2019; Milakis et al., 2017; Moreno et al., 2018). Reduction in parking space is expected (Zakharenko, 2016).

The long-run consequences of this scenario is an increase in PT ridership, as the AM provide seamless intermodal and last-mile trips and is considered as a mobility gap filler. However, the convenience of the service could replace more short-distance trips from walking and biking (Shen et al., 2018).

### 14.2.2.7 General Comments

It is important to note that the speed in all scenarios is limited to 30 km/h for all road transportation. The average occupancy factors are those of the Delft report (van Essen et al., 2019) for traditional road transportation. Also, we follow the Huber et al. (2022) assessment where the AM occupancy factor is five passengers. We assume an occupancy factor of 1.2 for the robotaxis which is more comparable to a normal taxi where individual trips are significant. These factors vary in suburban scenarios to translate its deployment particularities. Table 14.2 presents the different occupancy factors.

### 14.2.3 Modal Shifts

The modal shift is essential to determine the direct and long-term consequences in the qualitative description above. Moreover, it is a key component to the externality estimations. Thus, some assumptions were needed to determine the AM modal share in each scenario. The modal shift explanation and resources for each scenario are in Table 14.3.

<table><caption>**Table 14.3 Modal shift for the scenarios**</caption><tr><td>Sc1</td><td>Scenario 1</td><td>Replace bus share</td><td>–</td></tr><tr><td>Sc2</td><td>Scenario 2</td><td>Replace car share</td><td>–</td></tr><tr><td>Sc3</td><td>Scenario 3</td><td>–20% from cars<br/>–6% from buses<br/>–13% walking</td><td>Ward et al. (2019); Clewlow & Gouri, (2017); Heineke et al. (2019)</td></tr><tr><td>Sc4</td><td>Scenario 4</td><td>Depends on willingness question</td><td></td></tr><tr><td>Sc5</td><td>Scenario 5</td><td>Depends on willingness question<br/>4% from buses</td><td>Mancret-Taylor and Boichon (2015), Adra et al. (2004)</td></tr><tr><td>Sc6</td><td>Scenario 6</td><td>Depends on willingness question<br/>Based on journey to a train station in intermodal trips<br/>3% from walking<br/>1.7% from biking</td><td>Paydar et al. (2020), Giansoldati et al. (2020), Gebhardt et al. (2016)</td></tr></table>
### 14.2.4 The Representative Survey

The social impact assessment is based on surveys to study the potential acceptance of users and the trends related to AM use. In this assessment, we focus on the willingness questions that were part of the representative surveys. These specific questions help determine the potential modal shift in some scenarios.

Scenario: AM in MaaS: The modal share of cars to be absorbed by the AM is determined by using the question: “how willing are you to give up your car if AM offers a service that bridges the first and last mile?”. The modal share corresponds to the percentage of respondents who were very willing, who consider their residential area as a city centre and who use their cars daily.

Scenario: Expand the network and targeted expansion of the network: the modal share of cars to be absorbed by the AM is determined by using the question: “How willing are you to give up your car if AM is part of a seamless, intermodal trip?”. The modal share corresponds to the percentage of respondents who were very willing and who consider the area they live in to be either a big town or a small to medium village and who use their cars daily.

## 14.3 The Externality Methodology

### 14.3.1 Production Phase

The manufacturing of vehicles is a complex process; it accounts for steps from the extraction of raw materials to the production of the components (Chester, 2008; Pero et al., 2018).

The assessment focuses on the climate change impact on the production phase. The emissions are CO2, CH4, and N2O; it is based on climate avoidance costs. According to van Essen et al. (2019), the avoidance costs are determined by averaging values from the literature for the short and medium term (up to 2030) and the long term (2040–2060). The values used in this estimation are central and short- to medium-run climate avoidance which is 100 €/tCO2eq.

The marginal cost is estimated as follows:

$$ \text{Marginal cost for production} \left( \notin - \frac{\text{cent}}{\text{km}} \right) = \frac{\text{total emissions of CO2e}}{\text{during production (tCO2e)}} \times \frac{\text{expected lifetime mileage (km)}}{\text{the average occupancy}} \times 100 \times \text{central short run value (\notin)} $$
<table><caption>**Table 14.4** Marginal cost for the production phase</caption><thead><tr><th>Vehicle</th><th>Bus</th><th>Car</th><th>Electric car</th><th>AM</th></tr></thead><tbody><tr><td>Marginal cost in €-cent/pkm</td><td>0.18</td><td>0.38</td><td>0.64</td><td>0.3</td></tr></tbody></table>

The total emissions, as well as the expected lifetime mileage, were determined using the ecoinvent database for the car and electric car, while for the bus, the values were taken from Chester and Horvath (2009). Finally, Huber et al. (2022) as well as Viere et al. (2021) study on the LCA of the AM were used. More details about the estimations are found in the environmental impact assessment chapter. The marginal costs are presented in Table 14.4. These values are applicable at the EU level.

### 14.3.2 Parking Space

In the analysis, only the savings from parking spots for private vehicles are considered. The buses are usually stored and maintained in dedicated bus garages or depots, and they do not interfere with daily traffic. Although it raises an issue for land-use and transportation planning, it presents less nuisance to cities compared with private vehicles and thus, it is not covered in this assessment, similar to the AM (Lai et al., 2013).

To determine the overall parking space, the fleet size for cars and the AM is needed in each scenario. Thus, the fleet size calculator that is presented in the previous chapter is used. Moreover, we assume a linear relation between the parking space reduced and the modal share of car trips reduced.

## 14.4 Assumptions and Boundaries

To be able to calculate the external costs and build on work presented in Antonialli (2019) and Jaroudi et al. (2021) assessment, we assume the following:

The AM deployment could affect motorised mobility, public transport and active mobility (Janasz, 2018). The AM are introduced in mixed traffic (with no prior presence of automated technology on the roads).

Every additional person-kilometre travelled on the AM is travelled less on the other transportation modes. Therefore, the total transport performance remains identical compared to the reference scenario (Bubeck et al., 2014).

Active mobility's negative externalities as walking and biking are considered negligible (Keall et al., 2018).

The study does not account for the increase in transport performance due to population increase and other sociodemographic changes; we are focused on the effect of unpredictable factors such as the integration within the transportation system and policies. That is why we chose to simplify the calculations by omitting their effects (Huss & Honton, 1987).
Intermodal trips consist of two or more modes of transport (car/bus/walking/biking+train) (Fraedrich et al., 2015), while monomodal or unimodal trips are considered as one mode.

Walking trips accounted as a mode of transport for intermodal trips from 600 m or more (more than 5 min) (Gebhardt et al., 2016).

The average speed of circulation for all vehicles ranges from 25 to 30 km/h.

The AM operate on “other urban roads” or “other interurban roads” based on the scenario, and we estimate that the average traffic flow<fnref n="1" /> is near capacity.

## 14.5 Application of the Externalities Model to Geneva

In the following part, the assessment applies this model by adjusting the marginal costs (€-cent per pkm) to the context of the city and estimating the future transportation performance in person-km (PKM). It considers the four cities of AVENUE as a case study. First, Geneva is studied in detail to showcase the specifics of the scenarios and the externalities calculations. Second, the results are presented for Luxembourg, Lyon and Copenhagen, applying the steps shown in Fig. 14.4.

