become Cooperative Intelligent Transport Systems (C-ITS) nodes for emerging automated vehicles to recognise and protect them, given their potentially lower per- ceptibility and higher vulnerability. The impact of each new technology on PRM accessibility must be assessed during its design phase, and appropriate adaptability/ personalisation means and strategies should be supported.

There is also a need for standardised and enforceable European legislation on accessibility across the mobility chain, as no such dedicated legislation exists for multimodal transport, be it for long-distance or local transport. The European secto- rial legislation on air, maritime, railway and road transport does not fulfil this goal, as it (1) allows for exceptions that were largely used by Member States, (2) mainly focuses on long-distance transport and (3) does not support a wide range of the actual requirements of PRMs. The current legislation does not cover the possibili- ties of spontaneous travel (European Commission, 2018), i.e. there is a duty of prior notification in case of assistance requirements (Regulation on Rail Passengers Rights, n.d.), and provides inadequate support during travel and compensations for the loss or damage to accessibility equipment (for instance, in the cases of denial of travel and limited lost property compensations).

## 16.4 Conclusion

AVs might provide disruptive changes to the mobility of the twenty-first century. The regulatory environment in which they are tested and deployed can be a major incentive (International Association of Public Transport, 2021) or an obstacle to their development. The governance decides also about the citizen centricity, the creation of wealth but also the business viability in particular the creation or not of closed ecosystems. European executive and legislative bodies are now beginning to understand the need for AV regulations and the role they can play. The integration of AV in MaaS/ITS is a new challenge as well (see vision below). Encouraging signals have been emitted recently with the adoption of innovative regulations (Roedl & Partner, 2022). However, much legal work remains ahead, in order to avoid having regulations that are too far behind the technological developments, opening a regulatory “no man’s land” that would be beneficial for a few private stakeholders only, and not to society as a whole.

**Acknowledgements** This project has received funding by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 769033.

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**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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# Chapter 17
## Sustainability Assessment of the Integration of Automated Minibuses in Urban Mobility Systems: Learnings from the AVENUE Project

Eliane Horschutz Nemoto, Ines Jaroudi, Dorien Duffner-Korbee, Tobias Viere, Gabriele Naderer, Adrian Boos, Nicole van den Boom, and Guy Fournier

**Abstract** The AVENUE sustainability assessment integrates the environmental, economic and social assessment of the pilot trials of AVENUE. A step further, it adopts an interdisciplinary approach to conduct the analysis and to better understand the complexity of deploying a new form of mobility in urban areas and as part of the transportation system. The goal is to implement new mobility solu-

This chapter is written based on the previous publications: Nemoto, E.H.; Jaroudi, I.; Korbee, D.; Viere, T.; Naderer, G.; Boss, A.; Fournier, G. (2022). D8.12 Sustainability assessment. Available at https://cordis.europa.eu/project/id/769033/results.

E. H. Nemoto (✉)
CentraleSupélec, LGI, Gif-sur-Yvette, France

Pforzheim University, Pforzheim, Germany

ICLEI Europe, Freiburg im Breisgau, Germany
e-mail: eliane.nemoto@iclei.org

I. Jaroudi
CentraleSupélec, LGI, Gif-sur-Yvette, France

Pforzheim University, Pforzheim, Germany

Fraunhofer ISI, Karlsruhe, Germany

D. Duffner-Korbee
Pforzheim University, Pforzheim, Germany

Fraunhofer ISI, Karlsruhe, Germany

T. Viere · G. Naderer · A. Boos
Pforzheim University, Pforzheim, Germany

N. van den Boom · G. Fournier
CentraleSupélec, LGI, Gif-sur-Yvette, France

Pforzheim University, Pforzheim, Germany
tions that benefit the city and complement public transport. The findings from the social, environmental and economic impact assessments are embedded in a multidimensional set of indicators for the sustainability assessment of the automated minibuses. As a result, mobility radars are built to illustrate the assessment of the AVENUE demonstrator sites. The results reveal the state of the art of the current deployment of the automated minibuses in the AVENUE test sites. In addition, this study highlights other crucial elements for a sustainable deployment that rely on the mode of deployment of the automated minibuses, the level of integration to urban public transport or within MaaS perspective and the alignment with the city's goals, planning and strategies for sustainable mobility. Ultimately, the automated minibuses are an innovative piece within the mobility ecosystem that could support intermodality, MaaS, mobility hubs and the use of soft modes of transport.

# 17.1 Introduction

Automated minibuses for public transport are expected to contribute to sustainable urban mobility. By combining automated, connected, shared and electric technologies, the automated minibuses could improve transport accessibility, efficiency and reduction of greenhouse gases (GHG) (Jones & Leibowicz, 2019). They have the potential to play a role in a shift from vehicle ownership to shared mobility services (Shaheen & Chan, 2016) and to reduce transport externalities (Lim & Taeihagh, 2018). Nonetheless, one cannot take for granted that the deployment of innovation and new technologies per se will contribute to sustainable mobility. It rather depends on certain premises, planning and policies to frame the automated minibuses deployment.

The study by Taiebat et al. (2018) points out the main gaps concerning connected and automated vehicles' impacts; for instance, the net effect of AV technology on energy consumption and emissions in the long term remains uncertain. In addition, the broader society-level impacts and behavioural changes associated with AVs are also unclear. The study highlights that the 'synergetic effects of vehicle automation, electrification, right-sizing, and shared mobility are likely to be more significant than anyone isolated mechanism'.

AVs, especially for private use, could lead to an increase in vehicle kilometres travelled (VKT), reductions of the public transport and slow modes share (Soteropoulos et al., 2019), whereas shared automated vehicles, when considering a high share, could reduce the number of vehicles for the current travel demand, resulting in less parking and more space in the cities (ibid). Yet, it is worth noting that the results of impact assessment for AVs are strongly dependent on model assumptions (Soteropoulos et al., 2019).
The integration of automated minibuses into the public transport of European cities also raises questions regarding their potential benefits and critical points to contribute to the sustainable urban mobility plan (SUMP) and goals towards sustainable mobility of the cities. Therefore, this chapter presents the sustainability assessment of the AVENUE project and demonstrator sites. The goal of the sustainability assessment is to integrate and interrelate the results of the social, environmental and economic impacts and to embed these results by applying the set of indicators for sustainability assessment of the automated minibuses within the AVENUE demonstrator sites. In addition, the concept of SUMP is also a building block for the sustainability assessment.

## 17.2 Research Approach

The AVENUE approach to sustainability assessment comprises the concept of SUMP and indicators for sustainable mobility assessment.

By aiming to achieve sustainable mobility, indicators are used to measure performance and progress towards established goals and objectives (Litman, 2007). Urban sustainability indicators are fundamental to support target setting and performance reviews and to enable communication among the policymakers, experts and general public (Shen et al., 2011; Verbruggen & Kuik, 1991). Therefore, a set of indicators is applied for the sustainability assessment of the deployment of the automated minibuses in AVENUE pilot sites (Nemoto et al., 2021); subsequently, mobility radars illustrate the results.

The disaggregated indicators reveal the strengths and weaknesses of each mobility indicator (World Business Council for Sustainable Development, 2015). As graph representation, the radar (also known as spider chart) enables easy communication and visualisation of the results and comparison among case studies.

## 17.3 Sustainability Assessment of the AVENUE Demonstrator Pilot Sites

The sustainability assessment builds upon the set of indicators (Table 17.1) developed by Nemoto et al. (2021, 2022). The assessment comprehends the mobility multidimensions: social, environmental, economic, governance and system performance. Further, a selection of indicators (Table 17.2) was applied based on data availability from the pilot sites. Table 17.2 describes the indicators, units of measurement and scales of assessment.

Subsequently, the next section presents a brief description of the assessed pilot sites and reports the mobility radars for each pilot site based on the empirical data
<table><caption>**Table 17.1** Set of indicators for sustainable mobility assessment of shared automated electric vehicles from Nemoto et al. (2021)</caption><thead><tr><th rowspan="2">Indicators</th><th rowspan="2">Unit and methods of measurement</th><th colspan="5">Multidimensions</th></tr><tr><th>S (x)</th><th>En (xx)</th><th>Ec (xxx)</th><th>G (xxxx)</th><th>SP (xxxxx)</th></tr></thead><tbody><tr><td rowspan="2">Accessibility</td><td>• Percentage of the city (area) coverage by the automated minibus service</td><td>x</td><td></td><td></td><td></td><td>xxxxx</td></tr><tr><td>• Percentage of the population that has convenient access (within 0.5 km) to the automated minibus service</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">Accessibility for people with reduced mobility</td><td>• Automated minibus digitally accessible (e.g. via apps)</td><td>x</td><td></td><td></td><td></td><td>xxxxx</td></tr><tr><td>• External environment facilities, e.g. stops adaption for impaired/disabled people; tactile surfaces information</td><td>x</td><td></td><td></td><td></td><td>xxxxx</td></tr><tr><td>• Internal environment facilities, e.g. audible warning equipment for visually impaired people; facilities for wheelchair users</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• Usability of the SAEV by people with reduced mobility (PRM)</td><td>x</td><td></td><td></td><td></td><td>xxxxx</td></tr><tr><td rowspan="2">Safety</td><td>• Risk factor and number of accidents related to the automated minibus (mild injuries, serious injuries, fatalities) considering internal risk (related to passengers) and external risk (related to other road users, pedestrians and cyclists)</td><td>x</td><td></td><td></td><td></td><td>xxxxx</td></tr><tr><td>• Number of criminal occurrences; nr/year</td><td>x</td><td></td><td></td><td></td><td>xxxxx</td></tr><tr><td>Security</td><td>• Number of cybersecurity threats or attacks; nr/year</td><td>x</td><td></td><td></td><td></td><td>xxxxx</td></tr><tr><td>Passenger's affordability</td><td>• The price of the ride on the automated minibus</td><td>x</td><td></td><td>xxx</td><td></td><td></td></tr><tr><td>Social acceptance</td><td>• User's perception about the readiness of the technology</td><td>x</td><td></td><td></td><td></td><td>xxxxx</td></tr><tr><td rowspan="2">User satisfaction</td><td>• User's willingness to pay</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• Safety feeling</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>• Security feeling</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>• User rating concerning automated minibus experience (comfort, speed, punctuality, information, frequency, connection to other means of transport)</td><td>x</td><td></td><td></td><td></td><td>xxxxx</td></tr><tr><td>Energy efficiency</td><td>• Energy consumed for passenger per km (kWh/pkm)</td><td></td><td>xx</td><td></td><td></td><td>xxxxx</td></tr><tr><td>Renewable energy</td><td>• Use phase: energy source and percentage of renewable energy sources (%)</td><td></td><td>xx</td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>**Table 17.1 (continued)**</caption><thead><tr><th rowspan="2">Indicators</th><th rowspan="2">Unit and methods of measurement</th><th colspan="5">Multidimensions</th></tr><tr><th>S (x)</th><th>En (xx)</th><th>Ec (xxx)</th><th>G (xxxx)</th><th>SP (xxxxx)</th></tr></thead><tbody><tr><td>Air pollution</td><td>• automated minibus emissions of air pollutants: PM levels (ug/m3), NOx, CO emissions</td><td>x</td><td>xx</td><td></td><td></td><td>xxxxx</td></tr><tr><td>Climate change</td><td>• Automated minibus GHG emissions: CO<sub>2</sub>, N<sub>2</sub>O, CH<sub>4</sub></td><td>x</td><td>xx</td><td></td><td></td><td>xxxxx</td></tr><tr><td>Noise pollution</td><td>• Automated minibus traffic noise (dB)</td><td>x</td><td>xx</td><td></td><td></td><td>xxxxx</td></tr><tr><td>Investments on mobility</td><td>• Public and private annual average investment on transport concerning automated vehicles (Euro/year), e.g. infrastructure, operational expenditures (cost of personnel, software system, etc.), investments in the vehicle R&D</td><td></td><td></td><td>xxx</td><td>xxxxx</td><td></td></tr><tr><td>Economic incentives for SAEV and sustainable mobility</td><td>• Incentives and subsidies for automated and sustainable mobility, e.g. shared, electric, automated, zero-emission, vehicles (Euro)</td><td></td><td>xx</td><td>xxx</td><td>xxxxx</td><td></td></tr><tr><td>Economic profitability</td><td>• TCO (total cost of ownership), TCM (total cost of mobility), cost/km/ passenger, revenues (ticketing from passengers, subsidies from authorities and companies) and payback period</td><td></td><td></td><td>xxx</td><td></td><td></td></tr><tr><td>External costs related to the automated minibus</td><td>• Automated minibus impacts on congestion avoidance, accidents reduction, noise reduction, air pollution (PM, NOx) reduction, QALY (quality-adjusted life years) reduction, land/parking reduction, vehicle savings</td><td></td><td>xx</td><td>xxx</td><td></td><td>xxxxx</td></tr><tr><td>Institutional development and innovation</td><td>• Existence of policies and regulations concerning automated vehicles<br>• Regulations for open data and/or APIs for transport</td><td></td><td></td><td></td><td>xxxxx</td><td></td></tr><tr><td>Technical performance and reliability</td><td>• Automated minibus performance:<br>Travel time (speed, frequency of departure or response speed for on-demand, travel-matching, punctuality);<br>On-demand availability;<br>Percentage of operational service; performance on different seasons/ weather;<br>Vehicle occupancy (average passenger per km travelled);<br>The average lifetime of the vehicle;<br>Number of disengagements in the urban environment; number of km driven autonomously</td><td></td><td></td><td></td><td></td><td>xxxxx</td></tr></tbody></table>
<table><caption>**Table 17.1** (continued)</caption><thead><tr><th rowspan="2">Indicators</th><th rowspan="2">Unit and methods of measurement</th><th colspan="5">Multidimensions</th></tr><tr><th>S (x)</th><th>En (xx)</th><th>Ec (xxx)</th><th>G (xxxx)</th><th>SP (xxxxx)</th></tr></thead><tbody><tr><td>System integration and efficiency</td><td><ul><li>Automated minibus integration with mobility platform of the operator (planning, reservation, booking, billing, digital ticketing)</li><li>System and data interoperability and the existence of open data for the automated minibus (access, static and/or dynamic real-time data, diffusion format, data quality and open APIs for transport)</li><li>Intermodality: automated minibus integration with other public or private means of transport or with a multimodal platform for one intermodal trip (planning, reservation, booking, billing, digital ticketing)</li></ul></td><td></td><td></td><td>xxx</td><td>xxxx</td><td>xxxxx</td></tr><tr><td>Changes in total kilometres travelled in the transportation system</td><td><ul><li>Changes in per capita vehicle travelled induced by automated vehicles</li><li>Transportation demand management measures introduced congestion pricing, biking lanes, zoning measures, land-use policies</li></ul></td><td>xx</td><td></td><td>xxxx</td><td>xxxxx</td><td></td></tr></tbody></table>

from the trials. The four sites assessed comprise Groupama (Lyon), Contern (Luxembourg), Pfaffenthal (Luxembourg) and Nordhavn (Copenhagen).

### 17.3.1 Pilot Sites Assessment and Results

The indicators were applied for the sustainability assessment of four different demonstrate sites. The description of the sites and respective mobility radar are presented hereinafter. The indicators present a value from 1 to 5—with 1 for the worst performance and 5 for the best performance; therefore, the outside part of the radars represents the optimal results. It is worth noting that the data availability and sample vary from site to site. Table 17.3 summarises the main information on the pilot sites.

Next, Fig. 17.1 presents the sustainability mobility radars from AVENUE pilot sites.

