mobility data sharing. Thus, the **collaborative involvement of stakeholders** and the associated advocacy **foster new perceptions and representations** (Godenhjelm and Johanson, 2018). In terms of social acceptability and responsibility, this helps to gradually reduce some of the barriers to data sharing and to the use of MTM. The initial social acceptance of MTM presupposed a minimum of shared values. The IMCtt helps to establish a **fairer transition policy** in the field of transport and traffic. This common **promotes the discussion and sharing of values** that govern the decision-making process of TOs. Together with the mobility assessment criteria, they help to reduce reluctance and promote the **acceptability of the MTM** [KPA5-A4 & A5].

This orientation of traffic management to serve all mobility in the community promotes the **existence and recognition of trustworthy and fair regulatory authorities** that act in the public interest [KPA1-A5]. **Stakeholders** are regularly called upon to **participate with the authorities** in the re-evaluation of the data exchanged, the redefinition of traffic prioritisation criteria according to the
circumstances and the policies pursued at the level of the governance areas [KPA6-A3]. Therefore, these rules are clear and their choices transparent [KPA1-A3]. They are established at Community level on a pool of data, the minimum common denominator necessary for the MTM. However, they can be specified or supplemented locally in order to improve service provision or to support the implementation of specific public policies [KPA7-A2] & [KPA8 A3].

The IMC and IMCtt can thus gain knowledge and serve the general interest and free cooperation.

### 4.3.3 The MTM ensure universal basic mobility and does not burden future generations

Over the past 25 years, the development of infrastructure involved in traffic management has been favoured whenever it contributed significantly to a human activity trajectory in line with the IPCC SSP 1-1.9 scenario. Thus, these infrastructures are very resilient to negative impacts on the environmental footprint because the environmental standards in force are demanding and their application is imperative.

An adapted MTM infrastructure ensures that the European territory is well meshed, allowing the tool not only to manage traffic in real time but also to calculate the costs of travel. Under these conditions, the European states have agreed within the EU to set up and finance a universal mobility card for each resident and a mobility card for visitors. This card gives annual access to a travel credit allowing everyone to meet basic needs. This credit is a function of the environmental cost of each trip, adjusted by a socio-spatial equity coefficient that is revised annually. Beyond the threshold of the basic credit, each person and transporter (NU, FO, TSP, TU) must pay for the cost of the move made, which depends on the mode and means of transport, duration, distance, etc., and which includes the costs of negative externalities.

### 4.3.4 Social acceptability and business practices facilitated by welfare

In order to comply with the commitments of the Paris Agreements, the global economy has undergone significant quantitative and qualitative changes in productivity and business dynamism with the aim of significantly reducing the environmental footprint. The digitalisation of production has contributed to the partial re-composition of the employment structure. However, society and public policies no longer necessarily consider technological developments as “job gains” [KPA6-3]. The actual digitalisation of the economy is less than that imagined thirty years ago by the proponents of technoscience (Guchet, 2011).

By 2050, climate change and its awareness have encouraged the reconsideration and redefinition of “gains”. Working in particular on spatial reconfigurations in terms of densities and functionalities (Bringezu et al. 2017), countries, sectors, administrations, companies and NGOs have reallocated and relocated activities to improve living conditions for all (OECD 2018). The dynamism of activities is driven by an initial desire for sustainability [KP6-A4]. Thus, a part of the trips is no longer necessary, and another part is reconsidered. Nevertheless, traffic management may require decentralised real-time multimodal orchestration due to a re-composition of residential densities and of most activities.

This dynamism of an economy at the service of a fair transition is further strengthened by the maintenance of alternatives and the possibilities of technological and normative independence allowed by the strong limitation of Tech Giants and their capacity to influence (Bank et al. 2021).

With the reduction of the environmental footprint as a new episteme, a diffuse, dynamic and inventive entrepreneurship has made it possible to create counter-powers to the economic and technological hegemony sought by the Tech Giants. The imagination of a world saved by science
with no more conscience has been decolonised from their grip and very strict regulations now prohibit opaque lobbying practices, particularly those revolving doors (Pignatelli and Petitjean 2022). All the **agendas of elected representatives and senior civil servants are now public and accurately documented**. To avoid any conflict of interest, or even collusion, former elected representatives and former civil servants must also declare precisely all activities and income that link them to any private employer and to any public employer outside the EU. Corporate promotional and **lobbying expenditure and its beneficiaries are also accurately and publicly declared to an independent authority** in charge of ensuring this. These rules and their monitoring promote a **climate of trust between institutions and citizens** [KPA1-A5]. They strengthen democratic practices and the support of the people for the European project. As a result, **the stakeholders in charge of traffic** (TO, SPM and NM) **and of data intermediation** (Trusted Third Parties – DIs) are **away from economic pressures, destabilising speculation or undue political decisions**. The **decentralisation of the management of the MTM is fully realised** with a genuine concern for consultation with local stakeholders.