<figure><img src="image_151.png" /><figcaption>Fig. 14.4 Externalities calculations and data methodology</figcaption></figure>

<footnote n="1">¹Traffic flow = volume of traffic/capacity of a traffic on a link (“near capacity” is v/c between 0.8 and 1, “congested” between 1 and 1.2, “over capacity” is above 1.2)</footnote>
### 14.5.1 The Mobility Behaviour Profile in Geneva (National Census Data)

Geneva is the second most populated city in Switzerland, with 197,376 inhabitants. The “Mobilités 2030” long-term strategy aims to address mobility challenges in Geneva Canton, such as underdeveloped public transport in low-density areas, narrow city centre streets and the absence of tangential routes. The promotion of low-carbon mobility is part of the Climate Plan 2018–2022 in Geneva Canton. Automated driving could replace motorised individual mobility, promote active modes of transport and make the transportation system more robust against crises. The legal framework for autonomous vehicles is being developed, and the “Digital Switzerland” strategy will address the necessary digital key factors for autonomous driving implementation (Etat de Genève, 2013; Jenelius & Cebecauer, 2020; OFCOM, 2018).

### 14.5.2 Mobility Behaviour: The Reference Scenarios

To calculate the externalities, we refer to the current status with no AV on the roads, as mentioned in assumptions and boundaries. Based on the data available, we used the mobility census for Geneva in 2015. To distinguish between our urban and suburban scenarios, we present two reference scenarios; each is characterised by the modal split, average daily travelled distance per the mode of transportation, area in km2, population, and transportation performance (pkm). The first one is for the city of Geneva, while the second is for the second suburban ring, also where the Belle Idee pilot is operated.

The analysis relies on mobility behaviour data from a census (microrecensements mobilité et transports (MRMT)) realised between 2000 and 2015 by the statistical office of the Canton of Geneva (Montfort et al., 2019) as well as Geneva Public Transport-TPG (2016). The MRMT is an extension of a Suisse national census comprising the participation of 4500 residents. The census accounts for the evolution of mobility trends for 15 years (Montfort et al. (2019)). The analysis provides trip distribution and the average daily distance per mode of transport in 2015. These values help estimate the total pkm per mode of transport.

#### 14.5.2.1 Reference Scenario (Sc01): City of Geneva

This scenario is the comparison point for the scenarios “replace all cars” and “replace all buses”, “Robotaxis” and “AM in MaaS”. The trip distribution and the average daily distance per the mode of transport in 2015 are computed in Table 14.5. The population is 197,376 in the city of Geneva (Rietschin, 2015). The area in the city of Geneva is 15.93 km2 (Service de la mensuration officielle, 2005). The overall daily trips amount to 711,000 trips (Montfort et al., 2019). The modal split is estimated using Montfort et al. (2019) and TPG (2016) analysis for
<table><caption>**Table 14.5** Mobility behaviour and transport performance in the city of Geneva 2015</caption><thead><tr><th></th><th>Mode of transport</th><th>Total transport performance in million pkm = Transport performance * population</th><th>Transport performance (pkm)</th><th>Average daily distance (km)</th><th>Modal share (trips)</th></tr></thead><tbody><tr><td>Private mode</td><td>Private cars</td><td>972.57</td><td>4927.50</td><td>13.50</td><td>22.60%</td></tr><tr><td rowspan="2">Public mode</td><td>Bus</td><td>389.03</td><td>1971.00</td><td>5.40</td><td>12.30%</td></tr><tr><td>Tram metro</td><td>-</td><td>-</td><td>0</td><td>6.40%</td></tr><tr><td rowspan="2">Active mobility</td><td>Biking</td><td>72.04</td><td>365.00</td><td>1</td><td>6.80%</td></tr><tr><td>Walking</td><td>187.31</td><td>949.00</td><td>2.6</td><td>47.60%</td></tr><tr><td>Other</td><td></td><td>72.04</td><td>365.00</td><td>1</td><td>4.30%</td></tr></tbody></table>

<table><caption>**Table 14.6** Marginal and total costs in €-cent per pkm in city of Geneva</caption><thead><tr><th>Externality category</th><th>Car (marginal)</th><th>Bus (marginal)</th><th>Car (total)</th><th>Bus (total)</th></tr></thead><tbody><tr><td>Air pollution</td><td>0.63</td><td>0.76</td><td>6.09</td><td>2.94</td></tr><tr><td>Climate change</td><td>1.31</td><td>0.44</td><td>12.72</td><td>1.70</td></tr><tr><td>Wtt</td><td>0.42</td><td>0.19</td><td>4.09</td><td>0.74</td></tr><tr><td>Noise</td><td>1.92</td><td>0.84</td><td>18.67</td><td>3.27</td></tr><tr><td>Accidents</td><td>1.35</td><td>1.62</td><td>13.13</td><td>6.30</td></tr><tr><td>Congestion</td><td>39.30</td><td>6.50</td><td>382.22</td><td>25.29</td></tr><tr><td>Production</td><td>0.38</td><td>0.18</td><td>3.68</td><td>0.71</td></tr><tr><td>Total externalities per mode of transport</td><td></td><td></td><td>440.61</td><td>40.95</td></tr><tr><td>Total externalities—Reference scenario</td><td>481.56</td><td></td><td></td><td></td></tr></tbody></table>

the mobility in Geneva. These values are also used to estimate the new transport performance for the scenarios. The estimated transport performance and modal split is in Table 14.5.

Using annual transport performance from Table 14.5 and the marginal costs for buses and cars for Switzerland from Table 14.6, we estimate the total external costs for road passenger transport for Geneva in 2015.

The total external costs in 2015 are important because they represent the reference point that is used to compare the impacts of the AM introduction and calculate the potential increase or decrease in external costs (before and after the deployment of the AM).

### 14.5.2.2 Reference Scenario (Sc02): Second Couronne

This area has an overall population of 26,102 inhabitants and an area of 162 km2 (Federal Statistical Office, 2013; Service de la mensuration officielle, 2005). Based on Montfort et al. (2019), we estimate around 94,000 daily trips for all the
<figure><img src="image_152.png" /><figcaption>**Table 14.7** The mobility behaviour and transport performance for the second suburban ring in Geneva, 2015</figcaption></figure>

<table><thead><tr><th>Mode of transport</th><th></th><th>Total transport performance in million pkm</th><th>Transport performance (pkm)</th><th>Average daily distance in km</th><th>Modal share</th></tr></thead><tbody><tr><td>Private mode</td><td>Private cars</td><td>240.1</td><td>9198.00</td><td>25.2</td><td>43.9%</td></tr><tr><td rowspan="2">Public mode</td><td>Bus</td><td>39.1</td><td>1496.50</td><td>4.1</td><td>9.504%</td></tr><tr><td>Tram metro</td><td>19.1</td><td>730.00</td><td>2</td><td>4.896%</td></tr><tr><td rowspan="2">Active mobility</td><td>Biking</td><td>7.6</td><td>292.00</td><td>0.8</td><td>4.5%</td></tr><tr><td>Walking</td><td>21.0</td><td>803.00</td><td>2.2</td><td>31.5%</td></tr><tr><td></td><td>Other</td><td>20.0</td><td>766.50</td><td>2.1</td><td>5.7%</td></tr></tbody></table>

inhabitants in the second couronne consisting of 11 municipalities. The modal split is in Fig. 14.5.

The mobility behaviour and the transportation performance in Table 14.7 present the values used to estimate the new pkm per mode of transportation used in the scenarios.