The results from the sustainability assessment (Nemoto et al., 2023) reveal the strong and weak points of the deployment of automated minibuses. Some common results among the sites pointed out that the automated minibuses score poorly on ‘energy efficiency’ (with the exception of Pfaffenthal) and ‘low contribution to climate change’ due to the low vehicle occupancy. With the exception of Pfaffenthal
<table><caption>**Table 17.2** Indicators, units and scales of assessment</caption><thead><tr><th>Indicators</th><th>Parameter</th><th>Scale of assessment</th></tr></thead><tbody><tr><td>Social acceptance</td><td>Average rating reported concerning the (1) willingness to use automated minibus; (2) perception about the readiness of the technology; (3) willingness to pay</td><td>City</td></tr><tr><td>User satisfaction</td><td>Average rating satisfaction reported concerning the automated minibuses speed, comfort, punctuality, information, frequency of service, connection to other means of transport and satisfaction with the last ride</td><td>Local</td></tr><tr><td>Passenger's affordability</td><td>Costs (euro) passenger-km for passengers</td><td>City</td></tr><tr><td>Climate change</td><td>gCO2 eq/passenger-km</td><td>Local and global</td></tr><tr><td>Air pollution</td><td>Air pollutant emissions, particular matter, PM2.5 (g/km) and nitrogen oxides, NOx (g/km), from exhaust and non-exhaust</td><td>Local</td></tr><tr><td>Noise pollution</td><td>Vehicle noise in decibels (dB)</td><td>Local</td></tr><tr><td>Renewable energy</td><td>Percentage of renewable energy in the use phase of the mode of transport</td><td>Country</td></tr><tr><td>Energy efficiency</td><td>kWh/passenger-km</td><td>City</td></tr><tr><td>Economic profitability</td><td>Costs (euro)/passenger-km for operators</td><td>City</td></tr><tr><td>External costs</td><td>€-cent/pkm (with congestion)</td><td>City</td></tr><tr><td>Institutional development and openness to mobility innovations</td><td>ROAD index—'the regulation openness for autonomous driving' index. It sets four variables to measure the level of readiness for the implementation of autonomous collective vehicles on open roads:<ol><li>National industrial policy</li><li>Local territories autonomy</li><li>National sustainable development policy and declination</li><li>Governance and integration at local level</li></ol>The score for each variable results in the Road Index for a city</td><td>City</td></tr><tr><td>Technical performance and reliability</td><td>Assessment of (1) average speed in km/h, (2) frequency or response speed in minutes of waiting time, (3) average occupancy as the average number of passengers on board at any given time and any place within a trip and (4) the percentage of kilometres driven autonomously</td><td>Local</td></tr><tr><td>System integration and efficiency</td><td>Five levels of MaaS integration suggested by (Sochor et al., 2018)</td><td>City</td></tr><tr><td>Reduction of risk of induced demand</td><td>Percentage of motorised modes of transport—car and buses—that the automated minibuses are replacing based on the reference modal share</td><td>City</td></tr></tbody></table>
<table><caption>**Table 17.3** Description of the demonstrator sites</caption><thead><tr><th>City</th><th>Pilot</th><th>Characteristics of route</th><th>Type of passenger</th><th>Deployment</th></tr></thead><tbody><tr><td>Lyon</td><td>Groupama Stadium</td><td>Fixed route with stops 1.3 km. Will become an on-demand, door-to-station service</td><td>Regular workers, people with reduced mobility (medical centre nearby)</td><td>November 2019 to April 2022</td></tr><tr><td>Copenhagen</td><td>Nordhavn</td><td>Fixed route with stops, 1.2 km, will become an on-demand, door-to-door service</td><td>Residents of the area, tourists</td><td>September 2020 to April 2022</td></tr><tr><td rowspan="2">Luxembourg</td><td>Contern</td><td>Fixed route with stops, on-demand, 2.2 km</td><td>Employees working at Campus Contern</td><td>September 2018 to April 2022</td></tr><tr><td>Pfaffenthal</td><td>Fixed route with stops, on-demand, 1.2 km</td><td>Workers, tourists, residents and visitors of Luxembourg City</td><td>September 2018 to April 2022</td></tr></tbody></table>

(Luxembourg), all sites presented very low occupancy. This result can be an indication of low demand for the offered mobility services. However, we should be cautious in this conclusion due to the unknown impacts of the Covid-19 restrictions. In addition, the energy efficiency could also be affected negatively in case the automated minibuses were equipped with more hardware and technical features, such as sensors, cameras, Lidars and communications.

As electric vehicles, automated minibuses seem to be a good alternative to tackle ‘local air pollution’. However, they are not a significant solution to tackle ‘local noise pollution’, as their noise level does not differ that much from other motorised modes of transport from 30 km/h speed. It considers that regarding the background noise and traffic density, EV does not differ from ICEV in the usual traffic, except for urban traffic during the night in low-speed areas (Jochem et al., 2016).

As temporary pilot trials, the automated minibuses present low system integration. Nonetheless, they show a high potential in the near future to have information, booking and payment integration within the public transport services, considering that in most cases, they are already deployed by public transport operators. In addition, it is expected that in the future, the automated minibus could be integrated into MaaS systems.

Concerning the technical performance elements (speed, frequency, occupancy rate and km driven autonomously), all sites struggle with low speed and low occupancy rates. The percentage of fully automated driven kilometres is 80–94%. The manual interventions that took place were mainly caused by wrongly parked cars and trucks.

The use of renewable energy for the use phase varies significantly according to the electricity mix of each region or country. In this case, Nordhavn in Copenhagen has the best score (with 62.4% of renewable sources in the electricity mix), and Contern and Pfaffenthal in Luxembourg are the lowest.
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<figure><img src="image_197.png" /><figcaption>**Fig. 17.1** Sustainability mobility radars from AVENUE pilot sites—with 1 for the worst performance and 5 for the best performance: (a) Groupama (Lyon); (b) Contern (Luxembourg); (c) Pfaffenthal (Luxembourg); (d) Nordhavn (Copenhagen)</figcaption></figure>

Currently, the ride on the automated minibus is free of charge. Therefore, passengers' affordability performs high. In the future, it is envisaged to integrate the automated minibus in the ticketing of public transport.

Overall, the economic profitability is still low due to the elevated costs with feasibility studies and legal authorisations, infrastructure works, high annual depreciation and salaries for on-board safety drivers (Antonialli et al., 2021).

Concerning the indicators of social acceptance, respondents show goodwill to use the automated minibus (scoring 3.80 in Lyon and Copenhagen and 3.5 in Luxembourg), while the perception of the readiness of the technology is lower (2.5 in Lyon, 2.4 in Luxembourg and 2.5 in Copenhagen). Based on real experiences, the users in Copenhagen pointed a good satisfaction (3.96) with the ride and the automated minibus services (comfort, information, punctuality, speed).

The indicator on 'reduction of risk of induced demand' scored low in Nordhavn; this is explained by the users' survey, which shows that the automated minibuses
have been replacing walking and cycling (17% and 45%, respectively). In parts, this
can be explained due to the vehicles' low speed.

All in all, the indicators reflect an incipient phase of deployment and develop-
ment of the technology. In the short term, key factors for improvement are:

1. The minibuses' occupancy, a key factor in fostering environmentally friendly mobility. The automated minibuses should be deployed to cover real mobility gaps and to provide rides with great potential to replace private cars. These factors are crucial to guarantee higher occupancy and reduction of the risks of induced demand and increase in vehicle kilometres travelled.

2. Better mobility services integration, as the integration of information, booking and payment.

3. Offer of permanent lines/services, on-demand services and higher speed as a factor to improve flexibility and reduce travel time.

4. Monitoring and planning the deployment in order to replace car trips.

In the medium and long term, the economic profitability of deploying the automated minibuses should become more attractive with the development of a legal framework and lower costs with feasibility studies, authorisations and exemption of safety drivers.

Concerning the SUMP concept, it is worth noting that automated vehicles and minibuses will not be sustainable per se, but rather their mode of deployment is very important, and factors such as shared mobility, ride-matching capacity and efficiency, system integration and means of transport it will replace proper policies and regulations. The automated minibuses should be integrated into urban public transport or within MaaS perspective and fundamentally aligned with the city's goals, planning and strategies for sustainable mobility. Also significant is to keep an integrated vision of the mobility system and, as highlighted by the SUMP approach, to develop all modes of transport in an integrated manner. Thus, the automated minibuses are a piece within the mobility ecosystem that could support intermodality, MaaS, mobility hubs and the use of soft modes of transport.

Concerning SUMP principles, the deployment of this new mode of transport and new mobility technologies requires more than ever long-term vision and planning, development of all transport modes in an integrated manner, cooperation across institutions, stakeholders and citizens' participation, performance assessment and monitoring towards established sustainability goals.

Therefore, the SUMP principles and the four steps/guidelines are a valuable tool for planning and implementing automated minibuses aiming at people's mobility needs and better quality of life. In this regard, the indicators are a tool to measure and monitor the progress and achievement of sustainable mobility planning and goals.
## 17.4 The Mobility Radar to Assess Two Potential Scenarios

This section explores the application of the indicators for two potential scenarios: (i) automated minibus integrated in MaaS and (ii) AV deployed as robotaxis. According to these two scenarios (detailed in Chap. 14), the level of integration of the automated minibus varies significantly and, as consequence, the potential impacts on mobility systems as well.

Figure 17.2 presents the mobility radar for assessment of the two scenarios (Nemoto et al., 2023). This is an explorative approach, these future perspectives need further research, and the enclosed developed hypothesis will be deepened on sites in the EU-funded ULTIMO project (2022–2026).

The assessment suggests that, overall, automated minibuses in MaaS tend to present a better performance than AVs deployed as robotaxis. As electric vehicles, the external costs for climate change and air pollution of both scenarios score high, whereas automated minibuses in MaaS score much better in terms of external costs for congestion and energy efficiency, considering that the average occupancy and sharing rate for automated minibuses in MaaS are higher.

The calculation of the external costs is detailed in Chap. 14, and it comprises air pollution, climate change, well-to-tank, noise, accidents and congestion (Jaroudi, 2021).

The social acceptance for robotaxis could be higher than for the automated minibus in MaaS, since they are akin to individual mobility; they could be a faster option (shorter waiting time), cheaper and not intermodal. Therefore, the social acceptance could be higher; although this form of deployment would be less sustainable. The robotaxis are very attractive for users and compete with public transport. The passenger traffic would thus be displaced from public transport to robotaxis and increase congestion (WEforum, 2020).

The economic profitability is assessed according to the costs (Euro) per vehicle-km; therefore, in VKM the operation of robotaxis is cheaper than automated

<figure><img src="image_198.png" /><figcaption>**Fig. 17.2** Sustainability mobility radars for scenarios deploying automated minibus in MaaS and robotaxis</figcaption></figure>
minibuses. However, when it comes to costs (Euro) passengers-km, the price for the automated minibus services is more attractive and affordable (Bösch et al., 2018).

Regarding the technical performance, robotaxis are expected to have a higher speed, with less waiting time; however, the automated minibus would present higher occupancy. And in terms of system integration, the automated minibus would be integrated in MaaS for data, information, booking, ticketing, billing for different mobility services etc. On the contrary, robotaxis would present a lower level of integration. This means that the integration of data, information and the related partners within the transport system is low, and no synergies or positive externalities can be enabled.

The sustainability assessment of the scenario is in line with MaaS and SUMP approaches, reinforcing that mobility system integration is crucial to fostering inter-modality and sustainable mobility. Additionally, the development of policy instruments and push-and-pull measures is a lever for a mobility shift from private and individual mobility towards public transport-centred MaaS.

## 17.5 Limitations of the Assessment

As this study analyses pilot trials, the sustainability assessment in this study is limited to a local and small-scale deployment, in addition to the technological limitations due to the development of the automated minibuses (Levels 3 and 4 of driving automation) and software. The assessment is also limited to just one type of vehicle provided by the same vehicle manufacturer.

Further, the application of the indicators is limited to data availability and data asymmetry from the pilot sites. For instance, the AVENUE representative and user surveys among the four demonstrator cities have different samples (n), hence, varying their representativeness.

It is also important to note that all the pilot tests within AVENUE project have been directly affected by the Covid-19 pandemic. The trials had interruptions; the number of passengers dropped, as had happened to public transport in general; and in some trials, the maximum number of passengers was limited to four in order to keep the social distance. For these reasons, the data collection and data availability for assessment were affected.

For some indicators, the assessment was simplified considering standard units of measurement available in the literature and commonly applied to other modes of transport.

## 17.6 Concluding Remarks

The sustainability assessment embedded results from the AVENUE project, targeting the social, environmental and economic impact assessment.
The assessment of the pilot trials points out that at the current stage, the auto-
mated minibus does not fulfil all the premises for sustainable mobility. However, the
automated minibuses prove to be feasible as new alternative mobility and with the
potential to support cities to achieve sustainable mobility under certain premises
(e.g. technological improvements, vehicle usability and occupancy, integration into
the mobility systems and intermodality, policies and strategies for sustainable
mobility).

Further variables and questions will influence the performance and assessment
of the automated minibuses in urban mobility, such as which modes of transport it
will replace, what will be the occupancy rate, how fast the technology and policy
development will occur, at to which extent automated minibuses will be integrated
into the mobility system and under which policies and incentives. Additionally, the
development of policy instruments is a lever for a mobility shift from private and
individual mobility towards public transport-centred MaaS. Such elements are key
for system innovation, and it comprises changes in governance, from a laissez-faire
approach to a ‘governing by enabling’ approach.

Finally, the perspective is that automated minibuses could be integrated into
urban mobility to improve the transport network, cover mobility gaps and foster
intermodality by substituting motorised vehicles and offering on-demand and door-
to-door services. The automated minibuses can be seen as a game-changer by
improving mobility services and offering attractive private mobility, being part of
the mobility innovations that target a system innovation and a shift from private to
a mobility that serves the general interest. Indeed, automated minibuses could sup-
port MaaS approach, electrification and shared mobility, and accordingly to the rec-
ommendations in our study, they can foster SUMP and the sustainable agenda
of cities.

**Acknowledgements** This project has received funding by the European Union's Horizon 2020 research and innovation program under grant agreement No. 769033.

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Part III

Future Vision of AVENUE
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Chapter 18

# System Innovation in Passenger Transportation with Automated Minibuses in ITS: The Citizen-Centric Approach of AVENUE

Guy Fournier, Michael Thalhofer, Johannes Klarmann, Philippe Chrétien, Dorien Duffner-Korbee, Adrian Boos, Ines Jaroudi, Eliane Horschutz Nemoto, Lionel Binz, Gabriele Naderer, Dimitri Konstantas, and Tobias Viere

G. Fournier (✉)

Pforzheim University, Pforzheim, Germany

CentraleSupélec, LGI, Gif-sur-Yvette, France

e-mail: guy.fournier@hs-pforzheim.de

M. Thalhofer

Software-driven Ecosystems and Market Places, Siemens Technology, Munich, Germany

J. Klarmann · A. Boos · G. Naderer · T. Viere

Pforzheim University, Pforzheim, Germany

P. Chrétien

Centre Européen d'Etudes de Sécurité et d'Analyse des Risques, CEESAR, Nanterre, France

D. Duffner-Korbee

Pforzheim University, Pforzheim, Germany

Fraunhofer ISI, Karlsruhe, Germany

I. Jaroudi

Pforzheim University, Pforzheim, Germany

CentraleSupélec, LGI, Gif-sur-Yvette, France

Fraunhofer ISI, Karlsruhe, Germany

E. H. Nemoto

Pforzheim University, Pforzheim, Germany

CentraleSupélec, LGI, Gif-sur-Yvette, France

ICLEI Europe, Freiburg im Breisgau, Germany

L. Binz

Cantonal Transports Office, Geneva, Switzerland

D. Konstantas

Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland
**Abstract** The first chapter explores three pathways for incorporating automated vehicles (AVs) into future mobility ecosystems: privately owned AVs, robotaxis and automated minibuses in a Mobility-as-a-Service (MaaS) and (later) in an Intelligent Transport System (ITS). The chapter emphasises that automated minibuses, when seamlessly integrated into a MaaS, could emerge as pivotal “game changer”, complementing and fostering other means of transport, in particular mass transport. Integrating in a next step AVs within an ITS could further make it possible to use mobility data and artificial intelligence (AI) to improve the transport system to a higher level of mobility evolution. By employing both fast and slow closed AI loops, it envisions a transport ecosystem that not only operates more efficiently and flexibly but is also consequently citizen-centric and addresses sustainability challenges. The successful implementation of this concept hinges on open data, Application Protocol Interface (APIs) and the potential of AI to create a self-learning transport system to serve the general interest. A sustainable and citizen-centric mobility is thus possible without a coercive (technology push) transport policy. Instead, it champions an approach that increases the transport options and enhances the provided mobility services (demand & attractiveness pull). Depending on the local governance, even privately owned or privately shared cars can be part of the model, justified, e.g. in areas where mobility infrastructure deficits loom large or when travel time is too high. This chapter therefore forms the basis for a vision to redesign our mobility ecosystem and marks the beginning of a disrupting system innovation, where integrated sustainability and citizen centricity reshape the nature of mobility.