### 4.3.5 Yesterday's ports and airports have become true multimodal platforms

The internalisation of all transport costs for environmental sustainability purposes has forced spatial reconfigurations. As a result, the demands for mobility have adapted and so have the offers. **Time and speed are no longer the determining variables** as long as the quality and quantity of energy used to make a journey is correctly taxed.

As a place of exchange of transport modes to facilitate transit, **ports and airports are linked to the territories according to a variety of transport modes and offers**, primarily according to the distance to be covered and the volumes to be moved. They are **increasingly integrated with the agglomerations and hinterlands** they serve, and commuting links to these intense activity areas are particularly developed, especially public transport and low impact offers. **The quality and quantity of these connections to different territories (GAs) at different scales, as well as the multiplicity and diversity of stakeholders, make it more interesting to use the MTM.**

Even more than in the past, these areas are both **logistics zones and** primary, secondary and tertiary **production areas**.

**The purpose of the activities** that are deployed in these areas of access to the sky, sea and waterways is **motivated by a desire to reduce and optimise the impact of transport by playing on preferential locations.** Thus, the development of these hubs fully participates in the implementation of sustainable environmental policies.

Also, **data sharing allows multimodal platforms to take decisions based on socio-ecological criteria** such as prioritising green projects or green vehicles, for example, to reduce CO2 emissions assessed in a systemic way.

**The results of the environmental policy** of the "air-ground" and "water-ground" clusters are **shared with the surrounding society**. From the local to the international level, the actors of these hubs contribute to the well-being of the societies in and with which they interact. In particular, **they contribute to the awareness of the employees of the business park and the inhabitants of the surrounding region to ecological issues.**
# 5 Methodology

## 5.1 Short description of the sociocultural scenarios design process

The study developed exploratory scenarios of the future of the MTM in a long-term perspective in Europe up to 2050. These scenarios are broadly designed to stimulate dialogue and debate on the future of MTM by depicting a few contrasting pictures of a data driven MTM.

Generally, scenarios combine positive and negative aspects, thus providing food for thought on both risks and opportunities, as well as options and strategies. By extending alternative developments into the future, scenarios raise awareness of possible changes of the environment. They assist in identifying and clarifying strategic objectives and preparing knowledge for decision-makers.

Based on a robust set of plausible assumptions, the imagined scenarios help to define the barriers to be removed and the facilitating actions to be taken in order to support the MTME implementation. They are also aware on opportunities and threats that may influence the path from nowadays to the future.

The design of the scenarios did not start from a blank page but was based on the work carried out previously in the ORCHESTRA project. A short process description is given below (Figure 6). For more details on the scenario building process, see the following sub-chapters which follow the same order.

<table><caption>Figure 6: Construction method of the prospective scenarios</caption><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td><strong>PROCESS</strong></td><td></td><td></td></tr><tr><td><strong>Selecting key-factors</strong></td><td><pre>KPA 1<br>KPA 2<br>...</br>KPA 8<br>KPA 9</pre></td><td>8 of the 12 KPAs defined in the D6.1 were considered as influencing key-factors, main drivers, regarding the MTME implementation. They were defined through ORCHESTRA experts' analysis (first via D6.1, then via D6.2).</td></tr><tr><td><strong>Collecting data</strong></td><td><pre>Book icon<br>Workshop icon<br>Group icon</pre></td><td>Collect and analyse data related to each of the key KPAs from policies reports, regulations, research articles, CoPs' Workshops and experts' interviews. For the KPAs 1 to 4, this collection was done in a first round during D2.3 production and refined on a second round during D2.4, whereas the KPAs 5 to 8 were addressed during D2.4 period.</td></tr><tr><td><strong>Raw scenarios first description</strong></td><td><pre>2020 → 2050<br>S2<br>S1<br>S3</pre></td><td>Use the data analysis results to build three contrasted scenarios.<br>Then submission of the scenarios to the experts and CoPs to validate them and to refine them on the least documented key-factors, through workshops.</td></tr></tbody></table>
<figure><img src="image_20.png" /></figure>

Formulating assumptions
for the 3 scenarios

<figure><img src="image_21.png" /></figure>

For each of the 8 KPAs, a set of assumptions were first designed. These assumptions were then discussed through the experts of ORCHESTRA.

Assumptions clustering

<figure><img src="image_22.png" /></figure>

Then these assumptions were clustered in three groups:

* Those that were common to the 3 scenarios and implemented in the same way.