In this scenario, to better reflect the reduced public transportation offer, we adjust the occupancy factors. The bus has an average ten passengers on board. The estimation is based on the higher dependency on cars and a lower modal share of public transport, in general, in suburban areas compared to city centres (high-density population areas): The car modal share is 44% compared to 22.3% in the city centre. And the buses' modal share is 10%, while it is 15% in the city of Geneva. We assume that the AM are circulating on interurban other roads. The marginal costs are adjusted accordingly and included in Table 14.8, as well as the total external costs (Total external costs (2015), $Y = \sum_i \sum_j x_{ij}$, (2015)).
<table><caption>**Table 14.8** Marginal and total costs in €-cent per pkm in second suburban ring in Geneva</caption><thead><tr><th>Externality category</th><th>Car (marginal)</th><th>Bus (marginal)</th><th>Car (total)</th><th>Bus (total)</th></tr></thead><tbody><tr><td>Air pollution</td><td>0.63</td><td>1.43</td><td>1.50</td><td>0.56</td></tr><tr><td>Climate change</td><td>1.31</td><td>0.83</td><td>3.14</td><td>0.33</td></tr><tr><td>Wtt</td><td>0.42</td><td>0.36</td><td>1.01</td><td>0.14</td></tr><tr><td>Noise</td><td>1.92</td><td>1.60</td><td>4.61</td><td>0.62</td></tr><tr><td>Accidents</td><td>1.35</td><td>3.08</td><td>3.24</td><td>1.20</td></tr><tr><td>Congestion</td><td>25.50</td><td>7.98</td><td>61.22</td><td>3.12</td></tr><tr><td>Production</td><td></td><td></td><td>0.91</td><td>0.14</td></tr><tr><td>Total externalities per mode of transport</td><td></td><td></td><td>75.64</td><td>6.11</td></tr><tr><td>Total externalities</td><td>81.74</td><td></td><td></td><td></td></tr></tbody></table>

These values are used to estimate the increase or decrease in external costs in the suburban scenarios: “expand the network” and “targeted expansion of the network”.

In the following part, the scenarios in the city centre are analysed. Afterwards, those in suburban parts are presented. The assessment relies on data from the marginal costs in Table 14.4, Table 14.6, and Table 14.8, scenario description and the reference scenarios (see Fig. 14.4) by applying the steps from the externalities model.

### 14.5.3 Scenarios in City Centre

#### 14.5.3.1 Replace All Buses (Sc1)

Following, the scenario “replace all buses” is tested in the city of Geneva. First, the marginal costs for the vehicles are presented in Table 14.9 based on Table 14.6 and the marginal costs for the AM. Second, the AM are deployed to replace the 12.3% bus trips share. The estimated new modal share is shown in Fig. 14.5.

Similarly, we assume that the AM conduct the trips of the buses with the same average distance of the bus trips from the reference scenario. This led to a prediction of the transport performance and, consequently, the total externalities.

Scenario (Sc1) leads to an increase in external costs compared to (Sc01), mostly due to congestion. The increase in externalities equals around +12 million euros; see Fig. 14.6. If the congestion is omitted, the scenario shows a decrease in externalities of -11 million euros. The wtt costs more in terms of external costs, but all the other environmental impacts are considered positive if the buses are replaced with AM (see Fig. 14.6). However, this is still offset by almost +23 million lost due to congestion. The congestion is also explained by the occupancy of an AM being
<figure><img src="image_153.png" /><figcaption>**Fig. 14.6** The increases or decreases in externalities with congestion in million euros in Sc1</figcaption></figure>

lower than that of a bus, as well as the difference in the sizes of the vehicles (both factors interfere in the estimation of the marginal cost of congestion). In this scenario, there is no estimation for parking space saved since it focuses on bus replacement (Fig. 14.7).

### 14.5.3.2 Replace all Cars (Sc2)

The marginal costs are those in Table 14.9 (from Sc1). We replace the car modal share in the city of Geneva (i.e. 22.6%) with the traffic circumstances of 2015 (Fig. 14.8).

Replacing all cars with AM leads to a reduction in all external cost categories except the wtt costs, where there is an increase of +3.4 million euros. The wtt emissions increase could be justified by reliance on electricity generated from fossil fuels (Kasten et al., 2016). In general, this scenario could save up to 308 million
<figure><img src="image_154.png" /><figcaption>**Fig. 14.7** The increases or decreases in externalities without congestion in million euros in Sc1</figcaption></figure>

<table><caption>**Table 14.9** Marginal costs (car, bus, and AM) in €-cent per pkm in Geneva city</caption><thead><tr><th>Externality category</th><th>Car</th><th>Bus</th><th>AM</th></tr></thead><tbody><tr><td>Air pollution</td><td>0.63</td><td>0.76</td><td>0.05</td></tr><tr><td>Climate change</td><td>1.31</td><td>0.44</td><td>—</td></tr><tr><td>Wtt</td><td>0.42</td><td>0.19</td><td>0.54</td></tr><tr><td>Noise</td><td>1.92</td><td>0.84</td><td>0.30</td></tr><tr><td>Accidents</td><td>1.35</td><td>1.62</td><td>—</td></tr><tr><td>Congestion</td><td>39.30</td><td>6.50</td><td>13.10</td></tr></tbody></table>

euros, with the majority coming from the congestion savings (–210 million euros); see Figs. 14.9 and 14.10.

The environmental impact of replacing all cars with the AM is reflected in gains of around 40 million euros. The findings are aligned with most studies that support a shift from individual motorised mobility toward electric and shared transportation. Using the fleet calculator, the estimated fleet size needed to replace all car trips in the city of Geneva with AM is around 1380 minibuses for a waiting time superior to
Modal split for Sc2

<figure><img src="image_155.png" /><figcaption>**Fig. 14.8** Modal shift for scenario Sc2: replacing all cars in Geneva</figcaption></figure>

<figure><img src="image_156.png" /><figcaption>**Fig. 14.9** The increases or decreases in externalities with congestion in million euros in Sc2</figcaption></figure>
<figure><img src="image_157.png" /><figcaption>**Fig. 14.10** The increases or decreases in externalities without congestion in million euros in Sc2</figcaption></figure>

4 min. Finally, the savings in parking space is 0.65 km2, the equivalent of 64,824 parking spots.

### 14.5.3.3 Scenario 3: Robotaxis (Sc3)

This scenario focuses on AV's integration in the transportation network in the form of robotaxis. The marginal costs for the robotaxis depend on van Essen et al. (2019) estimations for electric vehicles (EV). In this scenario, we estimate an occupancy factor for the robotaxi of 1.2. The marginal costs in the Delft assessment for the electric vehicle use an estimation of an occupancy factor of 1.6 (as well as for an ICEV, Table 14.2). Thus, the marginal costs for the different external costs categories are adjusted for an occupancy factor of 1.2 (new marginal cost for robotaxi = marginal cost for EV x 1.6/1.2).

The robotaxis' marginal costs are presented in Table 14.10. Furthermore, to better translate the effect of the low occupancy factors, we assume that the robotaxis are operating on congested roads.

The robotaxis modal share is explained in Table 14.11, the new modal split of the scenario in Fig. 14.11. 20% of car modal share (~22%) means the AM will absorb around 5% of car trips and, similarly, a 6% reduction in bus share means around 1% of trips replaced by AM. Finally, for the walking trips, we assume the new modal share of walking is 37% (~48%–13% = 37%).