## 18.1 Introduction

In this chapter, we will discuss and present the vision of AVENUE for a medium-term horizon (2030) of future public transportation. Our aim is to integrate automated minibuses into transportation systems available in a city. A central issue in this vision is the mobility needs of citizens which have to be satisfied in an optimal way: an abundant service offer portfolio with a high variety of private and public mobility modalities combined to one individualised intermodal trip. Automated minibuses play a central and critical role in this model: (1) as a feeder for the other means of transport, in particular mass transit, and (2) as a mobility complementor for the entire transport system in case of weak or not existing transport offerings (e.g. rural or remote areas), incomplete transport chains (e.g. for tangential connection) and disturbances of the transport system. Public as well as private transport operators (PTOs) are forming an enhanced public-private partnership (PPP) to utilise and synergise their multifaceted complementarity in this MaaS (Mobility-as-a-Service) vision. The application of advanced self-learning systems (e.g. based on human and/or artificial intelligence) could in a further stage let this visionary MaaS concept become a self-learning ITS (intelligent transport system). This disruptive transport system innovation could create best citizen-centric transportation with optimised and balanced private and public value. Further, it improves economic performance, supports environmental benefits and also increases acceptance by
citizens, as this intelligent transport system is enabling smooth transition towards efficient, safer, nearly carbon-free, inclusive and sustainable transport in cities.

The AVENUE vision of the future mobility system builds upon insights out of the AVENUE sustainability assessment and the conceptual transition studies and recommendations (see Chap. 19). The following research questions have been guiding the analysis:

* How can citizens' mobility needs, stakeholders' interests and general interest be optimally satisfied with AVs?

* How can AVs and automated minibuses be integrated in a future transport system in the city in a meaningful and optimal way?

* How should EU regulations evolve to support a reliable transport system that balances economic values of all stakeholders, regards privacy requirements and ensures safety and security issues and governance; general interest on social, economic and environmental level; freedom; and sovereignty (mobility data, technological sovereignty)?

These questions will be answered in the next sections of this chapter. The integration of automated minibuses into an intermodal and citizen-centric perspective is discussed in Sect. 18.2; the integration of the automated minibus within a MaaS system is designed in Sect. 18.3; and a deployment of automated minibuses integrated with artificial intelligence (AI) and intelligent transport systems (ITS) is proposed in Sect. 18.4. After a discussion in Sect. 18.5 about limitations of the vision, conclusions are presented in Sect. 18.6.

In terms of methodology, numerous interdisciplinary workshops have been organised with scientists, public transport operators, original equipment manufacturers (OEMs), industrial companies, public transport authorities and other stakeholders, both within and outside the project (see Chaps. 9 and 10). Various alternatives were developed and evaluated using a holistic approach. Experts from a wide range of fields, including automation engineers, IT specialists, economists, social scientists, psychologists, environmental scientists and lawyers, contributed to the discussions. The aim of this multidisciplinary dialogue was to ensure a comprehensive understanding of the interrelationships between different impact areas, potential rebound effects and conceivable futures. The results have been accepted by all project partners and the advisory board of AVENUE. A more comprehensive summary of this chapter has been published in 2023 by Fournier et al. (2023).

## 18.2 Integration of Automated Vehicles in Future Urban Transport Systems: From Product Innovation to Citizen- and Purpose-Centric Transport System Innovation

Automated vehicles can be integrated into future transport systems in different ways. Among others, three use cases are possible (based on Grisoni & Madelenat, 2021; Heineke et al., 2019; UITP, 2017):
**Private automated vehicles (AVs):** Like the currently dominant choice of privately and individually owned cars, AVs can be integrated into an urban mobility system. An integration of AVs in such way would lead to an incremental improvement of the individually used vehicle technology from today's driver-assisting systems to fully automated driving systems (Fraedrich et al., 2015; Deloitte, 2022).

**Robotaxis:** The definition for robotaxis inhere follows the description as shared automated vehicles (Fagnant & Kockelman, 2018). This definition is quite general and does not distinguish between vehicle sharing (car-sharing) and service sharing (ride-sharing, ridepooling, ride-hailing etc.; see MOQO, 2020). Within the understanding of the AVENUE project, robotaxis mostly appear as driven by private operators with commercial interest. They are meant to be not or just poorly integrated with the public transportation and therefore stand consequently mostly in direct competition to it. They are further mainly used for single ridership although those capabilities would support ridepooling services. Examples for robotaxis are Waymo, Uber Pool or Moia in the German city Hanover (see, e.g. WEForum, 2020; May et al., 2020; Clewlow & Gouri, 2017; Merlin, 2017; Jaroudi et al., 2021; https://www.moia.io/de-DE/stadt).

**Automated minibuses in a MaaS system:** Another pathway of integrating AVs into an urban transport system is the usage of automated minibuses as one optional transportation mode in a Mobility-as-a-Service (MaaS) system (see Fig. 18.1). The automated minibuses within the scope of this AVENUE project are defined as fully electric, automated vehicles without a driver [SAE Level 4 of automation; see SAE International (06-May-23)] and with a transportation capacity of up to 15 passengers. This vehicle can be combined with a means of either public (e.g. train, tramway, traditional or even automated fixed-scheduled bus line etc.) or private (e.g. bike-sharing, car-sharing etc.) transportation to a seamless journey.

The integration into a MaaS is meant to pool multiple trips of users that request an on-demand, door-to-door service. Furthermore, the transportation service can cover mobility gaps in the public transportation network (Sochor et al., 2016; Shen et al., 2018). Aside from their capability to fill the gaps of public transportation networks, automated minibuses via MaaS enable the collaboration of private and public transport operators (PTOs) to provide one individual intermodal trip to citizens that optimises travel time, costs and/or environmental impacts (Sochor et al., 2018; Kamargianni et al., 2015; Vleugel & Bal, 2017). MaaS in this case provides citizens the freedom to select the optimised transportation modes for its travel depending on its user profile, persona and even external factors (such as weather, etc.) (see Fig. 18.2). Technically, MaaS systems rely on the commitment to a standardised application programming interface (API) for all participating transport operators to offer seamless intermodal trips to its users (Sochor et al., 2018; Kamargianni et al., 2015).

These described three pathways each have advantages and disadvantages. In the development of privately owned AVs (1), the service can be seen as very convenient due to exclusive usage, achieving probably the highest degree of individuality and
<figure><img src="image_201.png" /><figcaption>**Fig. 18.1** The integration of the automated minibus into a MaaS system</figcaption></figure>

freedom. On the other hand, the costs of individual mobility would be very high for the users and not affordable for most citizens. A mass diffusion of this solution is accordingly not expected yet. It is also assumed to be one of the reasons why the automotive industry currently focuses on SAE Level 2+.<sup>1</sup> Nevertheless, in case of a wider diffusion of the privately owned fully automated vehicles, the convenience of these vehicles would encourage more vehicles on the road and lead to higher traffic

<footnote n="1">¹The term Level 2+ in automated driving context reflects a combination of SAE Level 2 automated driving with technologies to monitor the driver. This technology involves a powerful form of sensing and automated control while keeping the driver engaged and able to monitor. It is important to note that liability remains on the driver’s side of the equation, which still makes a driver mandatory in the car (https://www.spglobal.com/mobility/en/research-analysis/fuel-for-thought-waiting-for--autonomy.html).</footnote>
<figure><img src="image_202.png" /><figcaption>**Fig. 18.2** Illustration of various citizen journeys as combinations of all means of transport offered to a citizen by the MaaS concept</figcaption></figure>
volume and congestion costs (externalities). A weakening of public transport would be likely as well with growing market share (Fournier et al., 2020; Milakis et al., 2017).

It is also expected that robotaxis (2) will be widely accepted by citizen due to their affordability, availability and comfort, but they may not complement other means of transport in a synergistic way (Korbee et al., 2023). Robotaxis could in particular be in competition with and may substitute other means of transport, including cars, walking and public transport (WEF Forum, 2020). Individual uni-modal mobility could be more convenient and strengthened. Simultaneously, the transportation system as a whole would become less efficient and sustainable, also resulting in increased external costs due to additional traffic and displaced public transport (Meyer et al., 2017; Niles, 2019; May et al., 2020; Clewlow & Mishra, 2017; Rayle et al., 2016; Merlin, 2017; Childress et al., 2015; WEForum, 2020). Market fragmentation and asymmetric competition between this market leader and other private and public mobility providers could also result in the emergence of a dominant robotaxi provider (Hassani, 2018; Niles, 2019). For example, if private transport platforms are not providing open mobility data in the same way as public transport providers, data asymmetries will arise and lock in the customer in the robotaxi ecosystem. As a result, a dominant market position for private transport providers and the associated “winner-takes-it-all” effect could appear and bear the risk of rising monopolies within the transport system (Hassani, 2018; Hoffmann, 2021; Cabral et al., 2021). Similar situations can be observed for other online services. There is currently an oligopoly dominated by a group of companies known as GAFAM (Google (Alphabet), Apple, Facebook (Meta), Amazon and Microsoft) or now MAAMA (Meta, Alphabet, Amazon, Microsoft and Apple) (The Economist, 2022; European Commission, 2021; Cabral et al., 2021; Toledano, 2020).

Integrating automated minibuses into a Mobility-as-a-Service system (3) could finally be the game changer: public and private transport could become individualised, by offering, e.g. first and last mile trips, as well as a critical mobility gap-filler for the feasibility of seamless citizen journeys. The concept of providing better transport services, improved efficiency and flexibility in the transport system and positive externalities in this system puts citizens at the centre (Coyle et al., 2020; Coyle & Diepeveen, 2021). Automated minibuses in MaaS create multifaceted value for the stakeholders involved, resulting in a win-win situation for all.

Automated minibuses in a MaaS thus seem to offer more benefits for the stakeholders (travellers, TPOs, cities) and for the general interest (Becker et al., 2020). The integration of automated minibuses into public transport in urban, suburban and rural areas and into a MaaS system (our vision) is highly beneficial and advantageous for its stakeholders, in particular for citizens and private and public mobility providers as well as cities and regions (administrations/government) in general. This integration is understood as follows:

A customer-/citizen-centred approach allows the user to orchestrate an individually selected combination of all different types of transport into a gap-less and reliable trip that meets the different preferences of all population groups. As such, automated minibuses in MaaS are seen as a transformative force in urban mobility, capable of providing personalised public transport that is diverse, accessible
(including for PRM), inclusive and affordable. This could be an attractive and ben-
eficial alternative to the private car transport. “The results of a representative survey
among 1816 citizens (of which 1526 have privately-owned vehicles) in Lyon,
Copenhagen, Luxembourg and Geneva confirm that 45% of car drivers are ‘willing’
(22%) or even ‘very willing’ (23%) to give-up using their own car to use automated
minibuses to bridge the first and the last mile if this were available. If the service is
on-demand and door-to-door, the acceptance could be even 52% (respectively 28%
and 24%) in total” (Fournier et al., 2023, p. 3; Korbee et al., 2023).

For the transport operators, the “automated minibus in a MaaS concept provides a better exploitation of existing capacities and resources and a positive experience of fair competition” (Fournier et al., 2023, p. 3). In particular, automated minibuses are an important factor and a missing complementarity modality in making large mass transit systems such as rail, metro, bus etc. more attractive, more efficient and thus more profitable. By taking this perspective, all offered means or modalities of transport are potentially available for an individual trip design. The mobility choice and subsequently the modality choice of the citizen (the end user/customer as central “king/queen of the system”) are considered as right and definitive and not limited by the transportation portfolio of a single (private or public) transport provider (Fig. 18.2). The automated minibus integration into the urban mobility system contributes to avoid thereby the “winner-takes-it-all phenomenon”. On contrary, a win-win situation for all stakeholders in terms of value creation, fair value sharing, sustainable mobility and long-term purpose is achieved, as stands the Purpose Economy (Hurst, 2016; Business Roundtable, 2019).

For the cities (administrations), the designed concept would offer a significant optimisation of mobility excellence (efficiency, effectiveness, safety, quality of transport, user-friendliness etc.), serving the wider public interest and providing financially, environmentally and socially sustainable transport. The complementary use of public and private transport together, with automated minibuses, would therefore be able to make travel less costly, more competitive and more predictable and would support a higher level of resilience in the overall mobility system, decreasing external impacts of mobility (Alazzawi et al., 2018; Vleugel & Bal, 2017), and this will contribute to a new paradigm of sustainable mobility and to the Sustainable Urban Mobility Plan (SUMPs) that involve citizens and stakeholders in a goal-oriented approach that serves public interest. Moreover, it will increase the public acceptance through higher effectivity and flexibility in the whole system (Hurst, 2014; Nemoto et al. 2021; Korbee et al., 2023). Based on the results of the aforementioned survey, a transformation of the mobility paradigm could be realised without a coercive policy (push strategy). An improvement of the offer would change the behaviour (pull strategy).

Open data and interaction platforms and open APIs are prerequisites to enable access to all the means of transport, their interoperability and intermodality, to ensure balanced win-win situations among all stakeholders and thus a kind of democratisation of the automated minibus in a MaaS ecosystem (ERTICO – ITS Europe, 2019; Coyle et al., 2020). Open data and open APIs are further a condition to enable innovation and evolution of mobility concepts and develop the MaaS towards an intelligent transport system (ITS). Using data makes it easier to generate
information and knowledge and provide the basis for decisions to improve the transport system and to generate positive externalities. The transport system could become a self-learning system with the support of artificial intelligence and humans (see details in chapter below). A “circulus virtuosis” (virtuous cycle/loop) and positive externalities could be created, to guarantee safety and to promote global improvement. To achieve this ITS vision, several improving loops have been identified which could be implemented and which make the transport system more resilient and future oriented (see Sect. 18.4).

The third path, “automated minibus in MaaS”, can thus provide the best public value and therefore represents the long-term vision of AVENUE. It is the main enabler of a holistic system innovation and is not only regarded as a classic product (e.g. automated minibuses) innovation. However, this is not to underestimate the importance of automated minibuses as a product innovation: combining automated minibuses and MaaS represents a disruptive game changer to take the entire transport system to a higher level of mobility evolution. This evolution will be described in the sections with AV in MaaS (Sect. 18.3) and the next progress with AV in ITS (Sect. 18.4).

## 18.3 Integration of Automated Minibuses in a MaaS System: A Citizen- and Purpose-Centric Approach

The concept of purpose economy refers to a new way in which people and organisations create value and define the principles for innovation and growth (Hurst, 2016). The value lies in establishing purpose and creating meaning value for employees and customers beyond their own benefits, but aiming personal and community development (Hurst, 2016).