* Those that were common to the 3 scenarios but implemented in the different ways.

* Those related to only one or two scenario(s) and implemented in different ways.

Story of each scenario

<figure><img src="image_23.png" /></figure>

The scenario 1 was written first as there are given in the D2.4.

Then, scenario 2 and scenario 3 were written as forks from the scenario 1.

<figure><img src="image_24.png" /></figure>

These scenarios were finally read by the experts of ORCHESTRA.

<figure><img src="image_25.png" /></figure>

## 5.2 Selecting key-factors

### 5.2.1 KPAs as key-factors

As introduced in D2.3 (chapter 4.2), **ORCHESTRA implementation scenarios are deductive scenarios** enlightening the challenges to cope with to reach the ideal target visions of MTM. **They aimed to detail the assumptions of the sociocultural context** that will fund the deployment of the MTM concepts and **allow the achievement of the MTM target visions** (as described in D2.3, chapter 4.1).

The implementation scenarios addressed:

- The gaps to fill up from the current situation to the MTM target scenarios (D2.3, chapter 5).

- Issues that must be handled and, if possible, how they should be handled.

- Foreseen barriers and risks.

These implementation scenarios encompass many parameters that can influence the sociocultural context. Deliverable D6.1 suggested classifying these parameters using the decomposition of the evaluation variables into KPAs (Figure 7).

The **KPAs** represents the **partners common understanding** of the underlying mechanisms and outcomes expected from the implementation **of the MTME**.
<figure><img src="image_26.png" /><figcaption>Figure 7: The selected KPAs from the MTME program theory that structures the implementation scenario</figcaption></figure>

Source: D6.1, p. 25.

The implementation scenarios mainly dealt with eight out of the twelve KPAs of the program theory (as defined in D6.1 pp. 24-26), the 4 other KPAs regarding effects and impact are not relevant for the implementation scenarios as they deal with the assessment's topics: “effects” and “impacts”.

These eight KPAs structure the implementation scenario description, and are presented in the first 3 stages of the MTME Program theory (red boxes in Figure 7) as follows:

### Requirements and enablers

1. Politics, governance and regulation

2. Data governance and sharing

3. Smart infrastructure

### MTM functionality and context

4. Technological solutions (functionality)

### Mechanism for change

5. Stakeholders' acceptance of traffic management operations and autonomy

6. Operational practices and decision making for traffic orchestrators

7. Business policy aspects

8. Organisational aspects

### 5.2.2 KPAs' refinement regarding to the sociocultural approach

Two reasons drive to the refinement of the implementation scenarios provided in D2.3.

First, as the implementation scenarios involve many parameters that can influence the sociocultural context, there may be several scenarios for implementing the MTM. Nevertheless, the implementation scenario as defined in D2.3 (Chapter 6) is a unique one assuming that the KPAs were addressed in a unique way. In other words, the implementation scenarios as described in the D2.3 gave the main
“ingredients” of the context in which the MTM could occur, and all the “ingredients” got the same weight. However, in real life, the respective weight of these ingredients may vary according to the priorities assigned by the society itself. Thus, existing research on the sociocultural frameworks that might be considered relevant in the emerging phase of a new technological field, has largely focused on the strategic and political aspects of the prevailing processes. To this end, the struggles of “protagonist” and “antagonist” actors have been widely described (Geels & Verjees 2011, Lempiälä et al. 2019, Markard et al. 2012, 2016, Rosenbloom 2018). **Therefore, this deliverable D2.4 intends to describe the possible contrasted scenarios in which the MTM could be deployed.** In this document, three scenarios are imagined referring to three worlds based on different values, and which imply different relationships that society has with digital technology in general and therefore that MTM stakeholders adopt for traffic data exchange and orchestration.

Second, among the KPAs some of them gave further insights (KPA1, 2, 3 and 4 described in section 3.3 above), but others were not yet fully explored. Thus, the following KPAs still had to be deeper investigated:

- KPA5: Stakeholders' acceptance of traffic management operations and autonomy;

- KPA6: Operational practices and decision making for traffic orchestration;

- KPA7: Business policy aspects;

- KPA8: Organisational aspects.

The eight KPAs have been associated to a series of assumptions and requirements that have been used to elaborate the three different scenarios.

## 5.3 Collecting data

To continue the analyses leading to the construction of the 2050 scenarios, a large amount of data was collected. The data came from different sources:

- Previously published deliverables.

- Data collected during the interviews previously conducted in WP2.

- Bibliographic work to deepen the understanding of certain topics.

- Discussions with colleagues during the working sessions.