The deployment of robotaxis in the city centre with a laissez-faire outcome causes a significant increase in external costs compared to Sc01. In fact, it is estimated to cost around +162 million euros in terms of environmental and societal
<table><caption>**Table 14.10** Marginal costs for scenario robotaxis in €-cent per pkm</caption><thead><tr><th>Externality category</th><th>Car</th><th>Bus</th><th>Robotaxis</th></tr></thead><tbody><tr><td>Air pollution</td><td>0.63</td><td>0.76</td><td>0.07</td></tr><tr><td>Climate change</td><td>1.31</td><td>0.44</td><td>—</td></tr><tr><td>Wtt</td><td>0.42</td><td>0.19</td><td>1.11</td></tr><tr><td>Noise</td><td>1.92</td><td>0.84</td><td>1.92</td></tr><tr><td>Accidents</td><td>1.35</td><td>1.62</td><td>—</td></tr><tr><td>Congestion</td><td>48.50</td><td>8.00</td><td>64.67</td></tr></tbody></table>

<table><caption>**Table 14.11** Robotaxis modal share composition</caption><tr><td rowspan="3">Robotaxis modal share 18.2%</td><td>4.78</td><td>% (reduced from cars)</td><td>Ward et al. (2019); Zhong et al. (2020)</td></tr><tr><td>13.0</td><td>% (reduced from walking)</td><td>Fournier et al. (2020)</td></tr><tr><td>0.79</td><td>% (reduced from buses)</td><td>Mishra et al. (2015)</td></tr></table>

Modal split for Sc3

<figure><img src="image_158.png" /><figcaption>**Fig. 14.11** Modal shift for scenario Sc3: robotaxis in Geneva</figcaption></figure>

impacts (see Fig. 14.12). Figure 14.13 is used to gauge the environmental impacts. The scenario also leads to decreased gains in the environmental and accidents categories except for the noise and wtt categories. The overall difference in external costs compared to Sc01 without the congestion is around one million euros.

Accounting for the increased travel demand, the fleet calculator by Fournier et al. (2020) shows a fleet of 1058 robotaxis for an on-demand service with a waiting time of 1 min. The 1058 robotaxis could cover 18.2% of all daily trips within
<figure><img src="image_159.png" /><figcaption>**Fig. 14.12** The increases or decreases in externalities with congestion in million euros in Sc3</figcaption></figure>
<figure><img src="image_160.png" /><figcaption>**Fig. 14.13** The increases or decreases in externalities without congestion in million euros in Sc3</figcaption></figure>

a geofenced area of 15.93 km2. We follow the simulation results from (Bischoff & Maciejewski, 2016) since the simulation has similar conditions to Sc3; thus, one robotaxi replaces ten cars. Hence the new car fleet is 54,624 cars compared to the original 64,824 cars (Bischoff & Maciejewski, 2016). The 10,200 parking spots saved result in an additional 0.1 km2 of available space. We do not account for parking space for robotaxis based on numerous studies that assume the robotaxis are to be stored off roads in special facilities (AV parks) to be maintained and charged or would be circulating all day (Hayes, 2011; ITF, 2015; Nourinejad et al., 2018; Zhang & Guhathakurta, 2017). Moreover, these garages are not part of the public domain since they belong to the private stakeholders (the robotaxis are operated by private stakeholders; see 5.2.2, scenario 3 robotaxis); hence, they would not affect public infrastructure.

Finally, we test the results for when the robotaxis are a part of a pooling service. Based on Alazzawi et al. (2018), we account for an occupancy factor of 2.4 instead of 1.2. The subscenario with ridesharing registers an increase in externalities that accounts for around 76 million euros. Alternatively, if we consider Mosquet et al. (2015) estimation for an average of four people per vehicle, the increase is around 41 million euros. See Table 14.12 below.

This shows a correlation between the occupancy factors and the externalities. Hence, ridesharing could contribute to a better environmental and societal impact when deploying robotaxis.
### 14.5.3.4 Scenario 6: AM in MaaS (Sc6)

Table 14.9 includes the marginal costs used to calculate the externalities of integrating the AM in MaaS in the city centre of Geneva.

The modal split of Sc6 is explained in Table 14.3. The willingness to give up the cars for daily car users in the city centre of Geneva is used to estimate the modal share of cars that is absorbed by the AM. It is important to note that the sample for daily car users in the city of Geneva was limited, with only 34 respondents.

After conducting a social assessment, it was found that the level of willingness to use AM in Geneva was similar to that of Lyon. As a result, the sample used for

<table><caption>**Table 14.12** Scenario 3 robotaxis applied with and without ridesharing service in Geneva increase in externalities</caption><thead><tr><th>Service</th><th>Individual</th><th colspan="2">Ridesharing</th></tr></thead><tbody><tr><td>Occupancy factor (average person per vehicle)</td><td>1.2</td><td>2.4</td><td>4</td></tr><tr><td>Increase in externalities in million euros</td><td>163.2</td><td>75.8</td><td>41.8</td></tr></tbody></table>

<table><caption>**Table 14.13** AM in MaaS modal share composition</caption><tr><td rowspan="3">The AM modal share = 11.12%</td><td>6.42</td><td>% of cars</td></tr><tr><td>3.00</td><td>% of walking</td></tr><tr><td>1.70</td><td>% of biking</td></tr></table>

#### Modal split in Sc6

<figure><img src="image_161.png" /><figcaption>**Fig. 14.14** Modal shift for scenario Sc6: AM in MaaS in Geneva</figcaption></figure>
<figure><img src="image_162.png" /><figcaption>**Fig. 14.15** The increases or decreases in externalities with congestion in million euros in Sc6</figcaption></figure>

the study included 81 individuals who use their cars daily and live in the city centre of both Lyon and Geneva combined.

From this sample, 23 respondents said they were very willing to give up their cars if the AM bridges first and last miles. Eventually, we consider that 28% of car trips could be replaced by the AM in MaaS, the equivalent of 6.42% of all trips. The AM is also replacing 3% from walking and 1.7% from biking trips, based on Table 14.3.

Table 14.13 and Fig. 14.14 show the composition of the modal share of the AM in this scenario.

The new modal split is shown in Fig. 14.14.

Following the same methodology to estimate the externalities, the scenario is predicted to lead to reductions in external costs that amount to -83.3 million euros compared to Sc01 (AM in MaaS will reduce the external costs from the reference scenario by 83.3 million euros). The reductions without congestion are around -13 million euros. Similarly to previous scenarios, the wtt impact when introducing the AM in densely populated areas is negative (leading to an increase in the wtt external costs). This is explained mostly by the effect of electricity production on air pollution. Congestion presents the bulk of the reductions, around -70 million euros. We estimate around -four million euros in savings for the categories of climate change, accidents and noise. See Fig. 14.15 for the results with congestion and Fig. 14.16 for results without congestion.
<figure><img src="image_163.png" /><figcaption>**Fig. 14.16** The increases or decreases in externalities without congestion in million euros in Sc6</figcaption></figure>

This contributes to savings in parking spaces of 0.04 km2 or around 4160 parking spots. Finally, we consider the results if the AM is deployed in suburban areas. This could be interesting to see the difference in applying the same scenario in different settings (urban and suburban), and it highlights the importance of targeted and context-based deployment strategy. The analysis for suburban scenarios is applied in this case.

Based on the social survey, we can anticipate a replacement rate of cars by AM of 12.3%. The AM will also absorb 3% of walking and 1.7% of biking, similar to the analysis of the urban version of the scenario described previously. The decrease in externalities is estimated to be around 12 million euros. Without accounting for the congestion, we have a decrease of two million euros. See the comparative Table 14.14 below.