> The Purpose Economy creates purpose for people. It serves the critical need for people to develop themselves, be part of a community, and affect something greater than themselves.
> (Hurst, 2016)

The “citizen-centric automated minibus in MaaS” approach is in line with the purpose economy and targets a mobility system built by all and for all. The concept is focusing on the compilation of various transportation service modalities to one single seamless trip on demand and, according to the preferences of the citizen, including related services like trip planning, ticketing and others (European Commission, 2016).<fnref n="2" /> Citizens have the choice to choose from various transportation

<footnote n="2">² The citizen-centric approach naturally also includes older people and people with disabilities, as these groups are dependent on accessible public transport and expected to change fast future mobility demand. This is also required by law (European Accessibility Act). The ageing society and 80 million people with disabilities in the EU create a new demand for products and services and will have a major impact on mobility in the future, especially regarding the fact that public transport can influence how and where people live and whether they can maintain their independence. Future mobility services must take this into account. For example, booking services need to include special needs in their service portfolio. Aspects such as additional space (e.g. for guide</footnote>
options for their journey within or outside the city or even the country. To support them in their decision-making, the MaaS platform provides various functionalities such as simulations of time, distance, cost and CO2 footprint, as well as options for comparison. Once the citizen has made their decision, they are requested by the mobility integrator app to make the booking and place a binding order. The data from the selected trip option is then sent back to the MaaS platform and onwards to the private and/or public transport platform and transportation provider or possibly to other MaaS system platforms. “The purpose is to better satisfy citizen mobility needs and evolve towards a sustainable mobility system, create value for all stakeholders, contribute to sustainable societal transformations, and provide an answer to societal challenges (the so-called ‘purpose’)” (Fournier et al., 2023).

To achieve this, it is necessary to consider factors such as the availability and interoperability of both hardware and software devices, which are provided through standardised interfaces (APIs), as well as management and coordination software and services offered by an open ecosystem of service aggregators and other intermediaries. By combining digitalisation with a citizen-centric approach, the distinction between private and public transport operators will be blurred, leading to the introduction of coopetition (Haan et al., 2020). Coopetition is a new perspective that involves both competition and cooperation existing together, utilising complementary resources cooperatively (Liu et al., 2015). Additionally, democratised and federated governance of ecosystem stakeholders could be established, where beneficiaries are determined by their value-creating contributions to the defined purpose (Gassmann & Ferrandina, 2021; Schmück, 2022).³

Figure 18.3 depicts the integration of automated minibuses in a citizen-centric MaaS, with the citizens at the core. A variety of transport means is available for the citizens to choose from, as depicted in the middle circle in Fig. 18.3. The transport options are provided by various stakeholders (the actors named in the yellow blocks, at the outer circle of Fig. 18.3). Other governance scenarios to coordinate the different stakeholders are of course possible depending on market (private, public and coopetition) and data schemes (private or open data, open interfaces and protocols). These scenarios have been developed by UITP 2019a (UITP, 2017, 2019; Ertico ITS-Europe 2019; Capgemini, 2020). The scenario which fulfils best the needs of the customer, enables fair competition through avoiding the “winner-takes-it-all” phenomenon, enables positive externalities and fulfils sustainable and societal goals is the citizen-centric MaaS approach chosen by AVENUE. It defines a purpose, balancing the interest of the numerous stakeholders to serve the general interest.

Important for the success of the AVENUE vision is the full integration of automated minibuses into the described citizen centric MaaS system. To ensure a fair understanding of how automated minibuses could be integrated in the MaaS system,

dogs or walkers) and booking assistance for entering/changing means of transport, as well as barrier-free design of the apps used to book these services, need to be considered.

³Private transport operators are the owners of a private transport operation platform providing and managing private transportation services from various modalities by subcontracting private suppliers.
<figure><img src="image_203.png" /><figcaption>Fig. 18.3 Automated minibuses integrated into a citizen-centric MaaS</figcaption></figure>

a typical booking process which integrates automated minibuses is set up with the
following five steps:
<figure><img src="image_204.png" /><figcaption>Fig. 18.4 Vision of the mobility of the future: Step 1</figcaption></figure>

<figure><img src="image_205.png" /></figure>

These steps are detailed and explained below; each of the steps is visualised by a figure (Figs. 18.4–18.8). The step-by-step description starts with a current, conventional outline of the system, in which the citizen has to enquire about options and
<figure><img src="image_206.png" /><figcaption>Fig. 18.5 Vision of the mobility of the future: Step 2</figcaption></figure>

<figure><img src="image_207.png" /><figcaption>Fig. 18.6 Vision of the mobility of the future: Step 3</figcaption></figure>

has to make the decisions themselves, building up to a fully integrated system. With each of the steps, more complexity (through the integration of stakeholders and additional platforms) is added to the MaaS system.

Step 1: The first step in a MaaS booking process consists of the citizen choosing the destination of its trip. All combinations of several means of transport, offered by both public and private operators, can be chosen. Public transport operators provide
<figure><img src="image_208.png" /><figcaption>**Fig. 18.7** Vision of the mobility of the future: Step 4</figcaption></figure>
<figure><img src="image_209.png" /><figcaption>**Fig. 18.8** Vision of the mobility of the future: Step 5</figcaption></figure>

demand-responsive transport (DRT), which aims to optimise the match of transportation supply with transportation demand efficiently and uses automated minibuses, for the first and last mile and as mobility complementor, and conventional public transport (CPT) services, like bus, metro and train. Private transport operators provide transportation modes like taxi, ride-hailing like Uber, car-sharing, bike-sharing and other micro mobility devices like e-scooter.

Traditionally, the citizen is forced to select and schedule separately every modality from private or public transport operators or a combination of it on his own and singularly according to his trip planning from A to B and related modality compilation, as is visualised in Fig. 18.4. This also includes a multi-factor optimisation of the modality-portfolio sample regarding time, cost, travel requirements (e.g. luggage, wheelchairs), personal preferences and others, including inherent error-proneness.

Step 2: In the next step, the position and availability of an automated minibus is documented together with other public and private means of transport on the MaaS platform. This connective platform is visualised as the yellow bar in Fig. 18.5. To optimise the decision and selection process, these tasks are transferred from citizens to specialised intermediaries: a MaaS data service platform is installed. This data service platform is designed, managed and controlled by a MaaS data and service platform operator and governed by Public Transport Authorities (PTA) as regulation bodies, for instance, by extending the existing National Access Points (Art. 3 Delegated Regulation 2017/1926 of 31 May 2017). In this platform all necessary data about modality and transportation provider offerings and related metadata from private and public transport operators are integrated, managed and exchanged. In particular private transport operators should provide all of their journey-relevant
data which are necessary to offer a seamless intermodal trip to the customer. Providing data avoids an information asymmetry with public transport operators which are per law (Art. 4 and 5 Delegated Regulation 2017/1926 of 31 May 2017) obliged to share static and eventually dynamic travel data. This information asymmetry could lead to the aforementioned “winner-takes-it-all phenomenon” and to a dominant position of the private MaaS provider. Private transport operators are already obliged to provide the data foreseen under Annex I of Delegated Regulation 2017/1926. In addition, the private transport operators might receive a reasonable and cost-based compensation for providing this data: “Any financial compensation shall be reasonable and proportionate to the legitimate costs incurred of providing and disseminating the relevant travel and traffic data” (Art. 8 (4) Delegated Regulation 2017/1926 in fine).

In the case of an integration of automated minibus services in this MaaS platform, information about current location of the automated minibus and the status of occupation by passengers should be made accessible. However, this should not include personal data or only depersonalised data in accordance to privacy and security regulations.<fnref n="4" />

For this reason, only the status and telemetry of the data are provided to the private or public transport operators. To make the data available to all users in a standardised and quality assured form,<fnref n="5" /> an open application programming interface (API) is required, as well as commonly agreed standards. These APIs<fnref n="6" /> enable authorised applications to communicate, use one another’s functions and exploit data sets provided by other applications or databases (Matthes & Bondel, n.d.). They make it in fact possible for the stakeholder to communicate with other stakeholders in order to provide a uniformed offer to the MaaS user (citizen). APIs are considered to be the “connective tissue of the cloud”; they are essential to integrate the transport system. However, the urban mobility ecosystem is still very fragmented (Bestmile, 2020). To overcome this barrier, initiatives envision setting standards of open APIs to enable mobility providers to integrate services. Examples of such an initiative are the projects MyCorridor, MaaS4EU and IMOVE. These projects foresee the use of a “common language” to designing a transport service API, comprehending “the use of communication protocol and data format to security standards, basic methods and service calls, responses and general behaviour of an API” (MaaS Alliance, 2019). Another initiative, the Information Technology for Public Transport (ITxPT),

<footnote n="4">⁴This has of course to be deepened in further research: according to some studies, “Merely technically collected data could, due to the transformational impact of big data analytics, become personal data or even sensitive data and thus trigger the application of privacy and data protection laws” (LeMO D4.1 p. 16 and quoted references link).</footnote>

<footnote n="5">⁵This should be documented as required by the GDPR. The data users are considered as data controllers who have obligations regarding the processing of data. This obligation could be placed in another (existing or upcoming transport dedicated) legal basis than in the “generic” GDPR.</footnote>

<footnote n="6">⁶Applications need policies to communicate with each other. In the digitised world, access to systems through open interfaces is called an open API (Termer, 2017). APIs are blocks of code that allow developers to access smartphone functionality, such as using GPS data or turning the phone camera light on and off.</footnote>
focuses on open standards and procedures for integrated Information and Technology Systems for public transport (Rogg, 2021). Therefore, open interfaces, protocols and standards are key factors for MaaS ecosystems.<fnref n="7" />

Consequently, an open standardised API<fnref n="8" /> concept is initially required for a functioning MaaS platform. It has to connect the vehicle modality and its providers, the transport operators and the exchange platform and its platform operator. Additionally, they are utilised by further relevant parties of the ecosystem, like other MaaS systems (e.g. when trips are overreaching MaaS systems of other cities), or regulatory tasks of PTAs and city infrastructure management. For the automated minibus, its MaaS relevant information is transferred to the open data MaaS platform to be exchanged between the public transport operator (PTO) on the one side and the “MaaS data and service platform operator” on the other side. In addition to the exchange of data, open APIs ease the management of automated minibuses from different OEMs. This allows a flexible use of the individual vehicles as well as the entire fleet. A functioning system results in lower costs of operation as less vehicles are needed.

An important reason for an open API for the “MaaS data and service platform operator” is that in case a Public Transport Authority (PTA) desires to redesign the “MaaS data and service platform” itself, there is no barrier due to dependencies with the data model of the original equipment manufacturer (OEM).

In Step 3, the availability of other means of transport of other MaaS in a particular country or other countries of the EU is documented in the MaaS platform. The MaaS platform provides further data to stakeholders (city, PTA, PTO etc.) which can be used to improve the transport system (city planning, traffic, incidents, accidents, fleet management, vehicles safety). This is visualised in Fig. 18.6 by adding yellow arrows connecting the MaaS platform to the private operators, other MaaS systems and other stakeholders.

Step 4 integrates MaaS mobility service aggregators, as intermediary organisations between public and private transport operators and the citizen to provide the existing choice for the requested passenger journey. This new layer is introduced by local, national or supra-national private or public mobility aggregators and even by PTAs themselves. PTAs regulate, delegate and supervise this market. The mobility service aggregators are designed to analyse and process citizen profiles (in an

<footnote n="7">⁷A study conducted in nine cities divided data formats into the following three categories and found that all of these cities already have developer-friendly data formats. This can facilitate both application development and the handling of this data in the future. Therefore, a standard for data sharing should be created so that both small and large transit agencies can connect to the MaaS solution (UITP, 2019).</footnote>

<footnote n="8">⁸An open API allows universal access and allows application programmes to interact with each other and share data; those elements are crucial for the mobility system interoperability and modality. Data interoperability is essential in this case, as the quality and consistency of shared open data and the data format are crucial for MaaS (UITP, 2019). For data mobility, the free flow of information must be enabled, interoperability must be ensured, and the values and interests of the broader society must be safeguarded, as private actors will play a central role in exploiting the value chain of data (Passau Declaration 2020).</footnote>
anonymous manner, under Art. 4 (4) GDPR) and trip requests as well as general data about transport providers, vehicles and services and specific data about current positions and status of vehicle usage from public and private transport operators. Hence, this additional layer integrates information from both sides: trip request or transportation demand, and transportation offering services or vehicles, in combination with further information from other MaaS ecosystems and city infrastructure.

All relevant data are utilised for compilation of alternative trip options, as displayed in Figs. 18.2 and 18.7. Alternative trip options are selected based on the trip request, including individual preferences, but have to be protected by adequate privacy and security concepts and measures. These concepts must be completed/developed and deployed. Especially in the case of disruptions, critical scenarios, and accidents, these incidents will be analysed and evaluated using concepts and tools of self-learning systems and artificial intelligence (AI).

By applying algorithms of artificial intelligence (AI) to a MaaS ecosystem, the infrastructure and the value chain of transport companies can be significantly improved according to the needs of the citizens. Furthermore, with the support of AI all kinds of incidents, risks or problems within the MaaS ecosystem can be identified, tracked, traced and analysed in order to define and conduct strategies and measures for detecting, preventing and solving them.

AI applications comprise multiple functionalities for supporting success critical tasks, such as finding patterns and new insights, making predictions, interpreting unstructured data and interacting with the physical environment, their machines and humans (Scherk et al., 2017). One of the central topics of AI are self-learning algorithms of systems (e.g. based on neuronal networks) with the goal of finding independent solutions to new and unknown problems (Scherk et al., 2017).

To summarise, all potential trip-relevant information from the MaaS stakeholders has to be available, and the quality of related data has to be assured (i.e. static and dynamic real-time data, data validity, data privacy and security). This information should be provided to the MaaS mobility service aggregators, who are able to aggregate all transportation needs or requests from citizens on the one hand and all transportation modality offerings (including automated minibuses) on the other hand and match them in an intelligent way (supported by AI algorithms) according to citizen preferences (first priority, e.g. time efficiency, cost-efficiency, carbon footprint) and offering goals (second priority, e.g. route efficiency, vehicle utilisation) in order to subsequently provide the information of alternative transportation options to each citizen.

In Step 5, individual citizens can select their preferred transportation option for his trip and can place a booking and ordering with payment. The automated minibus is planned and reserved for the citizen. The mission is provided to the vehicle, which requires a return flow of the data (indicated by the blue dotted arrows in Fig. 18.8). The citizen has the choice to select between different transportation options for his trip from A to B in or outside of the city or even outside of the country. Hereby various valuable functionalities (like simulations about time or distance or cost or CO2 footprint, together with option comparisons) are provided to the citizen. After making this decision, the citizen is requested by the ticketing app to make the booking
and place the binding order. After ordering, the data from the selected trip option is sent back to the MaaS data and service platform and hereafter to the private and/or public transport platform and transportation provider or possibly to other Maas system platforms.

As mentioned above, automated minibuses in MaaS could be used to generate information and knowledge and provide in this way the basis for human or AI-based decisions to enable a self-learning resilient transport system. This will be the endeavour of Sect. 18.4.

## 18.4 Automated Minibuses Integrated in Intelligent Transport Systems: A System Innovation Approach for a Resilient Self-Learning Citizen- and Purpose-Centric City Transport

The concept of “citizen-centric intelligent transport system” combines the intelligent transportation system with the citizen-centric approach and sustainability. Intelligence in this context is provided by artificial intelligence (AI) technologies. An ITS aims to provide services relating to different modes of transport and traffic management, enabling users to be better informed and make safer, more coordinated and “smarter” use of transport networks. They include advanced telematics and hybrid communications including IP-based communications as well as ad hoc direct communication between vehicles (V2V) and between vehicles and public and private infrastructure (V2X) (ETSI, 2021). Another keystone refers to a self-learning system with data-driven approach and AI for the development of intelligent transport system for smart cities and sustainable mobility (Iyer, 2021; UNESCO, 2021). AI and digital transformation could support to identify common patterns of mobility and quantify the crucial factors affecting the efficiency of the whole system (Lucca, 2022).