- Workshops held during the autumn 2022 (in Herøya and Malpensa) with the CoPs<fnref n="11" /> and partners

- Data collected during the sociocultural scenario's session in the plenary meeting (February 2023, Berlin).

- Additional interviews with experts, including:

- A Research Director from a public laboratory institution specialising in automated transport systems and their security, and particularly in rail transport.

- A Mobility Project Manager from a main European Airport.

- A Deputy Head of the Architecture Department from a main European Airport.

- A Head of Strategic Planning Department from a main European Airport.

- An expert in dimensioning, simulation and data management from a main European Airport.

- A Director of Planning and Mobility from a main European Airport.

- A Group Leader expert in Maritime and Inland Waterway Transport and Traffic from a public institution.

<footnote n="11">¹¹ Community of Practitioners
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 953618. This document reflects only the author's view and the Agency is not responsible for any use that may be made of the information it contains.</footnote>
These workshops and interviews were transcribed so that the information exchanged could be more
easily collected and worked on in order to build scenarios for the deployment of MTM in various
socio-cultural contexts.

5.4 Raw scenarios first description

Using the information collected during the previous tasks and published in the previous deliverables,
we first built up some questioning elements during the last summer. The idea was to ask the WP2
partners to express questions about the MTM and its ecosystem and to classify these questions
according to the KPAs.

In parallel, a major literature review was undertaken on topics related to these questions, in order to
sketch out three realistic socio-cultural contexts within which to consider MTME in 2030 and 2050.

On this basis, three storytelling contexts were written, each corresponding to a first contextualisation of each possible scenario. The objectives were then to assess the relevance of the selected contexts and to collect data on the KPAs involved in the mechanisms of change.

During the autumn working sessions in Herøya and Malpensa, the CoPs and partners were immersed,
per group, in one of these contexts during workshop sessions. They were then presented with
questions which they were required to discuss and answer in relation to the context in which they
were placed. The discussions were collected and processed in order to start to transform the
questionings into assumptions.

5.5 Formulating assumptions for the 3 scenarios

"Assumption": something taken for granted;
the act of taking for granted or supposing"
(Wordreference.com, online dictionary).