### 14.5.4 Suburban Scenarios

For our two suburban scenarios, we use the mobility behaviour of the second suburban ring in Geneva (Sc02).
<table><caption>**Table 14.14** Scenario 6 AM in MaaS applied in urban and suburban Geneva</caption><thead><tr><th rowspan="2">Decrease in externalities in<br>million euros</th><th colspan="2">Setting for AM in MaaS</th></tr><tr><th>urban</th><th>Suburban</th></tr></thead><tbody><tr><td>With congestion</td><td>83.38</td><td>11.95</td></tr><tr><td>Without congestion</td><td>12.92</td><td>1,85</td></tr></tbody></table>

<table><caption>**Table 14.15** Marginal costs (car, bus and AM) in €-cent per pkm in second suburban ring of Geneva</caption><thead><tr><th>Externality category</th><th>Car</th><th>Bus</th><th>AM</th></tr></thead><tbody><tr><td>Air pollution</td><td>0.63</td><td>1.43</td><td>0.04</td></tr><tr><td>Climate change</td><td>1.31</td><td>0.83</td><td>—</td></tr><tr><td>Wtt</td><td>0.42</td><td>0.36</td><td>0.41</td></tr><tr><td>Noise</td><td>1.92</td><td>1.60</td><td>0.23</td></tr><tr><td>Accidents</td><td>1.35</td><td>3.08</td><td>—</td></tr><tr><td>Congestion</td><td>25.50</td><td>7.98</td><td>6.38</td></tr></tbody></table>

Table 14.8 is used to calculate the total externalities in the scenarios. The AM marginal costs are also adjusted to take into account that the AM will compensate for the shortage of public transportation in these areas. The occupancy factor is 8, and the marginal costs estimated in D8.4 have been adjusted accordingly in the Table 14.15 below.

### 14.5.4.1 Scenario 4: Expand the Network (Sc4)

To expand the network in suburban areas, the AM are deployed to replace a percentage of cars' modal share. Thus, we rely on the explanation from Table 14.3 as well as why we used the sample of Lyon and Geneva from scenario 6.

Finally, the modal share of the AM equals 11.2%, absorbed from the cars' modal share as presented in Fig. 14.17.

This modal shift leads to a decrease in externalities. We account for around -13 million euros. Consistent with the previous scenarios, congestion accounts for the highest impact, around -12.3 million euros; see Fig. 14.18. We register a slight reduction in the WTT category in this scenario. In general, there are reductions in all externalities categories. Without congestion, the reductions are around -three million euros; see Fig. 14.19.

The scenario presents reductions in external costs of -18 million euros for the city. If the congestion is not accounted for, the targeted expansion still leads to reductions of around -five million euros.

The fleet size for AM is estimated as 220, with a waiting time of 6 min and exit and entry time of 3 min (Fournier et al., 2020).

In conclusion, the savings in parking space equals 0.01 km2, around 1367 parking spots.
Modal split in Sc4

<figure><img src="image_164.png" /><figcaption>Fig. 14.17 Modal shift of Sc4: expand the network compared to Sc02</figcaption></figure>

<figure><img src="image_165.png" /><figcaption>Fig. 14.18 The increases or decreases in externalities with congestion in million euros in Sc4</figcaption></figure>
<figure><img src="image_166.png" /><figcaption>**Fig. 14.19** The increases or decreases in externalities without congestion in million euros in Sc4</figcaption></figure>

### 14.5.4.2 Scenario 5: Targeted Expansion (Sc5)

The targeted expansion scenario builds on the previous scenario. The same modal shift from cars to AM remains around 11.2%. However, in this case, we consider that the AM are targeted to also replace low occupancy buses and night buses. The demand for public transport fluctuates during the day and during its route. The notion of welfare optimisation justifies maximising the capacity of the bus (the bigger the bus, the better the ability to meet the peak time travel demands). Thus, public transport operators would justify running big buses (fitting up to 60 passengers). First, it is difficult to predict exactly when a bus is running empty (even during off-peak time, there might be exceptional demand). Second, operating another fleet of smaller vehicles would require more drivers and thus more costs than just keeping the big buses. However, as drivers are no longer necessary in an automated vehicle, these costs are eliminated. Furthermore, the on-demand feature makes the service customisable according to the demand (Pyddoke, 2020).

Determining the specific ratio of buses with low ridership is a complex process since the number varies unpredictably. That is why we consider Adra et al. (2004) study. In their analysis, empty running km for buses is around 11% based on data from Paris public transport system (RATP). Moreover, Mancret-Taylor and Boichon (2015) report stipulates around 4% of bus trips are taken between midnight and
<figure><img src="image_167.png" /><figcaption>**Fig. 14.20** The increases or decreases in externalities with congestion in million euros in Sc5</figcaption></figure>

5 a.m. Hence, we assume that more than 12% of all bus trips in suburban areas are replaced by AM trips. Finally, the modal share of AM in this scenario is 13.4%. Sc5 also registers reductions in external costs of around -15 million euros and -4 million euros without counting the external costs from congestions. The parking savings are similar to the previous scenario, “expand the network”; we estimate savings of 0.1 km2 (Figs. 14.20 and 14.21).

## 14.6 Discussion

To better demonstrate the scenarios and compare the results, Table 14.16 includes increases or decreases in external costs per scenario for each of the six scenarios analysed.

The two scenarios that incurred increases in external costs were “robotaxis” and “replacing all buses”. The first is set to compete with public transport, while the second is set to update the bus service. However, both demonstrate that replacing traditional public transportation without a planned strategy (or with a laissez-faire
<figure><img src="image_168.png" /><figcaption>**Fig. 14.21** The increases or decreases in externalities without congestion in million euros in Sc5</figcaption></figure>

outcome) would worsen the impact of deploying AV in cities and diminish their potential benefits on the environment and society.

Replacing the bus scenario results matches the simulation of replacing buses with shared AV in Helsinki (ITF, 2017). Thus, it is not recommended to replace all the buses with AM but rather target the off-peak hours of bus trips like in scenario 5 “targeted expansion”, where the bus service is mixed with AM on-demand. The path to update the bus service would be better by replacing the fleet with electric buses.

The implementation of robotaxis exacerbates the shortcomings of the transportation system, as it perpetuates a model of individual mobility that has proven to be detrimental to the city's environment.