Figure 18.11 depicts the citizen-centric intelligent transport system. At the core are the citizens as it follows a citizen-centric approach. A variety of transport means is available for the citizens to choose from; these are depicted in the middle circle in Fig. 18.11 in a similar way to Fig. 18.5. The transport options are provided by various stakeholders (the actors named in the yellow blocks, at the middle circle of Fig. 18.11). In addition to Fig. 18.5, data are used to generate information and knowledge about and within the transport system. An intelligent transport system can learn and improve through experience gained by diverse data; this procedure is visualised by seven AI loops in the outer circle. Highly relevant is that those positive externalities only work in closed loops. This means that collected data are not enough since there are still decisions that need to be taken to close the loop (e.g. to improve the transport system). This is the case when automated vehicles have “learned” from data how to avoid an obstacle or a critical scenario in the city and the improved drive algorithm and eventual related hardware have been uploaded (fast
<figure><img src="image_210.png" /><figcaption>Fig. 18.9 Citizen-centric intelligent transport system (ITS)</figcaption></figure>

<figure><img src="image_211.png" /><figcaption>Fig. 18.10 Automated minibus integrated in ITS: Loop 1</figcaption></figure>
<figure><img src="image_212.png" /><figcaption>Fig. 18.11 Automated minibus integrated in ITS: Loop 2</figcaption></figure>

<figure><img src="image_213.png" /><figcaption>Fig. 18.12 Automated minibus integrated in ITS: Loop 3</figcaption></figure>
AVENUE Vision of the mobility of the future:
a CITIZEN CENTRIC APPROACH

AV integrated in a human and AI based
ITS (Intelligent Transport System)

<figure><img src="image_214.png" /><figcaption>Fig. 18.13 Automated minibus integrated in ITS: Loop 4</figcaption></figure>

AVENUE Vision of the mobility of the future:
a CITIZEN CENTRIC APPROACH

AV integrated in a human and AI based
ITS (Intelligent Transport System)

<figure><img src="image_215.png" /><figcaption>Fig. 18.14 Automated minibus integrated in ITS: Loop 5</figcaption></figure>
<figure><img src="image_216.png" /><figcaption>Fig. 18.15 Automated minibus integrated in ITS: Loops 6 and 7</figcaption></figure>

closed loop). This can happen as well when a city chooses to change the traffic rules on places with a high level of safety problems (slow closed loop).

On-demand and door-to-door data, compliant with the General Data Protection Regulation (GDPR), could be delivered with unprecedented precision in quality and quantity. In fact, this data could be used to generate information, including decisions and knowledge about the transport system, enabling continuous improvement of the urban mobility system through state of the art, self-learning human and/or AI loops and controls. This is visualised by the loops in the outer circle of Fig. 18.11, which show the loops in detail. The loops in the outer circle of Fig. 18.11 illustrate this as they are designed around stakeholders to implement lessons learned to improve locally and globally. Data and information are generated by all components of the transport system such as the automated minibus, infrastructure, platform, applications and especially the user. These generated data add in return value to the other stakeholders which closes the loop and results in an optimisation of the overall transport system (ERTICO - ITS Europe, 2019).

The functional context of an intelligent transport system is systematically described by the following step-by-step illustrations.

Depicting the seven relevant loops (as indicated in Fig. 18.9):
Loop 1: OEM centred (within building blocks (BB) (Fig. 18.10)

Loop 2: PTA, city-PTO centred (between BB) (Fig. 18.11)

Loop 3: PTO-OEM centred (between BB) (Fig. 18.12)

Loop 4: PTA, city OEM centred (between BB) (Fig. 18.13)

Loop 5: PTA, city centred (within BB) (Fig. 18.14)

Loop 6: PTA-MaaS mobility service aggregator centred (between BB) (Fig. 18.15)

Loop 7: MaaS mobility service aggregator-citizen centred (between BB) (Fig. 18.15)

Each of the loops will be described and explained below. The descriptions are accompanied by visualisations (0, Fig. 18.15) of the seven self-learning loops to achieve a complete self-learning ITS ecosystem. Each loop builds upon the previous system. The coding within the graphical representations is defined in the following way:

Arrows have different meanings: Dotted arrows indicate the data flow; plain arrows indicate the information flow and the related decisions. Yellow arrows indicate the outward flow of data or information, blue arrows indicate the return flow and the clothing of the loop; a data exchange loop is represented by the combination of outward and return flow. The determined loops can be fast (because, e.g. just one stakeholder or one building block like an OEM is involved), medium or slow (when a consent between stakeholder (building blocks) or laws/vehicle specification/vehicle certification must be developed and published).

Loop 1, as depicted in Fig. 18.10, is an OEM-centred fast loop. Three data flows and one information flow are emitted by the automated minibus to the surrounding systems. The closed data loop on the left in Fig. 18.10 illustrates the data flow between the automated minibus and the road infrastructure and environment, generating both sensor data from the automated minibus' on-board and roadside systems. Characteristic for the second data stream is the long-time data storage in a virtual black box similar to those used in air transport. The automated minibus uses the backend systems to store permanently real-time data about recorded incidents and accidents. Access to this real-time data is also available for the PTAs (Public Transport Authorities), who then can optimise service quality and safety (Loop 2) for cities, PTOs (Public Transport Operator) and OEMs (Original Equipment Manufacturer). A further beneficial data flow is the direct connection to the PTOs. This linkage allows the PTO to derive route and service optimisations based on the real-time data about incidents or accidents. Further recommendations to optimise the road infrastructure, traffic rules or change of transport means become possible based on this data flow. Data of the vehicle can finally be used to monitor and optimise the charging of the vehicle. Maintenance costs of the vehicle can be improved as well to move from a curative (in case of breakdown) to a preventive (plan is better than cure) and later to a predictive (forecast breakdowns and predict a malfunction) maintenance.

Loop 2—PTA, city-PTO centred: The second loop, as depicted in Fig. 18.11, is PTA, city-PTO centred.
To ensure a trustworthy accessibility of the generated data for PTA, an intermedi-
ary scientific service provider (trusted AV third party) shall be established. This
trusted third party gets certified and operates on behalf of the PTA as non-biased,
neutral and objectively acting institution. As neutral institution it could analyse,
process and forward all data about any incidents and accidents to the PTA and pro-
pose improvements but also support for juridical treatment when necessary. They
could also proof the AI used by the OEM for their vehicles and transmit this infor-
mation to the PTA and provide recommendation for improvement measures for
regulation.

Based on the PTO-generated data, the PTO is as well able to provide learnings
and suggestions for technology or regulatory improvements to the PTA. Subsequently,
the data cycle of permanent information between the PTO and PTA to drive optimi-
sations on the regulations for service quality and safety is captured in Loop 2 and
also with the connection to OEM (Loop 4) (see in Fig. 18.13).

Loop 3—PTO-OEM centred: The third loop, (see Fig. 18.12) is PTO-OEM cen-
tred, and further completes the intelligent transport system.

Critical automated minibus scenarios are transferred from the OEM to the PTO empowered by AI algorithms. Subsequently, the data is then returned back from the PTO to the OEM. This provides the OEM with information on adjustments to the transport specification such as capacity, range, availability and persona profiles, adjustments to the vehicle specification and new and passive safety requirements. This enables the OEM to analyse the data and provide an optimised utilisations of automated minibuses by taking into account the new road safety requirements.

Loop 4—PTA, city—OEM centred: The fourth loop, depicted in Fig. 18.13, is PTA-OEM centred. Based on mobility data from the trusted AV third party, PTO and the PTA, PTAs can create and improve OEM certification requirements and (type) approval requirements and define automated driving regulations (e.g. vehicle architecture, AI algorithms etc.). For example, new active and passive safety requirements can be introduced from accident analysis and lessons learned coming from the third party.

Loop 5—PTA, city centred: The fifth loop, depicted in Fig. 18.14, PTA, City centred. This data cycle shows a slow closed loop of PTAs receiving information and data provided by all stakeholders and using them as a basis for improving urban infrastructure and traffic management, updating, monitoring and mobility (re) planning.

Loops 6 and 7 complete the ITS by adding both the citizens and the MaaS data and service platform. The two-way information sharing between the PTAs and the MaaS mobility service aggregator is key:

The data provided by the mobility aggregator and the MaaS data and service
platform provider on one hand informs the PTAs anonymously about the mobility
demands and patterns of the citizen (attitudes and behaviours).

In return, the PTAs are in a better position to enhance the mobility system and the
mobility infrastructure (Loop 5) aimed at inducing positive and reducing negative
externalities of mobility. Eventually, the data collected by the mobility aggregator
will allow the provision of customised unimodal or intermodal mobility alternatives to optimise the customer's choice according to their profile, persona or even the weather. Trip duration, expense and environmental effects provide a foundation for decision-making. To ensure a positive mobility experience, trust and satisfaction, safe travel and acceptance, these last loops are crucial.

Through the seven loops, the integration of automated minibuses in the future transport system could thus be beneficial for all the stakeholders, lower negative externalities, provide positive externalities and satisfy better the citizen needs and their acceptance. All the means of transport can be offered and combined to a seamless offer which is nearly as individual, safe and attractive as an individual car. With the integration of automated minibuses into an intelligent transport system, the whole transportation system could further be improved continuously. The transportation system would become more efficient but at the same time more resilient and flexible as failures could be determined in a predictive way and in case of failure in the transport system, automated minibuses could bridge the mobility gaps. Achieving these antinomic economic goals (efficiency and flexibility) into one transport system could make the transport system ambidextrous (Raisch & Birkinshaw, 2008).

Various upcoming digitalisation technologies like digital twins of automated (smart) vehicles as transportation objects, digital twins of (smart) citizens, digital twins of (smart) infrastructures and even digital twins of all kinds of stakeholders (PTOs, intermediaries, PTAs etc.) and mobility subsystems are enabling the governance, simulation, monitoring, tracing, analysis and optimisation of the entire mobility ITS/MaaS ecosystem. The AI-based self-learning loops of the mobility ecosystem could be ready for design and implementation into a future citizen-centric "Mobility Metaverse" aiming to optimise the physical/real "Mobility Universe" as a digital twin.

As the herein described concept is combining two disruptive and technologically complex innovations, there are also limitations that need to be reflected: on the one hand, there is the automated minibus itself as a product innovation and on the other hand MaaS/ITS as a mobility system innovation. The automated minibus depends on technical capabilities (perception and ability to determine, speed, level of automation etc.), the automated minibus environment (mapping and modification of routes, e.g.) and human involvement (for disabled access or the necessity of onboard safety driver, e.g.) that can be questioned even if fast improvements are expected via AI deep learning. These include the highly sensitive issues of user privacy and data and personal security, as well as the ethical issues that will always accompany automated driving (such as the trolley problem) (Fagnant & Kockelman, 2015; Geisslinger et al., 2021). The MaaS innovation is further driving a wider social, economic and environmental "ecosystem" innovation and will be impacted by an appropriate governance approach focusing on the general interest.

The main risks and limitations of the automated minibus in MaaS concept are addressed in the following section.
## 18.5 Discussion and Limitations

The aforementioned advantages of the AVENUE vision 2030 can only be achieved through the described disruptive product and system innovation. A prerequisite for achieving the AVENUE vision 2030 is an alteration of the current mobility paradigm. This paradigm is based on cheap fossil fuel energy, high CO2 exhausts, individual mobility (product orientation) and a linear economy (Fournier, et al., 2018).<fnref n="9" /> Altering this mobility paradigm requires social transformations in addition to technical innovations (such as automated minibuses) to accomplish a socio-technical transformation of the mobility system (Geels, 2011). A socio-technical transformation requires alterations in society, in business (ecosystem collaboration & market places, governance & management, vehicle/fleet demand & offering side) and in particular for stakeholders such as passengers, transport operators and related companies, technology provider, governance etc. For one, this entails an integration of stakeholders in designing, implementing and evaluating sustainable solutions for the mobility sector. As an optimum solution, all involved stakeholders should cooperate and agree upon a common purpose-centric strategy. This strategy should evolve towards a sustainable mobility system and, like mentioned above, create value to all stakeholders including citizen, public and private PTOs, OEM, city etc. and contribute to the transformation of our society in providing a no coercive answer to societal challenges.<fnref n="10" />

To fulfil this overarching purpose of serving general safety and sustainability interest, there is a strong need for stakeholders to harmonise different interests and cooperate with each other in an innovative coopetition mode. Known difficulties are that stakeholders will make choices based on knowledge available to them with the aim of maximising their individual gain. The combined outcome of the individual strategies could result in a situation that was not intended and not desired by any of the involved stakeholders [see, e.g. the prisoner's dilemma from game theory by Tucker (1950/1983) in Morrow].

To establish a mobility system as shown in the AVENUE vision 2030, some stakeholders could still try to promote individual mobility with private cars or take advantage of the “winner-takes-it-all” phenomenon to capture the value creation to the detriment of other stakeholders or even take control of the entire mobility system by taking control over a significant mass of captured system relevant data (private MaaS) of the mobility ecosystem and selling it to the system’s customers (such as city governments) and public transport authorities (PTAs). It is therefore crucial

<footnote n="9">⁹A linear economy is based on the “take-make-dispose” strategy. For a transition to a more sustainable society, one standard assumption is that the economy should transform into a circular economy. A circular economy is an economic system of closed loops in which raw materials, components and products lose their value as little as possible and systems thinking is at the core.</footnote>

<footnote n="10">¹⁰Discussions about stakeholders have existed since the 1930s and were raised in the World Economic Forum (WEF) in 1973 by Klaus Schwab. Freeman saw in his comprehensive approach stakeholders as essential to every management process. The stakeholder approach became afterwards so popular that in 2018, even BlackRock—the largest asset manager in the world—preferred the stakeholder instead of the shareholder approach to create sustainable value for all stakeholders.</footnote>
to coordinate the actors involved and to synchronise operations both vertically (between the actors along the different modes of transport of the journey) and horizontally (among actors operating the same modality, such as different automated minibuses) to ensure seamless intermodality and interoperability of the transport system, similar to a supply chain (Giusti et al., 2019).

For a successful socio-technical transformation of the mobility sector, this process needs to be managed, to avoid and overcome divergent interests and resistance. Research has shown that 70% of transformations fail, and part of the solution for the path to transformation includes moving fast, “shared vision and stretching aspirations” by leaders and a “more holistic and expansive approach to transformation” (McKinsey, 2020). In addition, cornerstones of public sector transformation relate to performance and organisational health (McKinsey, 2021). In the following, we have identified transformation challenges at the technical, environmental, social, economic and governance dimensions. These dimensions are part of a holistic and expansive approach to transformation, and they can influence each other through the interdependence of success factors and barriers.

Next, we present in more detail the success factors and barriers that need to be addressed in order to successfully implement the AVENUE vision 2030. For each of the dimensions, we subdivide into success factors and obstacles for the automated driving system and separately for the automated minibus integration in an ITS/MaaS system.

### 18.5.1 Technical Success Factors and Obstacles

In order to achieve the desired vision, there are several technical obstacles and success factors that need to be addressed and solved. The deployment of automated minibuses in all environments, both in urban and rural, requires the technology to be able to handle all safety-related scenarios and to be effective, intelligent, connected and fully implemented within the ITS infrastructure. The technical obstacles and success factors related to the automated driving ecosystem concern automated technologies, such as integral sensor systems and technologies. The automated driving ecosystem can be defined in the following categories, with reference to Table 10.3 (Chap. 10), which is specific to the on-demand and door-to-door solution developed by AVENUE.

**Automated shuttle capabilities:** In terms of active safety, LIDAR and camera technologies that detect and avoid obstacles as an integrated part of the decision-making engine will be key. Machine learning algorithms and AI technologies will enable continuous development of driving capabilities and vehicle positioning in traffic patterns and infrastructure. Perception and judgement, which are also related to AI technologies, will allow the vehicle to build up and constantly update a library of objects and obstacles, so that it knows the difference between a car, a bicycle, a truck, a pedestrian and so on. Radar technologies are needed to perform emergency
braking and detect large objects. The processing power of automated minibuses and AVs is developing rapidly around the world, and technologies need to be shared to achieve the highest possible processing power—and therefore both speeds and braking algorithms and other safety features—ultimately making AVs safer and more reliant than human drivers. Accident analysis will also lead to passive safety requirements, which have to be anticipated, as they are structural to the vehicle platform. Conventional crash tests are not applicable to existing automated minibuses, and a compromise will need to be found quickly.