The assumptions and requirements provided has been drawn up from literature analyses, previous deliverables, data collected from the CoPs and partners during the Herøya and Malpensa workshops in autumn 2022, interviews with experts conducted during task T2.3, and discussions with Orchestra project colleagues during the working sessions. They are presented by KPA in a grid (see Figure 8 below).
<table><caption>Figure 8: Grid of all assumptions considered by KPA for the construction of the prospective scenarios</caption><thead><tr><th>Key Performance Area</th><th>KPA 1 - Policies, governance and regulations</th><th>KPA 2 - Data governance and sharing</th><th>KPA 3 - Smart infrastructure</th><th>KPA 4 - Technological solutions (i.e. MTM functionality)</th><th>KPA 5 - Stakeholder's acceptance of traffic management operations and autonomy</th><th>KPA 6 - Operational practices and decision making for traffic orchestrators</th><th>KPA 7 - Business policy aspects</th><th>KPA 8 - Organisational aspects (between TOs)</th></tr></thead><tbody><tr><td></td><td>A1 - Harmonised EU regulations (CAV, infrastructure, data management, cybersecurity, civil liabilities.)</td><td>A1 - Standardised data of good quality is available to interoperable and real-time traffic management</td><td>A1 - Infrastructure solutions must ensure data security. High level of data protection and security in all digital applications</td><td>A1 - Use of shared data in multimodal traffic orchestration is generalised</td><td>A1 - MTM supports transport actors in the planning and management of transport operations</td><td>A1 - The personnel involved are trained in continuous based on regular exercises. The usability is high</td><td>A1 - Traffic orchestration can support multimodal transport services provided by TSFs (e.g. MaaS)</td><td>A1 - Ongoing, transparent and seamless horizontal coordination of stakeholders</td></tr><tr><td></td><td>A2 - Green Deal promotes ambitious environmental policies (LCA, sustainable investments' financing mechanisms.)</td><td>A2 - Efficient, scalable and federated infrastructure for data discovery/mining/collection and sharing</td><td>A2 - All networks support a minimum set of measures and driving support (e.g. SAE level 3 for road)</td><td>A2 - Vessels/vehicles and transport operations properties as well as TDM and DCB processes are confirmed by certificates</td><td>A2 - Stakeholders trust the choices and decisions of the TO(s) (human or artificial) because of its governance process</td><td>A2 - The actors involved fulfil the responsibilities defined by the roles.</td><td>A2 - MTME can be adapted to local needs according to the policies</td><td>A2 - The responsibilities within one governance area can be handed over to another while respecting everyone's rights</td></tr><tr><td></td><td>A3 - Clear and transparent prioritization of traffic management criteria set prior by the authorities</td><td>A3 - The exchange of private data is carried out using an accredited intermediary service provider (trusted third party)</td><td>A3 - Vehicles with ADS feature SAE level 4 on appropriate sections and feature SAE level 5 on dedicated sites</td><td>A3 - MTM collaboration and measures meet requirements regarding information and cyber security</td><td>A3 - Stakeholders recognize the robustness of the system in terms of cyber security</td><td>A3 - Stakeholders participate to TOs continuous evaluation and their improvements</td><td>A3 - MTME stakeholders reach an institutional arrangement on data ownership(s) and use rights</td><td>A3 - Governance structures can be adapted at all territory scales</td></tr><tr><td></td><td>A4 - Ambitious EU directive on combined, multimodal and mixed transport</td><td>A4 - In EU, stakeholders respect harmonised data formats and data sharing requirements</td><td>A4 - Vehicles with ADS feature SAE level 4 on appropriate sections and feature SAE level 5 on dedicated sites</td><td>A4 - MTM demonstrates clear financial, time and environmental gains to stakeholders and the society</td><td>A4 - Stakeholders recognize the robustness of the system in terms of cyber security</td><td>A4 - Workload could be reduced through automation when this is appropriate</td><td>A4 - Cyber risks and civil liabilities are insured without a negative impact on transport costs</td><td>A4 - Stakeholders' efforts to comply with regulatory incentives can be rewarded</td></tr><tr><td></td><td>A5 - Trustworthy and fair regulatory authorities</td><td>A5 - In EU, stakeholders respect harmonised data formats and data sharing requirements</td><td>A5 - Safety-critical and high-traffic network segments have a dedicated channel to communicate with the NUs.</td><td>A5 - MTM demonstrates clear financial, time and environmental gains to stakeholders and the society</td><td>A5 - Stakeholders recognize the robustness of the system in terms of cyber security</td><td>A5 - Workload could be reduced through automation when this is appropriate</td><td>A5 - Cyber risks and civil liabilities are insured without a negative impact on transport costs</td><td>A5 - Stakeholders' efforts to comply with regulatory incentives can be rewarded</td></tr><tr><td></td><td>A6 - At any time NU can easily make a choice about his past, present and future personal data. A6 - Data sharing understood, not suffered and practised consciously and confidently by the majority if not by everyone</td><td>A6 - In EU, stakeholders respect harmonised data formats and data sharing requirements</td><td>A6 - Vehicles with ADS feature SAE level 4 on appropriate sections and feature SAE level 5 on dedicated sites</td><td>A6 - MTM demonstrates clear financial, time and environmental gains to stakeholders and the society</td><td>A6 - Stakeholders recognize the robustness of the system in terms of cyber security</td><td>A6 - Workload could be reduced through automation when this is appropriate</td><td>A6 - Cyber risks and civil liabilities are insured without a negative impact on transport costs</td><td>A6 - Stakeholders' efforts to comply with regulatory incentives can be rewarded</td></tr><tr><td></td><td>A7 - At any time NU can easily make a choice about his past, present and future personal data. A7 - Data sharing understood, not suffered and practised consciously and confidently by the majority if not by everyone</td><td>A7 - In EU, stakeholders respect harmonised data formats and data sharing requirements</td><td>A7 - Vehicles with ADS feature SAE level 4 on appropriate sections and feature SAE level 5 on dedicated sites</td><td>A7 - MTM demonstrates clear financial, time and environmental gains to stakeholders and the society</td><td>A7 - Stakeholders recognize the robustness of the system in terms of cyber security</td><td>A7 - Workload could be reduced through automation when this is appropriate</td><td>A7 - Cyber risks and civil liabilities are insured without a negative impact on transport costs</td><td>A7 - Stakeholders' efforts to comply with regulatory incentives can be rewarded</td></tr><tr><td></td><td>A8 - At any time NU can easily make a choice about his past, present and future personal data. A8 - Data sharing understood, not suffered and practised consciously and confidently by the majority if not by everyone</td><td>A8 - In EU, stakeholders respect harmonised data formats and data sharing requirements</td><td>A8 - Vehicles with ADS feature SAE level 4 on appropriate sections and feature SAE level 5 on dedicated sites</td><td>A8 - MTM demonstrates clear financial, time and environmental gains to stakeholders and the society</td><td>A8 - Stakeholders recognize the robustness of the system in terms of cyber security</td><td>A8 - Workload could be reduced through automation when this is appropriate</td><td>A8 - Cyber risks and civil liabilities are insured without a negative impact on transport costs</td><td>A8 - Stakeholders' efforts to comply with regulatory incentives can be rewarded</td></tr></tbody></table>

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 953618. This document reflects only the author's view and the Agency is not responsible for any use that may be made of the information it contains.
The formulations of the assumptions contained in the grid (Figure 8) are explained one by one below in code order, i.e. from the 1st assumption of the Key Performance Area (i.e. KPA1-A1) to the 3rd and last assumption of the Key Performance Area (i.e. KPA8-A3).