Replacing all cars offers the greatest potential for savings, which aligns with many urban initiatives aimed at reducing car usage. This approach is in line with Geneva's efforts to promote sustainable transportation, such as soft modes and public transit. Although replacing all cars clearly results in cost savings, it may be more difficult to implement in suburban areas, limiting its overall impact and potential (Duarte & Ratti, 2018; ITF, 2015; ITF, 2017). Moreover, it is more challenging to realise as it depends strongly on people's acceptance of the technology and their willingness to get rid of their cars.
<table><caption>**Table 14.16** The increases (+) and decreases (−) in external costs in the six scenarios of AM deployment in the Canton de Geneve</caption><thead><tr><th></th><th>Scenario</th><th>Setting</th><th>The modal share of AV</th><th>The scenario modal shift</th><th>Sources used for modal shift</th><th>Decrease (−) or increase (+) in external costs in million euros</th><th>Savings in Parking space in km2</th><th>Equivalent number of parking spaces</th></tr></thead><tbody><tr><td>1</td><td>Replacing all buses</td><td>Urban</td><td>12%</td><td>− Replace all bus trips in city Centre = &gt; 12% of all trips</td><td></td><td>+12.11</td><td>−</td><td>−</td></tr><tr><td>2</td><td>Replace all cars</td><td>Urban</td><td>23%</td><td>− Replace all car trips in city Centre = &gt; 23% of all trips</td><td></td><td>−307.95</td><td>0.65</td><td>64,824</td></tr><tr><td>3</td><td>Robotaxis</td><td>Urban</td><td>18%</td><td>− Replace 7% of bus trips = &gt; 1% of all trips<br>− Replace 20% of car trips = &gt; 4.5% of all trips<br>− Replace 13% of all trips (from walking modal share)</td><td>Ward et al. (2019), Clewlow & Gouri, (2017), Heineke et al. (2019)</td><td>+ 161.8</td><td>0.16</td><td>15,800</td></tr><tr><td>4</td><td>Expand network</td><td>Suburban</td><td>12%</td><td>− Replace the car trips from daily car users who would give up their car −26% of car trips = &gt; 11.5% of all trips</td><td>− Representative survey</td><td>−12.94</td><td>0.01</td><td>1367</td></tr><tr><td>5</td><td>Targeted expansion</td><td>Suburban</td><td>13%</td><td>− Same for car trips from Sc4 = &gt; 11.5% from all trips<br>− Replace all trips on night buses and empty-running buses = 12% from all bus trips = &gt; 1.2% from all trips</td><td>− Representative survey<br>Mancret-Taylor and Boichon (2015), Adra et al. (2004)</td><td>−14.86</td><td>0.01</td><td>1367</td></tr><tr><td>6</td><td>AM in MaaS</td><td>Urban</td><td>11%</td><td>− Same for car trips from Sc4<br>− Replace car last/first mile trips to connect to a rail station = &gt; 6.5% of all trips<br>− Replace walking last/first mile trips to connect to a rail station = &gt; 3% of all trips<br>− Replace biking last/first mile trips to connect to a rail station = &gt; 1.7% of all trips</td><td>− representative survey<br>Paydar et al. (2020), Giansoldati et al. (2020), Gebhardt et al. (2016)</td><td>−83.38</td><td>0.04</td><td>4160</td></tr></tbody></table>
On a larger scale, MaaS could reduce environmental deterioration and increase the efficiency of the shuttles even if it absorbs some active mobility means. It is the most realistic and could provide better results. It could be part of an on-demand and door-to-door service across the canton of Geneva to reduce the long-distance trips conducted with cars and improve connectivity to mobility hubs.

Moreover, the suburban scenarios show optimistic results, which justifies the need to strengthen the transportation network where there are shortcomings of public transport. The gains might be limited compared to other scenarios gains (AM in MaaS or replacing all cars). Nevertheless, these deployment strategies would strengthen the overall transportation offer across the canton and encourage passengers to use trains more. This would lead to further gains in suburban and urban areas alike. Concurrently, high-density population areas usually contain more short-distance trips in smaller areas, as opposed to suburban and interurban areas. In this case study, the city of Geneva (one commune with a population density of 12,500 inhabitants/km2) accounts for 711,000 daily trips, while the second suburban couironne (11 communes with a population density of 161 inhabitants/km2) accounts for 94,000 daily trips. Hence, this makes the comparison between the two sets of scenarios biased. In order to maximise the savings, the AM could cover longer distances and connects communes (rather than operate exclusively within the commune).

If we focus on the externalities categories, we notice that the congestion presents the factor that leads to the biggest reduction in external costs (Sc 2, 4, 5 and 6) or the biggest increases (Sc 1 and Sc 3). It reflects the transport pricing and value of time. Replacing buses that usually operate within specific lanes with AM would potentially slow down the traffic flow, whereas deploying more individual vehicles like robotaxis would affect the traffic congestion.

This is incremental for policymakers as it showcases the perils of traffic jams as it worsens the traffic flow, which affects daily life and air pollution and GHG emissions. Reducing congestion is a leading cause of externalities gains in our scenarios. Dominating congestion externalities are aligned with Jochem et al. (2016) and van Essen et al.'s (2019) results for road traffic congestion.

The categories of accidents, air pollution, production and climate change show savings across the six scenarios. Hence, any introduction of AV, whether AM or robotaxis, will have positive impacts on accident rates and air pollution GHG emissions. The air pollution and GHG emission externalities during the wtt phase, on the other hand, show negative results for all urban scenarios. This is explained by the fact that the production of electricity for battery charging is strongly energy-intensive, and it involves air emissions, thus causing a not negligible environmental burden, as proved by Pero et al. (2018). Furthermore, the Robotaxis scenario is the only case where it causes a negative noise externality which is understandable since the marginal cost is the same as that of an ICEV. The decision not to distinguish between an EV and ICEV is due to the similarity in speed and the occupancy factors for both vehicles (Jochem et al., 2016).

Also, the introduction of AV in the city will, in general, lead to savings in parking spaces. The more cars are replaced, the more urban area is free. Free urban space
means reshaping the built environment to be more green and liveable, which again
aligns with the urban strategy of Geneva (Etat de Genève, 2013).

The direct costs of the deployment on a microscale for the pilots were analysed previously in Antonialli et al. (2021). Building on this analysis, we note that it is important to account for the effects of letting go of the drivers. The AM are operating with safety drivers on board in the AVENUE pilots. However, in the future, they will operate without human interference on board. This used a major advantage for replacing traditional public transport with AM as it will significantly cut the costs for the public transport operators, which would make it easier for them to adopt the technology and make their public transport more attractive and competitive. However, the layoffs mean an increase in the unemployment rate. For instance, Transport Public de Geneve (TPG), the public transport operator in Geneva, hires almost 1300 bus drivers. In Sc1, eliminating all buses might seem disadvantageous for the city in terms of environmental and societal impacts. It could reduce around 50 million euros of labour costs for TPG. However, this would have a more negative impact on the society by decreasing 1300 jobs, even if we account for the creation of new jobs: off-site safety operator positions are limited and would not compensate around 1000 posts, which would create labour market disruption and have a significant effect on the economy as well (Nikitas et al., 2021; Sousa et al., 2018).

To conclude, the externalities calculations supported most of the initial assumptions for the consequences of the deployment in the scenarios. It also helps clarify the ambiguity of the direct environmental effects of the AM. The analysis provides insights that would help policymakers decide on how to deploy the AM (or robotaxis) to support the prosperity of their cities.

## 14.7 The Model Applied to Other Cities

In this part, we apply selected scenarios to the AVENUE cities. First, we use the representative survey to filter which scenarios are interesting to be studied based on the sample of respondents that are using their cars daily in the geographic areas of the city centre, large towns and small to medium villages. For Luxembourg, the sample of respondents that are daily car users and live in the city was too small. Thus, we select the scenarios in the suburban and preurban parts. For Lyon, we follow the same analysis as for Geneva; we combine the samples for Lyon and Geneva. Finally, for Copenhagen, we focus on the urban scenarios (replacing all cars, replacing all buses, robotaxis and AM in MaaS) since the sample of respondents is significantly larger than those from small to medium villages.
## 14.7.1 Copenhagen

For this study, we focus on the municipality of Copenhagen for the four urban scenarios. The data used is based on the Danish national census for 2019 (Christiansen & Baescu, 2021). The modal share for the urban scenarios is from 2015 based on Kayser (2017). This represents the most recent data we could find that represents the mobility behaviour of the municipality of Copenhagen. The modal share is 26% private cars, 5% bus, 10% train, 36% biking, 22% walking and 1% other (including motorcycles). This is combined with the average distances per the mode of transport from the greater area of Copenhagen based on Christiansen and Baescu (2021).