**Automated shuttle environment:** The main factors here are mapping and commissioning of new routes and updates of already existing routes. Current automated minibuses can yet only drive on maps recorded by the vehicle manufacturer, which is time-consuming and costly to record. These recorded maps are not shared with other manufacturers. In the future, these “map routes” will need to be shared and constantly updated across Europe to allow automated minibuses to drive anywhere. Similar strategies are followed in Japan and South Korea. The processing power of automated minibuses and AVs needs to be developed at a rapid pace in order to achieve speeds, braking algorithms and other safety-related features. Such elements are important to ensure that AVs are safer and more reliable than regular drivers (as already described in the prior paragraph). All weather-sensing systems should be able to operate in all conditions: this robustness is fundamental to vehicle reliability, a key requirement for service quality and safety. Currently, heavy rain and snow still cause sensors to malfunction and lead to many operational problems. In these cases GNSS makes it possible to position the vehicle precisely. The mobility infrastructure of cities and other application areas (e.g. roads, transport facilities and services, roadside sensors, charging facilities, communication technologies, future: AI and digital twin-based traffic management) also plays a crucial role in the implementation of automated minibuses and should be considered in local development planning. Finally, cybersecurity is critical as laws and standards need to be further developed to include automated minibuses.

**Human involvement:** The key factor here is the need for a safety operator on board of automated minibuses. Human interaction with automated minibuses will not be eliminated, but the transition from on-board safety drivers to remote supervisors and local technicians need to be developed and tested. Currently, automated minibuses can only operate with a safety driver on board and, in many scenarios, with a remote supervisor at the same time. This is making the current implementations very costly. Finally, access for disabled people is critical, as human intervention is still required. Human-machine interaction and interface technologies play an increasingly important and critical role for passengers, supervisors and other stakeholders in operations and service applications.

With regard to automated minibuses in a MaaS/ITS ecosystem, as previously mentioned in Sect. 18.2 of this document, open data, open platforms and open APIs are key factors to ensure a consistent citizen-centric approach and a single-point-of-contact applications for travellers in ITS. This openness is a prerequisite for multi-sided access to stakeholders, in particular passengers, and for integrating all modes
of transport into a seamless journey. Interoperability and intermodality can improve performance and flexibility of the system, enable positive mobility externalities and reduce negative externalities. Open data, open platforms and open APIs are therefore a prerequisite for developing the benefits of automated minibuses in MaaS. The described AI loops are the precondition to enable an ITS with its benefits, but they require a collective effort for the common good. This process is challenging to manage as it involves several private economic interests of the related ecosystem.

These advantages however raise the risk of unauthorised tracking and traceability of passengers (privacy and safety of passenger data) and thus the risk of multiple undesired monitoring possibilities of mobility. In the public discussion on automated minibuses in MaaS, open data could lead to a major limitation and therefore needs to be technically secured (Meijer et al., 2014). With the definition of the open standards and regulations for data (e.g. GDPR) and APIs, as well as specific software-based technologies (ledger technologies such as blockchain), strong economic interests are further impacted as an ecosystem is open to all. A closed ecosystem could enable companies to capture and privatise the value of data in particular the positive externalities associated with it. The technical definition of the communication protocol, data format and interfaces has thus to be managed by the PTA and probably at European level to avoid security issues and “the winner-takes-it-all phenomenon” (see Chap. 2). The last point is important, as in an extreme case, the creation of private MaaS based on automated vehicles could limit the profitability of running mobility businesses but even severely limit the influence of the PTA in creating own rules for the urban mobility ecosystems.

## 18.5.2 Economic Success Factors and Obstacles

The results of AVENUE indicate that the business with automated minibuses is not yet profitable within the current automated driving ecosystem due to high driving-related personnel costs. This is mainly because safety drivers are still required to be inside the vehicle, and remote supervisors are currently legally forbidden or restricted in most European countries. The profitability of using automated minibuses in the fleet of PTOs is heavily impacted, making it a main restriction for usage in urban, suburban or rural areas. Furthermore, the high acquisition costs of the vehicles, elevated costs of feasibility studies and legal authorisations, infrastructure works and high annual depreciation are also seen as constraints (see Chap. 12; Antonialli et al., 2021).

The non-integration of automated minibuses with other means of transport within a MaaS reduces further the attractivity for passengers in terms of time and usability as several apps have to be used. Currently, there is no customer-centric approach that integrates automated minibuses with other means of transportation.

In both cases the technology has to be improved (see above), and governance (see below) has to be adapted. Regulatory sandboxes for self-driving vehicles could facilitate the testing and implementation of new technologies, leading to economies
of scale and accordingly lower prices for wider adoption (BMWi, 2019). The lim-
ited acceptance and integration of automated minibuses’ into mobility networks by
private and public PTOs, fleet owners and city/regional governments as B2B cus-
tomers is due to technology readiness and operational business risks, as well as low
subsidies for this innovative technology.

Integrating automated minibuses in MaaS/ITS is in fact one of the several pos-
sible future scenarios where the expected benefits are the highest due to the integra-
tion of societal goals. The governance discussed later will determine the benefits for
the stakeholders and the general interest. Accordingly, it is expected that the stake-
holders which will be negatively affected by serving the general interest could be
inhibitors of the required mobility transformation. The leading stakeholders of the
current mobility paradigm, such as the automotive industry and its employees, may
be inhibitors and unwilling to adapt to the new constraints like it could be observed
with environmental constraints (particulate matter) or electric vehicles. This is
understandable as a real alternative to the spatial and temporal flexibility of cars
could be provided and could satisfy the needs of the citizen (see Chap. 15): indi-
vidual mobility with private cars could be more easily substituted by automated
minibuses in MaaS/ITS, with the consequence of losing the shaping of the mobility
value chain. The PTOs and PTAs will have to further adapt to the new digital eco-
system and customer-centric approach. Cities and regional governments, which
would have to transform not only their operational mobility concepts but also their
strategic business ecosystem (including open data, platforms, APIs, towards a new
IT concept) from a “traditional” type of vehicle and (government) business ecosys-
tem to a balanced, federated or democratised governance and operational IT con-
cept with automated minibuses, could also face multilateral obstacles regarding the
cost and effort of transforming a “running system” and the often notorious lack of
budget as well as the difficulty of a necessary paradigm (mindset) change of public
authorities that are often risk-averse (non-entrepreneurial). However, even this kind
of disruptive (game changing) transformation of the business ecosystem can be
guided by individually adapted and designed approaches (e.g. incremental transfor-
mation steps, “think big, but start small and scale fast”).

Finally, all stakeholders tend to maintain their current habits: passengers, mem-
bers of organisations or the organisation itself try to reduce the psychological costs
of change.

Automated minibuses in ITS will last but not least require investments in digitali-
sation, hardware, software, training etc. In particular, as cited in the Digital Economy
and Society Index (DESI) 2021 (European Commission, 2022), the availability of
employees with digital skills could in particular slow down the digitalisation and
dematerialisation of mobility. The comparatively slow digital transformation of
companies in many EU countries is linked to “a lack of employees with advanced
digital skills” (European Commission, 2022). For example, by 2020, 55% of com-
panies will report difficulties in recruiting IT specialists (European
Commission, 2022).
### 18.5.3 Environmental Success Factors and Obstacles

The automated driving ecosystem of AVENUE shows a low environmental impact (see Chap. 13). A Life Cycle Assessment (LCA) of the automated minibus deployed within AVENUE indicates that its automated technologies account for less than 5% of the total energy used. In a near-future use case, 59% of the automated minibus impact comes from the use phase, while 39% comes from the component manufacturing phase (Huber et al., 2022). In addition, the automated minibus' environmental credentials depend on many factors such as occupancy, vehicle speed, mileage and lifetime.

The analysis of the energy demand of automated driving technology and the connectivity-related demand shows that, on the one hand, predictive, adaptive and information sharing through vehicle communication with infrastructure and other vehicles improves driving performance (e.g. braking performance) and, consequently, energy consumption. On the other hand, a highly connected vehicle means more data processing inside and outside the vehicle, which may outweigh the sustainability of V2X. Overall, the energy-saving potential of predictive driving features is likely to exceed the energy used for data transmission.

The AVENUE environmental impact assessment reveals that if automated minibuses are well utilised in terms of mileage and are used regularly by passengers, they will have major environmental advantages over single-occupancy vehicles (Huber et al., 2022). The automated minibuses are seen as complementary vehicle in public transport and could increase the availability, flexibility, efficiency, effectiveness and reliability of regional public transport (Huber et al., 2022).

More relevant is whether and how automated minibuses could be integrated into a MaaS/ITS ecosystem. Depending on the governance scenario chosen, automated minibuses can be used in a separate ecosystem competing with current transport modes (laissez-faire strategy). In this strategy, for example, the convenience of the robotaxi and the customer centricity of the robotaxi provider could substitute public transport, private cars, bicycles and even walking. This is expected to generate additional traffic in the city (rebound effects), with associated external costs such as congestion, CO2 emissions, additional space etc. The proposed automated minibus in a MaaS/ITS, on the contrary, aims to satisfy the best needs of the citizens and the general interest in the leveraging of positive externalities and the reduction of negative externalities. Positive externalities like cost-efficiencies gained from increased usage (Mohring effect), strengthening network effects, enhanced accessibility etc. can be generated additionally through the use of data, intermodality and interoperability.

### 18.5.4 Social Success Factors and Obstacles

An important finding of the AVENUE social impact assessment (see Chap. 15) is that users and potential users demonstrate a positive attitude and a receptive (good-will) attitude towards the automated minibus. There is therefore potential to
convince those who are not yet refusing, but who are open-minded, by means of targeted communication campaigns, particularly on social media. We defined five target groups of potential users (unreserved goodwill; sceptical goodwill; undesided; critical reservation; unconvinced refusal), which differ not only in their perception of perceived benefits and concerns but also in their level of knowledge, preferred transport system, willingness to use and pay for travel by automated minibus and sources of information used to form their attitudes. The results show that high levels of goodwill among potential users (e.g. car drivers) and high levels of satisfaction among users of public transport translate into high levels of willingness to use (again). In general, users were very satisfied with their experience, and most were willing to use the automated minibus again. In Copenhagen 76% of users are very willing to use the automated minibus again, and in Sion 55% of respondents are willing or very willing to use the automated minibus again.

The most important factors for social acceptance are the (perceived) need for improvement of the current situation and whether the proposed alternative service meets this need for improvement. Fears of a lack of safety or security are currently less important for social acceptance. In addition, real experience with the automated minibus has a generally positive effect on trust in the system. Therefore, an important success factor for the social acceptance of automated minibuses is to allow citizens to use and experience the advantages of automated minibuses.

Some of the critical points encompass the highly sensitive issues of user data handling and data security, as well as the ethical questions that constantly accompany automated driving (such as the trolley problem, which addresses the ethical dilemmas of the trolley's role in the car's life, and the question of whether or not the trolley's role is to act as a safety device for the driver) (Fagnant & Kockelman, 2015; Geisslinger et al., 2021). In addition, the results of a representative survey of citizens (n = 1816) indicated perceived concerns about the use of automated minibuses. The main concerns are related to the interaction of the automated minibus in unforeseen situations and with other road users (motorised and non-motorised), liability in the event of an accident, security issues and the risks of hacking and misuse of the software (Korbee et al., 2023).

In addition to this, the safety operators also mention that they are observing a high level of goodwill towards the innovative service. In the opinion of the safety operators, the users are highly satisfied, especially when subjective aspects such as the good atmosphere are taken into account, not least because of the lower number of passengers.

Based on their observations, safety and accessibility are qualities that are also evaluated as being satisfactory. In the viewpoint of the safety perspective, the automated minibuses meet the needs and requirements of users and contribute to a positive user experience.

A risk for the target groups of enthusiasts, uncritical goodwill and sceptical goodwill is that they may be disappointed if they recognise actual performance in terms of speed and flexibility. It is very important to increase both the speed and the flexibility of use through an on-demand service, or at least improved temporal and area flexibility (nearly as flexible as private cars) compared to existing public
transport services, in order to ensure that the high level of goodwill actually leads to a high level of acceptance of the new systems. This positive perception is a good point, but it cannot be taken for granted: the perception can change in the event of an accident, for example.

Beyond the current AVENUE approach, integrating automated minibuses into a MaaS/ITS system aims to best satisfy passenger needs and increase social acceptance. We could therefore expect automated minibuses to be a real “game changer” in the future, making public and private transport more personalised and providing a real alternative to individual privately owned vehicles as it has the potential to increase flexibility for users and choice for passengers while serving the public interest. The results of a representative survey of 1816 citizens (of whom 1526 have privately owned vehicles) in Lyon, Copenhagen, Luxembourg and Geneva confirm that 45% of car drivers would be “willing” (22%) or even “very willing” (23%) to give up using their own car in order to use automated minibuses for the first and last mile, if it were available. If the service is on demand and door to door, acceptance could be even higher (Korbee et al., 2023). Of course, this needs to be explored in future research if these measured attitudes can be changed in behaviour.

Another alternative could be the robotaxi which could satisfy best individual mobility needs without a change in transport mode but at the same time would reduce and privatise positive externalities and increase negative externalities through additional traffic and space. This choice could be better accepted than automated minibuses in MaaS in ITS but without serving the general interest. The choice between a non-integrated robotaxi in a MaaS and automated minibuses in a MaaS/ITS therefore needs to be discussed, organised and formalised in a future governance. Automated minibuses in an ITS/MaaS is of course the better alternative for the city and its citizens, but this requires a transformation towards a new social contract for mobility to avoid social frustration and crisis (Shafik, 2021; Rousseau, 1762). Special attention should be paid to digital illiteracy to avoid excluding parts of the population from the transformation process.

### 18.5.5 Governance Success Factors and Obstacles

The above limitations are all influenced by the chosen governance. Thus the governance is a key issue to enable the technical ecosystem and the integration of automated minibuses in MaaS or ITS.

For the automated minibus and its vehicle-related ecosystem, the development of regulations for the automated driving ecosystem encompasses amendments of the Vienna Convention on Road traffic and the adoption of a new legal instrument on the use of automated vehicles in traffic at the international level.<fnref n="11" /> At regional and

<footnote n="11">¹¹ See among others the work of UNECE's Group of Experts on drafting a new legal instrument on the use of automated vehicles in traffic (GoE LIAV).</footnote>
national level, it should be possible to approve the operation of automated mini-buses without strict requirements for the safety operator (in a first stage) and with a remote supervisor only (in a second stage),<fnref n="12" /> as the costs and time for type approval are a key issue for the diffusion of automated minibuses. In Member States that have not yet adopted AV-related legislation, regulatory sandboxes<fnref n="13" /> for self-driving vehicles, as mentioned above, could ease the disruption process and provide EU stakeholders with similar competitive conditions to other more liberal parts of the world to test and trial new technologies and facilitate their diffusion. Moreover, a legislative database at European level which brings up to date and real-time information about the fast-growing automated vehicles legislation (e.g. type approval, commercial, cybersecurity of vehicle, definitions, infrastructure and connected vehicles, insurance and liability, licensing and registration, operation on public roads, safety operator requirements etc.) on local, state and regional regulations is missing. This could simplify the planning of all the stakeholders like vehicle manufacturers, deployers of automated minibuses, transport operators, states, municipalities etc. and encourage the diffusion of the automated minibus technologies and the improvement of the transport system. The USA is a good example of how such a database could be managed (NCSL, 2022).

At the city or local authority level, it will be necessary to design, implement and monitor individual technical (vehicle, IT) certification and licensing schemes for automated minibuses and their integration into MaaS according to relevant regulations or standards (similar to TÜV in Germany) as well as related training courses for operational personnel (experts).

City or local authorities are also in the best position to understand the local mobility needs and where automated minibuses could provide new services and/or improve the existing services (as feeders).

Governance is therefore crucial for balancing these interests, although it is possible that the creation of closed ecosystems supported by the users who are individually highly satisfied by customer-centric affordable robotaxi could weaken the power of the PTA and the ability to balance interests.

Implementing AV for mobility is also a technical and innovative approach that believes in progress and science, using dematerialised consumption to save resources for a sustainable development (mobility) path. It will have to overcome the growing scepticism generated by debates on concepts such as “degrowth” or “frugality”, which promote a change in consumer behaviour through sacrifice in order to save energy and resources (sufficiency strategy).