### 5.5.1 Explanation of KPA1 assumptions: Politics, governance and regulation

**KPA1-A1:** The European legislation needs to be clarified or further developed in some aspects that concern MTM. Some legislation could be re-evaluated, as is the case for the Data Governance Act, which is subject to re-evaluation period after the first two years of implementation.

**KPA1-A2:** The European Green Deal succeeds in generating demanding and ambitious environmental assessments.

**KPA1-A3:** The rules of priority in traffic management are established in advance and known by all even if policies and strategies can be at different levels.

**KPA1-A4:** The European Union could further encourage multimodality and combined transport (freight-passenger) for optimization and ecological transition purposes.

**KPA1-A5:** At each territorial scale of traffic management, the competent regulatory authorities have the confidence of the MTME stakeholders and are fair in their management.

### 5.5.2 Explanation of KPA2 assumptions: Data governance and sharing

**KPA2-A1:** The MTM requires standardized data layouts and clean data sets to be properly exploited.

**KPA2-A2:** For greater efficiency, data sharing and exchange infrastructures need to be scalable and federated to facilitate the data gathering, the data mining, the data discovery and the data sharing.

**KPA2-A3:** In order to facilitate cooperation within the MTM ecosystem while preserving the interests of each of the stakeholders, the exchange of private data is carried out through an accredited intermediation service provider, a trusted Data Intermediator.

**KPA2-A4:** In the EU, stakeholders are playing the game in terms of harmonizing enforcement and data controls to boost the ecosystem.

**KPA2-A5:** MTME stakeholders agree to share data under certain conditions with certain actors. They also fully comply with the GDPR.

**KPA2-A6:** MTM network users have understood the rules of data sharing. They are fully aware of the nature and types of information shared and practice this sharing with confidence. They can control or even stop the sharing of their data at any time, unless the sharing of these data is compulsory.

### 5.5.3 Explanation of KPA3 assumptions: Smart infrastructure for automation and integration of CAVs

**KPA3-A1:** In order to secure people and assets and enable their insurance, infrastructure solutions must guarantee data security. A high level of protection and security for all digital applications

**KPA3-A2:** Real-time traffic data and measurements are generalized to all networks at an intermediary level of automation (such as SAE roads Level 3 for instance). Vehicles are equipped with on-board systems supporting the operation. If necessary, old and vintage vehicles or slow-moving vehicles (agricultural, construction, security or military) may for example carry a mobile transponder to be able to communicate at least the necessary data to the MTM. To travel on roads outside of bicycle paths, active mobility vehicles should also have to be equipped with a kind of transponder.
KPA3-A3: The vehicles of all modes are equipped with upper level of automation than in KPA3-A2. Thus, vehicles corresponding to SAE roads Level 4 operate on appropriate infrastructure and vehicles corresponding to SAE road Level 5 run on dedicated sites.

KPA3-A4: In areas with high mobility but also in spaces with very low mobility density, real-time information and communication systems need an adapted server network to be efficient.

KPA3-A5: The deployment of low-impact infrastructure respecting the 9 planetary boundaries (Rockström et al. 2009, Steffen et al. 2015, Persson et al. 2022), requires the generalized practice of demanding multi-criteria evaluation upstream any TOs' decision.

### 5.5.4 Explanation of KPA4 assumptions: Technology solutions

KPA4-A1: In multimodal traffic orchestration, the use of shared data is widespread as a result of standardization and interoperability efforts.

KPA4-A2: Vessel and vehicle properties as well as transport operations and TDM and DCB processes are confirmed by certificates.

KPA4-A3: Technical solutions for data exchange must ensure the security of MTM stakeholders' data, including those related to cyber security.

KPA4-A4: In real-time traffic management, AI supports decision making, process automation and exchange coordination.

KPA4-A5: The MTM is, at all times, able to integrate environmental assessments according to different standards (cost-benefit, multi-criteria, LCA, ...) to respond to public policy decisions (OECD 2018) and elaborate traffic management choices accordingly.

KPA4-A6: The solutions selected by the authorities and then by the TO offer great resilience to negative impacts on the environmental footprint.

### 5.5.5 Explanation of KPA5 assumptions: Acceptance for traffic management operations and autonomy

KPA5-A1: Transport stakeholders and authorities use the MTM to support their business or activities and manage a majority of the mobility flows in their territory.