Copenhagen municipality has around 623,000 residents and an area of 86.4 km2 (Statistics Denmark, 2021). The average daily trips per person are around 2.8. We account for a fleet of 252,600 cars and around 1000 buses (Movia, 2019).

The scenarios applied in this case are: “replacing all cars”, “replacing all buses”, “robotaxis”, and “AM in MaaS”. By using the same analysis and methods described for Geneva, we obtain the potential increase or decrease in external costs as presented in Table 14.17.

The robotaxis scenario remains the one with the highest potential increase in external costs compared to the status quo, while “replacing all cars” is the one with the highest reductions in external costs.

## 14.7.2 Luxembourg

The scenario that we consider for Luxembourg is the communes of the Luxembourg canton without the city of Luxembourg. The area is 187 km2, and the population is 58,079 inhabitants (Ville de Luxembourg, 2021). The mobility behaviour data was limited. Therefore, we used averages for modal share from Luxembourg and average distances on EU level.

<table><caption>**Table 14.17** The potential increases or decreases in external costs in Copenhagen for Sc1, Sc2, Sc3 and Sc6</caption><thead><tr><td>Scenario</td><td>The modal share of AV (AM or robotaxis)</td><td>The difference in externalities (million euros)</td><td>The decrease in external costs without congestion</td></tr></thead><tbody><tr><td>Replacing all buses</td><td>5%</td><td>+(8.28)</td><td>-1.33</td></tr><tr><td>Replace all cars</td><td>26%</td><td>-964.96</td><td>-94.27</td></tr><tr><td>Robotaxis</td><td>18%</td><td>+(452.07)</td><td>-3.28</td></tr><tr><td>AM in MaaS</td><td>7%</td><td>-86.54</td><td>-8.37</td></tr></tbody></table>
<table><caption>**Table 14.18** The decrease in external costs in Luxembourg for Sc4 and Sc5</caption><thead><tr><td>Scenario</td><td>Setting</td><td>AM modal share</td><td>Decrease in external costs in million euros</td></tr></thead><tbody><tr><td>Targeted expansion</td><td>Suburban</td><td>12%</td><td>-27.11</td></tr></tbody></table>

The externalities results following the estimations in marginal costs for Luxembourg, the transport performance and the predicted modal shift are presented in Table 14.18.

The results show that Luxembourg canton would reduce its external costs if it deploys the AM in the suburban parts.

### 14.7.3 Lyon

We focus on the area of the Greater Lyon (Metropole de Lyon) composed of the areas of Lyon-Villeurbanne (the centre Lyon) as the city centre and the ring of the Lyon Metropole (Couronne de la Metropole de Lyon). Thus, the reference scenarios are applied to these areas: Sc01 is the scenario of Lyon-Villeurbanne, and Sc02 is the scenario of the ring of the Lyon Metropole, both in 2015.

The transport performance and the mobility behaviour data are computed using a mobility survey for 2015, conducted by Lyon (Sytral, 2016).

The City of Lyon is one of the most populated cities in France, with a population of around 500,000 inhabitants and a surface of 47.9 km2. As for the mobility behaviour, using the average number of 3.3 daily trips for each inhabitant, we could estimate a sum of 1.65 million overall trips a day. In the city, the motorisation rate is 414 cars per 1000 residents. Thus, we count 207,000 cars. The modal split is 26% private cars, 9% bus, 16% tram metro, 3% biking, 45% walking and 1% other (including motorcycles)..

For the suburban ring, the population is around 885,927 inhabitants, and the surface is 486 km2.

The average number of daily trips is 3.43 for an average distance of 20 km compared to the 13 km average for the City of Lyon. In this area, there are around 30 million daily trips. The car fleet accounts for 575,853 cars. The modal split is 57% private cars, 5% bus, 9% tram metro, 1% biking, 27% walking and 1% other (including motorcycle).

We apply the scenarios and the externalities model as it was applied for Geneva.

The results are presented in Table 14.19 below.

The results here differ slightly from previous findings. In this case, updating the city's bus fleet by replacing the vehicles with AM would lead to decreases in the external costs.
<table><caption>**Table 14.19** The potential increase or decrease in external costs in million euros Lyon for all six scenarios</caption><thead><tr><th>Scenario</th><th>Setting</th><th>The modal share of AV</th><th>Increase + or decrease—in externalities</th><th>Increase + or decrease—in externalities—without congestion</th></tr></thead><tbody><tr><td>Sc1-replacing all buses</td><td>Urban</td><td>9%</td><td>-6.41</td><td>2.7</td></tr><tr><td>Sc2-replace all cars</td><td>Urban</td><td>26%</td><td>-319.65</td><td>41.86</td></tr><tr><td>Sc3-Robotaxis</td><td>Urban</td><td>19%</td><td>+133.75</td><td>4.98</td></tr><tr><td>Sc4-expand network</td><td>Suburban</td><td>15%</td><td>-170.74</td><td>30.32</td></tr><tr><td>Sc5-targeted expansion</td><td>Suburban</td><td>16%</td><td>-174.21</td><td>32.82</td></tr><tr><td>Sc6-AM in MaaS</td><td>Urban</td><td>12%</td><td>-88.89</td><td>11.6</td></tr></tbody></table>

<table><caption>**Table 14.20** Summary of the results for AVENUE cities, increase (+) or decrease (–) in external costs for each scenario in million euros</caption><thead><tr><th rowspan="2">Scenario</th><th rowspan="2">Setting</th><th colspan="2">Geneva</th><th colspan="2">Lyon</th><th colspan="2">Copenhagen</th><th colspan="2">Luxembourg</th></tr><tr><th>Modal share AV</th><th>Increase or decrease in million euros</th><th>Modal share AV</th><th>Increase or decrease in million euros</th><th>Modal share AV</th><th>Increase or decrease in million euros</th><th>Modal share AV</th><th>Increase or decrease in million euros</th></tr></thead><tbody><tr><td>Replacing all buses</td><td>Urban</td><td>12%</td><td>+12.11</td><td>9%</td><td>+6.41</td><td>5%</td><td>+ (8.28)</td><td>–</td><td>–</td></tr><tr><td>Replace all cars</td><td>Urban</td><td>23%</td><td>-251.94</td><td>26%</td><td>-319.65</td><td>26%</td><td>-964.96</td><td>–</td><td>–</td></tr><tr><td>Robotaxis</td><td>Urban</td><td>18%</td><td>+161.8</td><td>19%</td><td>+133.75</td><td>18%</td><td>+ (452.07)</td><td>–</td><td>–</td></tr><tr><td>Expand network</td><td>Suburban</td><td>12%</td><td>-15.42</td><td>15%</td><td>-170.74</td><td>–</td><td>–</td><td>10%</td><td>-23.89</td></tr><tr><td>Targeted expansion</td><td>Suburban</td><td>13%</td><td>-17.63</td><td>16%</td><td>-174.21</td><td>–</td><td>–</td><td>12%</td><td>-27.11</td></tr><tr><td>AM in MaaS</td><td>Urban</td><td>11%</td><td>-83.38</td><td>12%</td><td>-88.89</td><td>7%</td><td>-86.54</td><td>–</td><td>–</td></tr></tbody></table>