<footnote n="12">¹² See, for instance, the French and German legal frameworks adopted in 2021, as well as the EU Commission's Commission Delegated Regulation of 20 June 2022 as regards the technical requirements for [...] fully automated vehicles produced in small series [...], C(2022) 3823 final, and Implementing Regulation of 5 August 2022 [...] as regards [...] the type approval of the automated driving system, C(2022) 5402 final, described in Chap. 16.</footnote>

<footnote n="13">¹³ As suggested by Art. 53 and 54 proposal for a regulation laying down harmonised rules on artificial intelligence (COM (2021) 206 final).</footnote>
For automated minibuses integrated into a MaaS or ITS ecosystem, public institutions have a crucial role to play in developing an interoperable, standardised and connected data landscape across different sectors, including the mobility sector in particular, through specific regulatory frameworks. The benefits of interoperable, standardised and connected data include improvements and new offerings in public services, an increased government efficiency, data-driven policymaking and the value of open data (Domeyer et al., 2021). The regional and national data strategies are also important: these address data protection and privacy requirements. Some of the challenges to interoperable and connected data stem from the fact that public and private data remain dispersed, not digitally accessible and not interoperable (referring to obstacles that prevent the combination and joint processing of data) (Domeyer et al., 2021). Currently, vehicle manufacturers can control who has access to which vehicle data, creating an undesirable “gatekeeper” position that will probably only be resolved partially by the cross-sector Data Act (European Commission, 2022).

To address these issues, European and national strategies for integrated data management are needed, as well as infrastructure and the setting of technical standards, as “fast and automated data exchange is only possible through harmonized data formats and standards” (Domeyer et al., 2021). The “European Data Strategy” aims to create a single market for data, and one of the main goals is to create safer and cleaner transport systems (European Commission, 2020c). For automated minibuses in MaaS or ITS, open APIs and open data are accordingly a prerequisite and a key to achieving interoperability and coordination of all stakeholders in the mobility system. As a result, seamless trips to meet the needs and acceptance of citizens become possible.

At the same time, this raises the difficult question and debate for regulation: who benefits from the use of data? This is clear for data collected in the event of an accident, as it will help to understand the causes and build anonymised lessons learned, to the benefit of all mobility stakeholders (see Figure Loop 1, Fig. 18.10), as feedback is shared. For other data, while individuals may have an interest in not sharing their personal data for privacy, safety or security reasons, private companies are interested in collecting these data to create new profitable business models (“winner takes it all”, advertising or other forms of value capture) but also to better meet customer needs. The use of data is further necessary to allow OEMs to optimise automated minibuses and make the drive more reliable and safer or to realise the loops described in Sect. 18.4. PTOs could use the data to optimise the use of the fleet and the PTA to serve the general interest. A balance therefore needs to be found and managed between the interests of the individual data provider (the mobility user and its stakeholders, GDPR) and the legitimate interests of the community in using the data to serve the general interest. These discussions should take place at European, national and local levels. This will require a less administrative corporate culture on the part of PTAs in order to manage the transformation and, in particular, to find the right balance between the numerous stakeholders towards the unity of society for sustainable mobility (purpose). A decentralised governance model which unifies the mobility network and supports a federated, democratic, decentralised
mobility platform using distributed ledger technologies (DLT, so-called Web3) to “integrate stakeholders and different technologies in a non-discriminatory and equal way” (Schmück et al., 2021) could accordingly be chosen. As a result, the beneficiary of the system would be directly the data subject rather than an intermediary party that owns the mobility platform and the customer touchpoint (Schmück, et al., 2021). As an alternative, other federation-based governance strategies could be used, where so-called substantive platforms are used to serve the general interest between stakeholders. The aim is to integrate economic dimensions to satisfy (mobility) needs on the one hand and political issues (through collective deliberation) to preserve the commons and enable purpose within a community on the other. Linking all stakeholders allows defining the needs, rules and structures of the ecosystem without intermediaries. The substantive platforms can vary and depend on the valuation chosen, which can be based on (classical) markets, on off-markets (based on donation, reciprocity and/or redistribution) or on hybrid forms combining market and off-market valuation. These new models are still exploratory. These respect GDPR by design (ethics by design).<fnref n="14" />

In terms of data protection and data privacy, the GDPR is an asset for personal data protection, safety and security, but it brings complex and challenging implementation in the field of connected and automated vehicles, as well as obstacle for AI (see the aforementioned loops within this chapter).

From a legal perspective, there are many challenges ahead, including the following. With regard to privacy and personal data protection, certain key concepts (e.g. “personal data”, “identifiable natural person”, “anonymous information” and “processing”) are defined and could be interpreted in such a way that the rules of the GDPR would apply, to the extent that this would require additional investment or hinder or slow down the development of the processing activities described in previous sections above (Sects. 18.3 and 18.4).

With respect to the data flows between the different stakeholders described in Sect. 18.4, the distinction between “controller” and “processor” is complex, taking into account the concepts of joint controllers, controllers in common and sub-processors.<fnref n="15" />

The “controller” is any person or entity that determines the purposes and means of the processing of personal data, and the “processor” only processes personal data on behalf of the controller. Hence, it may be difficult to identify the role of each actor intervening in this context their underlying obligations.

Only technically collected data (i.e. non-personal data) or personal data anonymised by the “GDPR filter” (described in Fig. 18.8 above) or by another

<footnote n="14">¹⁴Vercher-Chaptal, C., Acosta Alvarado, A. S., Aufrère, L., Brabet, B., Broca, S., et al. There Are Platforms as AlternativeS. Entreprises plateformes, plateformes collaboratives et communs numériques. [Rapport de recherche] DARES - Ministère du Travail, de l'Emploi et du Dialogue social; DREES. 2021. https://hal.archives-ouvertes.fr/hal-03413930/document</footnote>

<footnote n="15">¹⁵See LEVERAGING BIG DATA FOR MANAGING TRANSPORT OPERATIONS (LeMO), Deliverable 4.1 Report on the characterization of the barriers and limitations, September 2019, § 4.2.1 p. 17.</footnote>
<table><caption>**Table 18.1** Expected actions from public authorities in the development of MaaS and the different governing approaches by Audouin and Finger (2019)</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>Governing approaches</td><td>Corresponding actions expected in the development of MaaS schemes</td></tr><tr><td>Governing by authority</td><td>Develop specific legislation/regulation enforcing the development of MaaS in a top-down fashion<br>Procure MaaS to a third party through traditional tender mechanisms</td></tr><tr><td>Governing by enabling</td><td>Initiate public-private interactions<br>Define vision with strong quantitative objectives<br>Provide funding<br>Influence negotiations in favour of MaaS, and leverage MaaS opponents using horizontal network governance</td></tr><tr><td>Governing by doing</td><td>Develop a MaaS solution in-house in a closed manner<br>Minimise collaboration with third parties</td></tr><tr><td>Self-governing</td><td>Provide all government employees with a MaaS solution to show the example for citizens to follow</td></tr><tr><td>Governing by laissez-faire</td><td>Refuse to get involved in the development of MaaS<br>Adopt a wait-and-see approach</td></tr></tbody></table>

Source: Audouin and Finger (2019)

technique will be provided to the MaaS data and service platform operator. This data may become personal data or even sensitive data due to the transformative impact of big data analytics and thus trigger the application of the GDPR and further privacy and data protection laws.<fnref n="16" />

The potentially high volume of data (personal and non-personal) collected by automated minibuses can be assimilated to big data analytics, challenging certain core assumptions of EU data protection law, such as data minimisation and purpose limitation.<fnref n="17" />

The scope of the e-Privacy Directive (lex specialis to the GDPR) is much broader and applies not only to personal data but also to all information regardless of its nature. Consequently, this framework requires an assessment of which processing activities may fall within the scope of the e-Privacy Directive and, subsequently, what specific requirements the e-Privacy Directive imposes on stakeholders in relation to these processing activities. Furthermore, where personal data are involved, the interaction between the GDPR and the e-Privacy Directive needs to be closely examined.<fnref n="18" />

<footnote n="16">¹⁶ See LEVERAGING BIG DATA FOR MANAGING TRANSPORT OPERATIONS (LeMO), Deliverable 4.1 Report on the characterization of the barriers and limitations, September 2019, § 4.2.1 p. 16.</footnote>

<footnote n="17">¹⁷ See LEVERAGING BIG DATA FOR MANAGING TRANSPORT OPERATIONS (LeMO), Deliverable 4.1 Report on the characterization of the barriers and limitations, September 2019, § 4.2.1 p. 17.</footnote>

<footnote n="18">¹⁸ European forum and oBsErvatory for OPEN science in transport (BE OPEN), Open Science in transport research: legal issues and fundamental principles, 31 August 2020, p. 8.</footnote>
The (current) lack of standards for open APIs could lead to coordination issues, and it should be ensured that Member States apply the same standards to avoid the creation of technical barriers.

Finally, it is important to note that there are different governing approaches from public authorities for MaaS development (see Table 18.1). The aforementioned economic and environmental scenarios show that the governance determines if the rules serve the general interest and promote sustainable mobility and accordingly the technical, social, economic and environmental impacts. A governing by authority on one side which puts forward the general interest can be imagined as well as a governing by “laissez-faire” (e.g. voluntary lack of regulation or minimalistic regulation) on the other side where few private companies could take advantage from the “winner-takes-it-all phenomenon” of the mobility ecosystem and capture the expected benefits for private purposes. In the case of “laissez-faire”, potential competitors would be excluded (vendor lock-out strategy) and could not find a viable business model anymore. Vendor could also be complementary to the private ecosystem (vendor lock-in strategy) when an alternative market is not viable anymore and the competitor has to become a complement of the vendor (proprietary ecosystem owner). Even a threatening of Public Transport Authorities might be imagined when the private terms and conditions of the ecosystem of a private MaaS substitute to the public laws of the PTA. This has already been observed in other European markets. The lack of speed of adapting the rules of the PTA to the disruptive changes in mobility could hence generate the same problem that the EU had to experience before introducing the DMA (Digital Market Act). The aim of the DMA is to ensure the proper functioning of the internal market, by promoting effective competition in digital markets, in particular a contestable and fair online platform environment. More specifically, the DMA’s objectives are (1) to address market failures to ensure contestable and competitive digital markets for increased innovation and consumer choice, (2) to address gatekeepers’ unfair conduct and (3) to enhance coherence and legal certainty to preserve the internal market (European Commission, 2020d). As the DMA is not likely to apply to the mobility sector yet, an anticipatory approach for the local, national and European mobility markets which takes the specificities of the transport sector into account through a dedicated “mobility data act” is most likely needed and would accordingly be recommendable as well.

## 18.6 Conclusion

For all initial questions about “how to integrate AV in the city transport system to serve general interests”, the AVENUE vision could provide a convincing conceptual solution approach and a pragmatic transformation concept based on the hypothesis that automated minibuses integrated into intermodal transport and MaaS or better ITS can be a promising game changer in urban mobility. The automated minibus, deployed on demand and door to door, will provide more mobility choices and flexibility for all citizens, including better accessibility for people with reduced
mobility (PRM) and potentially better acceptance of AVs based on positive experiences.

As argued above (Sect. 18.3), a coopetition governance scenario and open data schemes (open data, open platforms, open interfaces and protocols) are further key factors to guarantee fair competition between public and private mobility providers, avoiding dominant position and “the-winner-takes-it-all” effects. In addition, AVs coupled to ITS and AI are expected to make the transport systems more reliable, safe, efficient and flexible (concept called ambidexterity), and thus the antinomic goals—incremental and disruptive innovation—are combined. As a result, the transport system becomes, through ITS and automated minibuses, citizen-centric, inclusive and sustainable, enabling positive externalities (Mohring effect, network effects, enhanced accessibility etc.) and lowering negative externalities (e.g. congestion costs, space, use of energy and materials). The citizen-centric approach could thus become purpose-centric, serving the general interest to the best for all stakeholders. This vision is a key for acceptance and thus coherent with the SUMP concept of a holistic approach and the requirement of the EU Green Deal (European Commission, 2020a), EU Sustainable and Smart Mobility Strategy (European Commission, 2020b, 2021) and the European Data Strategy (European Commission, 2020c).

Through the vision concept, the EU could gain on sovereignty with automated minibuses in ITS and become the worldwide mobility system leader in purpose-centric—this means citizen-centric, responsible, independent and sustainable for all the stakeholders—mobility which respects human and individual rights (data privacy and security, GDPR), by aligning and utilising future (technical and business) mobility product and system innovations for this purpose. This could be an alternative to the path of development chosen in other continents.

Of course, this approach is a socio-technical and innovative approach which believes in progress and science and uses dematerialised consumption to save resources for a sustainable development (mobility) path (McAfee, 2019). It will have to convince the growing scepticism which arouses in discussions around concepts such as “degrowth” or “frugality” which promotes a paradigmatic change in consumer behaviour through renunciation to save resources. This so called sufficiency strategy asks people to consume consciously and since around 13% of consumer spending by European citizens is spent on mobility, this issue is an important factor (Statista, 2020). Our vision shows a feasible and promising path of how dematerialisation of mobility through automated minibuses and ITS could save energy and resources without sacrificing individual mobility needs and comfort (Linz, 2004; Mauch et al., 2001).

This vision will be the basis to structure and deduct our transition goals, recommendation and transition roadmap to design the future (public) transportation service. The Horizon Europe project ULTIMO (Safe, Resilient Transport and Smart Mobility services for passengers and goods) implements parts of the concept from 2022. The concept has been further presented at Transportation Research Area Conference 2022 (TRA, Lisbon) (Fournier et al., 2023) and at the High-Level
Dialogue on Connected and Automated Driving organized in June 2024 in Ghent by the Belgian Presidency of the European Council (Flemish Ministry of Mobility and Public Works 2024).

**Acknowledgements** This project has received funding by the European Union's Horizon 2020 research and innovation program under grant agreement No. 769033.

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Chapter 19
# Transition Planning Towards a Sustainable Urban Mobility Ecosystem

<figure><img src="image_217.png" /></figure>

Check for
updates

Michael Thalhofer, Adrian Boos, Eliane Horschutz Nemoto, Dorien Duffner-Korbee, Markus Dubielzig, Christian Zinckernagel, Inès Jaroudi, Dimitri Konstantas, and Guy Fournier

**Abstract** In this chapter we aim to provide pragmatic recommendations and exemplary concrete transition measures for consultants and ecosystem stakeholders to get valuable practice proven cornerstones and hints for successfully achieving their individually designed mobility vision. Hereby we describe the transition

M. Thalhofer (✉)
Software-driven Ecosystems and Market Places, Siemens Technology, Munich, Germany
e-mail: michael.thalhofer@siemens.com

A. Boos
Pforzheim University, Pforzheim, Germany

E. H. Nemoto
Pforzheim University, Pforzheim, Germany

CentraleSupélec, LGI, Gif-sur-Yvette, France

ICLEI Europe, Freiburg im Breisgau, Germany

D. Duffner-Korbee
Pforzheim University, Pforzheim, Germany

Fraunhofer ISI, Karlsruhe, Germany

M. Dubielzig
Siemens Technology, Accessibility Competence Center, Paderborn, Germany

C. Zinckernagel
Holo, København N, Denmark

I. Jaroudi
Pforzheim University, Pforzheim, Germany

CentraleSupélec, LGI, Gif-sur-Yvette, France

Fraunhofer ISI, Karlsruhe, Germany

D. Konstantas
Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland

G. Fournier
Pforzheim University, Pforzheim, Germany

CentraleSupélec, LGI, Gif-sur-Yvette, France
planning as a systemic and systematic approach designed as a pragmatic process with essential key methods from the status quo (2020) of all existing states towards a desired common future vision of AV in ITS to be achieved in 2030. For this purpose we visualised a 'Big Picture' of transition management work packages and a roadmap as a 'common thread', conceptual guide and orientation for all stakeholders of the transition. This roadmap of multifaceted recommendations is structured into phases which are systematically building on one another, outlining the transition from the status quo with no automated minibuses (AM) via the milestones: AM, AM System, AM in MaaS, AM in MaaS/ITS to the future mobility Vision 2030 (Chap. 18). These recommendations are the basis for individual operational measures which have to be documented in an implementation plan. Finally, a general transition plan is suggested consisting of all scenarios, steps and planning results of the transition concept. This should serve all stakeholders as an orientation guide as well as a strategic and operational basis for the entire mobility transition.