KPA5-A2: The Stakeholders trust the choices and decisions of the TOs (human or artificial) because of its governance process.

KPA5-A3: In matters of cyber security, stakeholders recognize the robustness of the multimodal traffic management system and its ecosystem.

KPA5-A4: The MTM clearly states financial, time, social and environmental gains for stakeholders.

KPA5-A5: During traffic management, stakeholders' efforts to comply with regulatory incentives can be rewarded according to predetermined and explained procedures.

### 5.5.6 Explanation of KPA6 assumptions: Operational practices and decision making

KPA6-A1: The personnel involved in tasks related to MTM strategies and operations are continuously trained on the basis of regular exercises. The ease of use is high and is thus enhanced.

KPA6-A2: The Stakeholder roles can be mapped to real actors in all types of modes and networks. This facilitates system design: Flexible organisation of the traffic orchestration tasks, adapted to modes and networks as well as European, national and local strategies.
**KPA6-A3:** All ecosystem actors (MTME) participate to TOs continuous evaluation and their improvements.

**KPA6-A4:** The workload of multimodal real-time traffic management could be reduced by automation when appropriate for the relevant authorities.

### 5.5.7 Explanation of KPA7 assumptions: Business and organisational aspects

**KPA7-A1:** Real-time traffic orchestration management can support multimodal transportation services provided by TSPs (e.g. MaaS).

**KPA7-A2:** MTME can be adapted to local needs according to policies implemented in the territory.

**KPA7-A3:** MTME stakeholders reach an institutional agreement on data ownership and use rights.

**KPA7-A4:** Cyber-risks and civil liabilities are insured without significant impact on transport costs.

**KPA7-A5:** Thanks to their financial power, the Tech Giants always are a step ahead with respect to use of technologies developments and solutions. So, they have a competitive edge in service delivery and dominate the MTM ecosystem.

### 5.5.8 Explanation of KPA8 assumptions: Operational aspects

**KPA8-A1:** Coordination among MTME stakeholders is ongoing, according to transparent rules and decisions, in a cooperative background through the MTM stakeholders.

**KPA8-A2:** The responsibilities within one governance area can be handed over to another by authorities while respecting everyone's rights.

**KPA8-A3:** The governance structures can be adapted at all territory scales.

The grid contains a set of factors that can make the advent of the MTM evolve according to three different sociocultural contexts, bases for the three 2030-2050 scenarios, depending on the combination of these variables:

- a main scenario, as the MTM advent scenario (scenario 1);

- and two forks in particular directions:

* Scenario 2

* Scenario 3

## 5.6 Assumptions clustering

The set of hypotheses posed was classified under three categories according to their "frequency" and the variability of the associated responses:

- Assumptions common to all three scenarios and implemented in probably similar ways (see below orange boxes in Figure 9 below).

- Assumptions common to all scenarios but whose implementation may vary significantly depending on the context of each scenario (yellow boxes in Figure 9 below).

- And those which are common to only one or two scenarios and which are implemented differently (white boxes in Figure 9 below).
<figure><img src="image_27.png" /><figcaption>Figure 9: Constancies and variations in the implementation of assumptions</figcaption></figure>

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 953618. This document reflects only the author's view and the Agency is not responsible for any use that may be made of the information it contains.
The scenario's design is based on the combination of hypothesis derived from the interconnections between the KPAs, the assumptions and requirements. The following section details the hypothesis selected for each of the three contrasted scenarios.

To make more explicit the variations in the implementation of an assumption, let us take the example of assumption 2 of KPA1 (KPA1-A2): "Green Deal promotes ambitious environmental policies (LCA, sustainable investments financing mechanisms...)$^{)}$".

In Scenario 1, public authorities, at national and local levels, implement several policies and measures that impact the MTME stakeholders, for example by working in different ways to reduce the use of cars (by carpooling incentives, low emission zones, etc) or certain emission factors (CO2, etc.) or by trying to limit the use of space by optimising existing transport networks, or even reducing the space dedicated to mobility. Nevertheless, such practices are not immune to generating rebound effects of practices that often appear after implementation.

In Scenario 2, the Tech Giants who dominate the MTME are only interested in environmental policies to the extent that they significantly measure this expectation among their user-customers. Profit maximisation is primarily driven by personal rather than community or common gain. Therefore, the implemented green public transport policies struggle to find a favourable response and have only a marginal effect on improving the environment. Some dominant actors may even deploy media strategies to try to shape public opinion if they feel it runs counter to their strategic and financial interests. As a result, the CO2 consumption related to data exchange, storage and data processing does not come to the forefront.