# 19.1 Introduction

It is of course not sufficient to describe a desirable mobility future with AVs as mobility game changer (AV in MaaS/ITS) (see Chap. 18). A status quo state as a starting basis has to be considered as well and transition planning to understand how to realise and shape a future sustainable mobility ecosystem. This transformation has also to consider a high complexity of multifaceted challenges in a volatile, uncertain, complex ambiguous (VUCA) mobility environment.

To meet all these goals and framework conditions, a systemic and systematic approach to provide a clear structure and process for the stakeholders of the transition, especially providing strategic guidance to be manageable for the transition managers, has accordingly to be followed. Furthermore, the transition planning must be operationally feasible and deployable for the operational actors in a way that detailed goals and concrete pragmatic recommendations and roadmap-guided measures must be provided. Finally, all these transition deliverables must be compiled into an agile (incremental-iterative) comprehensive transition plan that ensures learning loops on strategic as well as on operational transition management level. A special focus must be set on the mindset and cultural issues of the transition in form of an explicit 'soft fact change management' since the transition of the mobility ecosystem requires a complex and disruptive paradigmatic change for all stakeholders of the transition together with accompanying and enabling management measures and roles like enablers, promoters, accelerators, multiplicators, change management board, etc.

Considering the goals, the giving framework and the mentioned guiding principles, the transition approach in this chapter will thus first explain the purpose
and goals of the transition (Sect. 19.2). In the third section, the design (Big Picture) of the transition management concept will be developed. After a description of the status quo states and the future vision (Sect. 19.4), the transition steps from the status quo to the future vision will finally be explained in Sect. 19.6.

The methodology for this transition concept has been developed and elaborated by experts from academia (e.g. Pforzheim University, CentraleSupélec), industry (e.g. Siemens AG) and transport organisations (PTOs) of the involved pilot sites (Geneva, Luxembourg, Copenhagen, Lyon).

## 19.2 Purpose and Goals of the Transition

The transition planning towards a sustainable urban mobility ecosystem is following a generic process in three phases: (1) analysis of the status quo of all relevant scenarios, (2) definition of a future vision to be achieved and (3) design of the transition from status quo states to achieve the defined future vision (see Fig. 19.1). This procedure ensures that the transition design is based on the balanced integration of both transitional forces: the ‘status quo push’ effect and the ‘future vision pull’ effect.

Purpose of the transition is finally to generate and suggest pragmatic recommendations and concrete transition measures for consultants and mobility B2B customers or ecosystem owners to get proven, structured and valuable cornerstones and hints for successfully achieving their individually designed vision starting from their individual status quo position.

Goal of this transition approach is to provide a pragmatic process and methods for achieving the desired common future vision of AV in ITS. This is based on a validated, detailed and consolidated understanding of the status quo and conducted in systematically designed and efficiently manageable process steps.

The following illustrations and descriptions are partially excerpts from the documentation of the work results from chapter D9.3 of the AVENUE project (roadmap for cost viable AV in MaaS business (Fournier, 2022)).

<figure><img src="image_218.png" /><figcaption>**Fig. 19.1 Generic phases of transition planning**</figcaption></figure>
## 19.3 Design (Big Picture) of the Transition Management Concept

The following overview represents a 'Big Picture' of all identified possible scenarios as levels (vertical dimension) and development steps (horizontal dimension) of the transition management concept from the status quo to the vision of AVs in an ITS. This is regarded as a systemic 'work package map' and a 'common thread' and thus as the systemic and orientational task basis for all partners to collaboratively contribute to this concept with defined deliverables (see Fig. 19.2). The so defined and structured work packages (in total regarded as a 'work breakdown structure') can be put in a sequence (as prioritised actions) for systematic elaborations.

Regarding the status quo analysis in *generic phase 1* (see Figs. 19.1 and 19.2: step 1), the status quo considered has been identified, described, analysed, summarised and compared each, and cases are built for why a scenario of automated vehicles (AVs) in a Mobility-as-a-Service (MaaS)/Intelligent Transport Service (ITS) environment is preferable to a pure laissez-faire strategy resulting in the development of robotaxis for individual transport. A laissez-faire strategy means no changes in governance are planned and new players enjoy a high level of autonomy, e.g. for creating an own proprietary ecosystem which could compete with public transport or enable congestion costs. In an evolutionary logic of scenarios, both—an existing scenario 1 and scenario 2—could be a preliminary stage of scenario 3, which would be a preliminary stage of a future vision itself. These activities are collectively represented in step 1 of the detailed overview of the transition management concept (see Fig. 19.2).

<figure><img src="image_219.png" /><figcaption>Fig. 19.2 Transition from status quo to AVs in ITS</figcaption></figure>
Focusing *generic phase 2* (see Figs. 19.1 and 19.2: *step 8*), the vision of AVENUE for a medium-term horizon (2030) of future public transportation has been discussed and defined (see details in Chap. 18). The aim hereby is to integrate automated minibuses into all citizen transportation systems available in a city. A central issue in this vision is the mobility needs of citizens which must be satisfied in an optimal way: an abundant service offer portfolio with a high variety of private and public mobility modalities should be provided and combined to one individualised intermodal trip. Automated minibuses play a central and critical role in this model (a) as a feeder for the other means of transport and (b) as a mobility gap filler for the entire transport system. Public as well as private transport operators (PTOs) are forming an enhanced public-private partnership (PPP) to utilise and synergise their multifaceted complementarity in this MaaS vision. The application of advanced self-learning systems (e.g. based on human and/or artificial intelligence) could in a further stage let this visionary MaaS concept become a self-learning ITS. This disruptive transport system innovation could create best human centric transportation, optimised and balanced private and public value and raised acceptance by citizens. This task of future vision generation is represented in step 8 of the detailed overview of the transition management concept (see Fig. 19.2).

In *generic phase 3* (see Figs. 19.1 and 19.2: *steps 2–7*), the essential building blocks for the systematic development of a comprehensive transition plan are represented. The elaboration of each building block has been conducted on every scenario level of the three identified scenarios (scenarios 1–3) for AV to ITS which avoids a not desired laissez-faire situation (scenario 4).

* The focus of step 2 is to identify the main critical success factors as well as the main obstacles of the transition of AV in ITS for each scenario (mid- or long-term relevance), from their status quos to the common future vision. This knowledge is of high importance for putting special attention on these factors at further elaborations of the transition concept. These factors include all facets of the comprehensive (business, technical, social, environmental and governance related) transition concept and thus also the main aspects of a stakeholder-focused change management.

* Step 3 aims to identify and characterise all stakeholders relevant for the transition towards the future vision of AV in ITS represented by a variety of structural designs of stakeholder maps from different perspectives (e.g. promoters, opponents, catalysts, multipliers) as well as characterisation tables.

* In step 4 the goals and strategies as well as business models of AV in ITS are defined for the transition of each scenario (mid- or long-term relevance), aiming at the achievement of the common future vision. These are based on the success factors and obstacles previously identified in step 2.

* Step 5 focuses on the identification or derivation of strategic and operational recommendations and their translation or refinement into concrete measures for action of AV in ITS for the transition of each scenario (mid- or long-term relevance).
* The task of step 6 is to design an implementation plan, integrating the previously elaborated operational strategies and measures in steps 4 and 5, as well as their prioritisation and timely assignment as a transition roadmap for each scenario (mid- or long-term relevance).

* Step 7 is regarding the compilation of all results of steps 2–6 towards a comprehensive, systemic concept of a transition plan(s) of each scenario (mid- or long-term relevance). As already mentioned in the evolutionary scenario logic in phase 1, the transition plans of scenario1 as well as of scenario 2 are oriented to the future vision of AV in ITS, but pragmatically aiming to prepare and achieve scenario 3 as mid-term transition plans. Scenario 3 as preliminary stage of AV in ITS as the future vision is adapting these previous stages focusing on the achievement of AV in ITS as a long-term transition plan towards the future vision.

## 19.4 Description of the Scenarios, the Status Quo (Step 1) and the Future Vision (Step 8)

By 2050, almost 68% of the world's population is expected to live in cities, indicating that urban areas will continue to experience rapid growth (Khor et al., 2022). Therefore, it is essential for metropolitan areas in particular, but also for connecting these areas with rural regions, to develop and use new forms of mobility as a complement to existing transport systems. One recent example for this is the rentable e-scooters that can now be used in most cities.

Conventional transport systems are made up of a mix of public and private transport. Local public transport is a vital economic and location factor, particularly in conurbations, but also in numerous medium-sized and small towns. From an environmental perspective, there is a strong interest in expanding and enhancing local public transport by decreasing the traffic volume from private vehicles while at the same time lowering environmental pollution through improved journey timing and further expansion of local public transport. It also offers a wide range of transportation options in the urban environment. Except for some local special forms such as cable cars, the commonly used forms of public transport throughout the world are buses, trams, metros and trains.

However, with a share of up to 70%, private cars are used far more frequently. In Germany, for instance, the share of total mobility in 2017 amounted to 43% as a driver and 14% as a passenger, which shows that 57% of approximately 3.2 billion total passenger kilometres per day were travelled by car (Federal Ministry for Digital and Transport 2019). Additionally, there exist private-sector mobility systems, like taxi and ride-hailing, regional and suburban rail, car and bicycle sharing, or the previously mentioned e-scooters.

Within the AVENUE project, the boundaries of automated driving representing different nested levels of mobility action fields are divided as shown in the diagram below (see Fig. 19.3). The first and most condensed level includes only the vehicle and the related minibus ecosystem (electric, automated, connected and
<figure><img src="image_220.png" /><figcaption>Fig. 19.3 System boundaries (Nemoto, 2022)</figcaption></figure>

rideshared). The mobility system level includes additionally the integration in the MaaS system to provide a seamless journey and a more resilient, convenient and flexible on-demand and door-to-door public transport system. The urban mobility system level builds up on an even higher level, including the aforementioned vehicle and mobility system levels. Here the effects of AV in MaaS are shown by a reduction of negative externalities as indicated in Chap. 14. The last and most inclusive level is the society. On this level, SDG 11, ‘Sustainable cities and communities’, is finally measurable. This shows the impact of AV in MaaS on cities, making them livable and more inclusive and actively working towards climate change mitigation.

### 19.4.1 Scenario 1 (at Generic Phase 1 or Step 1): AVs Without MaaS

The status quo of AVs, especially automated minibuses within the project, is explained in detail in previous chapters of this book (Chaps. 2, 3, and 11), regarding the trial use cases, international pilots, in-vehicle services (Chap. 5) and out-of-vehicle services (application or panel for information services, ticketing etc.), integration in public transport and much more. The main objective of the whole AVENUE project was to test and present this status quo and to identify what can be possible in future mobility scenarios. That’s why the description of different topics and details continues naturally throughout all deliverables of the project, e.g. ‘vehicle-to-platform interfaces’ or the reports from the pilot sites. The main part, however, has been regarded in the assessments for social impact, environmental impact and especially economic impact. The economic impact describes how the direct and indirect costs of the status quo are distributed. However, a meaningful summary is not feasible in a short form that makes sense, which is why the reader is referred to the economic impact chapters.
## 19.4.2 Scenario 2 (at Generic Phase 1 or Step 1): MaaS Without AVs

Considering the current state of transport options, it can be observed that mobility in cities has been transformed by mobility services such as carsharing, ride-sharing and on-demand services. These provide flexibility, convenience and customisation while helping to reduce the number of private vehicles and increase the use of mobility service providers. Hence, a multimodal transport infrastructure, which refers to the parallel use of different means of transport, is in place already. The emphasis is on making the serial use of different modes of transport on one route efficient and attractive. It is already a possibility right now to use different means of transport in every city, but it is in many cases not very intuitive to change and switch between them. Because of changes in travel behaviour and customer demand, new players are entering the market besides the traditional transport providers. This presents new challenges for public infrastructure and public transport services regarding planning, design, operation, regulation and financing. To enable citizens to use these different modes of transport and mobility offers in combination, and thus providing them with optimised route planning tailored to their needs, the MaaS concept is being adopted in a growing number of cities. This concept provides users with access to various means of transport with different mobility offers, which can be chosen, booked and paid for using an app. MaaS marks a shift away from a transport model where individuals travel by private vehicle to a model where individuals can choose between different travel service providers to create the optimal seamless itinerary.

In consequence, mobility is no longer viewed as a ‘commodity’, since owning a vehicle is no longer required to move around, but as a service where the journey is purchased. It is believed that the travel behaviour of citizens is shifting, and as a result the use of private vehicles could decrease. MaaS is made up of two components:

1. The mobility modes and mobility service providers like bike-sharing, scooter-sharing, ride-hailing, micro-transit and carsharing in addition to the existing transport modes (tramway, bus, train etc.).

2. A mobility platform where all mobility forms and mobility service providers are integrated, and the user can map out his routes and obtain the travel costs to then book the route and pay the mobility providers. This mobility platform will be provided to the user as an app.

Given the global trend of urbanisation, MaaS is projected to grow at a gigan-tic rate (Araghi et al., 2020). In certain cities, the first MaaS are already in operation, including companies and start-ups with promising concepts, such as the ‘Whim’ app (Whim online) or ‘Trafi’ platform (Trafi online), both with millions of users worldwide. In addition to examples like these, there are now also normal public transport providers such as Ruter AS in Norway, who already offer a functioning MaaS in Oslo and beyond, although without AVs so far (Ruter, 2019).
### 19.4.3 Scenario 3 (at Generic Phase 1 or Step 1): AVs in MaaS

To our knowledge, there is currently no MaaS that includes AVs. Even in AVENUE, this is only a vision for the year 2030, but it is far from being a reality. As mentioned above, a variety of MaaS environments exist worldwide in which AVs can be included. For this to happen, they must first be recognised and accepted as one of the reasonable building blocks of modern mobility. Therefore, the AVENUE vision for a future mobility of a MaaS with AVs is presented in detail in Chap. 18.

### 19.4.4 Scenario 4 (at Generic Phase 1 or Step 1): Robotaxis Without MaaS

This section details the status quo of development and testing phases of existing robotaxi players. A robotaxi is an automated taxi that can be hired by up to two passengers for individual trips.

As the population will continue to move to urban areas in the future, mobility in these areas will also undergo changes. The development and use of automated vehicles through robotaxis appears to be a realistic future scenario. A study on 'Urban mobility and autonomous driving in 2035' by Deloitte identifies five possible developments resulting from the use of automated driving services. Firstly, automated driving services could become the primary mode of transportation, with one in three journeys made by citizens in urban regions being made by automated driving services. In addition, the use of automated vehicles could result in a price war since they could be up to 25% cheaper compared to current public transport and private vehicles (Deloitte, 2019). Furthermore, the market potential of automated vehicles is very considerable. The Deloitte study estimates a sales volume of up to 16.7 billion euros per year. Nonetheless, this is also dependent on the business model of the providers and on future regulations. Still, it is expected that the use of automated vehicles will decrease the number of private vehicles in cities, but that the use of driving services will increase traffic volumes in total. In consequence, up to 40% more vehicles can be on the road simultaneously during peak times. With higher traffic volume, the risk of congestion rises, and the average speed in cities declines (Deloitte, 2019).

The forecasts of this and other studies reveal a large market potential for established mobility companies as well as for newly created start-ups globally. The most important, or at least most recognised, companies with respect to the development of automated vehicles are Cruise, Waymo, EasyMile and Mobileye.

### 19.4.5 The Future Vision 2030 (at Generic Phase 2 or Step 8): Automated Vehicles (AV) Within MaaS and ITS

The vision of the AVENUE project for future public transportation in an urban environment integrates both personal transportation and public transportation in mass transit. This vision of future mobility is outlined using the citizen-centred approach.