In Scenario 3, environmental policies and variables are considered upstream and given priority. They drive the transition of society towards a sustainable social-ecological economy. Gradually, they constitute a real culture that integrates our lifestyles and should make it possible to limit certain rebound effects. Involving MTME stakeholders in the processes of developing public policies fosters their subsequent commitment to implementing solutions that contribute to the achievement of collectively negotiated environmental objectives.

## 5.7 Story of each scenario

The main sociocultural scenario built (Figure 10) is in line with the core purpose of ORCHESTRA, i.e. the optimisation of traffic management inside each mode of transport and in the combination of different modes. Thus, the other two scenarios (Figure 11 and Figure 12) can be seen as the result of radical choices that fundamentally change the deployment contexts of the MTM. These two scenarios are derived from the main scenario because some of its assumptions are likely to be discussed, even be discarded and replaced by others. These two other scenarios emphasise societal values different from traffic optimisation in the strict sense. They could be seen as the result of choices made by society – either in a regulated way when public authorities retain a major decision-making role – or more spontaneously – when the laws of the free market and its main actors take the lead.
<figure><img src="image_28.png" /><figcaption>Figure 10: Grid of assumptions considered by KPA for the construction of the Scenario 1</figcaption></figure>
<figure><img src="image_29.png" /><figcaption>Figure 11: Grid of assumptions considered by KPA for the construction of the Scenario 2</figcaption></figure>
<figure><img src="image_30.png" /><figcaption>Figure 12: Grid of assumptions considered by KPA for the construction of the Scenario 3</figcaption></figure>
6 Conclusions

The aim of this deliverable (D2.4) was to **refine the implementation scenario of the target vision of MTME**, as drawn up in D2.3. It sought to explore in greater depth the ways in which the three pillars of the *MTME temple*, namely: Technology, societal aspects, frameworks and models, could be deployed, revealing the **main trend scenarios for a socio-culturally accepted MTME**.

As data sharing implementation is the core issue of the acceptation of the MTME, D2.4 provides
answers to the research questions addressed in D6.2 (p. 28), namely:

- How should data sharing and its governance be implemented (RQ 2.1)

- What are the societal aspects and concerns to be taken into account in data sharing (RQ 2.2).

The first main answer to data sharing issue is the proposal to **insert a Data Intermediator (DI) role at the heart of MTME**. This new role emerged in response to the need for safe and GDPR-friendly access to a wide range of data **for the implementation of a decentralised, useful and efficient MTM**. This introduction of the DI into the system also seeks to respond to the questions or wishes of certain actors (CoPs, project partners, etc.) to see the **European data strategy** organised in concrete terms in accordance with the latest regulation of the **Data Governance Act**.

Beyond this necessary trusty stakeholder, the **contemporary issues surrounding data** are such that it appeared useful and necessary to evaluate particularly those concerning ORCHESTRA and its MTME concepts. Thus, the societal aspects of MTM data sharing have been addressed through different “universes”, or so-called “socio-cultural” contexts. Thought in a systemic approach, these prospective evaluations allow apprehending at a variety of scales the **requirements necessary to reach the target vision, to overcome the societal bottlenecks, and the conditions of acceptance of the MTME concepts**.

In this perspective, each of the **three contrasted main trends** selected suggest the realities of three very different European societies depending on the **relative weight of the three spheres of society: Citizens, companies and public authorities.** Each scenario gives a different **weight to well-being and citizens, to environmental sustainability, to business – namely data business.** Scenario 1 provided an ideal technological context for seamless and optimised MTM. Scenario 2 was imagined to think about the issues linked to the possible domination of Tech Giants, the problems of regulation they raise, and even the pitfalls of the surveillance capitalism they embody. Scenario 3, suggests a more challenging path founded on a responsible society concerned with the rational consumption of resources and the drastic limitation of CO2 emissions, including by the data industry.

As a synthesis, the traffic management proposed by **ORCHESTRA** could be a formidable **tool to encourage and facilitate the modal shifts necessary** for the ecological transition and the establishment of a welfare and peaceful society. Depending on its configuration, it could tomorrow offer local authorities and various public policy institutions a tool capable of **combining transport offers, regulatory constraints and taxation favourable to the whole of society**.

Thus, D2.4 can be an inspiring resource for tacking arrangements to meet the challenges facing society as they emerge in the early 2020s. It opens up the **need for a responsible digital transition to ensure more resilient traffic management organisations.** Overall, it calls for the moral and social principles that underpin it to be decided collectively. Thus, D2.4 paves the way for a meaningful white paper (to be provided as deliverable D2.5) to policy makers for European MTME.

D2.4 will also be useful for the two Living Labs of ORCHESTRA, by providing inputs on data sharing issues.
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