## 14.8 Conclusion

In general, the robotaxis scenario will always lead to increases in external costs when deployed in competition with public transport. A deployment with ridesharing could improve the balance. The environmental and accident categories record a slight decrease in externalities, but the decrease in external costs (without congestion) remains the lowest out of all the urban scenarios. The AM in MaaS, replacing all cars and the suburban scenarios consistently show decreases in external costs. Replacing all cars leads to the highest reduction in externalities (Table 14.20). While
<table><caption>**Table 14.21** Summary of the results for AVENUE without congestion</caption><thead><tr><th rowspan="2">Scenario</th><th rowspan="2">Setting</th><th colspan="2">Geneva</th><th colspan="2">Lyon</th><th colspan="2">Copenhagen</th><th colspan="2">Luxembourg</th></tr><tr><th>Modal share AV</th><th>Decrease million euros</th><th>Modal share AV</th><th>Decrease million euros</th><th>Modal share AV</th><th>Decrease million euros</th><th>Modal share AV</th><th>Decrease million euros</th></tr></thead><tbody><tr><td>Replacing all buses</td><td>Urban</td><td>12%</td><td>11.02</td><td>9%</td><td>2.7</td><td>5%</td><td>1.33</td><td>-</td><td>-</td></tr><tr><td>Replace all cars</td><td>Urban</td><td>23%</td><td>46.77</td><td>26%</td><td>41.86</td><td>26%</td><td>94.27</td><td>-</td><td>-</td></tr><tr><td>Robotaxis</td><td>Urban</td><td>18%</td><td>2.57</td><td>19%</td><td>4.98</td><td>18%</td><td>3.28</td><td>-</td><td>-</td></tr><tr><td>Expand network</td><td>Suburban</td><td>12%</td><td>3.24</td><td>15%</td><td>30.32</td><td>-</td><td>-</td><td>10%</td><td>3.29</td></tr><tr><td>Targeted expansion</td><td>Suburban</td><td>13%</td><td>4.75</td><td>16%</td><td>32.82</td><td>-</td><td>-</td><td>12%</td><td>5.5</td></tr><tr><td>AM in MaaS</td><td>Urban</td><td>11%</td><td>12.92</td><td>12%</td><td>11.66</td><td>7%</td><td>8.37</td><td>-</td><td>-</td></tr></tbody></table>

AM in MaaS is a user-centric approach pull strategy, replacing all cars is a typical push strategy which forbids cars in the city. Thus replacing all cars is more difficult to realise due to low social acceptance. For Copenhagen, the most appealing scenario seems to be to replace all car trips by the AM (see Table 14.21). The number of the city's residents and the high average daily distance for car trips would justify this potential reduction.

Lyon shows that the emphasis should be on strengthening its public transport in suburban parts by deploying the AM serving seamless and intermodal services and also in the city, where the AM could be deployed within MaaS to bridge first and last-mile gaps and enhance connections to the rail stations.

Geneva would benefit from the AM deployment in the urban areas, mostly as seen in scenarios 2 and 6 since it has the highest reduction in externalities. It could reduce car access to the city centre and introduce the AM to support public transport and replace all car trips in these areas. Relying on the users' acceptance, the most realistic approach is to introduce the AM gradually, first as part of a MaaS service. Where AM are filling existing mobility gaps rather than completely replacing individual mobility, as the users' acceptance increases, passengers would switch more and more to the AM instead of relying on their cars. If this introduction is accompanied by urban policies such as road pricing and no-car zones, it will further deter citizens from using their cars in the city centre, which would increase the modal shift to the AM, active mobility and public transport.

Deploying the AM in suburban areas in Luxembourg also shows a reduction in externalities. This strategy fits with the canton's plans to reduce car use and improve connections to train stations to better serve cross-border travellers.

The scenarios with the externalities calculations provide indicators on the recommended deployment strategy that would fit with a European city's sustainable development. In conclusion, AM in MaaS presents a significant reduction in externalities, and it is a scenario supported by social acceptance as shown in the scenario description. This scenario also supports the public transport network, which makes
it more sustainable. In contrast, the robotaxis scenario would increase externalities and compete with public transport. It is more convenient but also would lead to more congestion and pollution compared to the AM. However, the model has some limitations that are described in the following part.

### 14.8.1 Limitations of the Model

It is important to present the limitations of the model and where it falls short. It is a given that the accuracy of the results is dependent on the accuracy of the input data. For instance, Luxembourg and Copenhagen case studies would benefit greatly from updating the mobility behaviour data to better reflect the areas in question (city centre or suburban ring).

This study provides important results on AM as a new force capable of transforming the transportation sector. It foresees scenarios of embedding AM in societal and political contexts and their impacts.

It has the potential to be more robust if the following limitations are addressed:

The external noise costs depend on the population density, traffic status and time of day. However, the data on a country level for these specific contexts is limited. This requires more fine-tuning to reflect the contextual impact.

Similarly, monetising congestion is complicated as it depends on an EU-level study to reach national marginal costs (Jochem et al., 2016). The meta-analysis requires inputs of speed-flow functions, demand curves and value of time (VOT) at a large scale and poses a challenge to downscale to smaller cities.

The use of national-level marginal costs for city-level assessment is a limitation. For instance, air pollution national external costs might underestimate those on an urban level (PM emissions differ between rural and urban areas). This could be addressed using the European values for urban and rural parts in a sensitivity analysis.

We should also consider whether the use of AM will increase the use of traditional public transport and estimate this increase in modal share. Notably, the study of the induced demand requires more analysis since it is difficult to predict the exact numbers for this rebound effect.

An optimised assessment should also account for the potential demographic changes and their effect on VKM (in Geneva, between 2000 and 2015, there was an increase in travel demand by 10% attributed to the increase in the population, for example).

The reliance only on one source to estimate the marginal costs (the Delft handbook of external costs by van Essen et al. (2019) gives one version of the analysis: a sensitivity analysis using different marginal costs would make the model more robust.

A further limitation is accident marginal costs estimation when AV interacts with human driving. The methodology of calculation is still evolving with growing knowledge.
### 14.8.2 Rebound Effects

As mentioned in the limitations, the deployment of the scenarios should account for potential rebound effects. Since this analysis tries to imagine potential future scenarios based on current data and observations, it is limited to what could be extrapolated quantitatively. The study of rebound effects thus is better addressed qualitatively.

Hymel et al. (2010) describe the rebound effect as an increase in vehicle usage due to an unintended effect of raising fuel efficiency and decreasing the cost of car usage through policies and technological advancements. They also mention induced demand as a rebound effect to road transportation where an increase in infrastructure capacity attracts new traffic and causes it to reach its capacity, which was not the intended goal (Hymel et al., 2010). The AM would provide a convenient, affordable and safe option to passengers (Onat et al., 2023). Thus, it could lead to more trips, as it provides more trips to people who were not commuting with vehicles in the first place, such as children and the elderly. In addition, it might cause a secondary modal shift after its implementation. It could reduce active mobility and public transportation shares even further than first predicted (Childress et al., 2015; Fagnant & Kockelman, 2018; Zmud & Sener, 2017). This might lead to a vicious circle of deploying more vehicles to meet the new demand. Then, as an unintended effect, more people shift to using the AM. Thus, the operators will need to deploy even more vehicles to meet the increasing demand. Thus, the AM would aggravate the traffic congestion and increase the environmental footprint. Hence, the rebound effect undermines the gains from reducing the use of individual mobility by causing new external costs because of reducing walking, biking and public transport trips. Ergo, it is crucial that the deployment is accompanied by a regulatory framework to monitor the introduction of the AV in the transportation system and reduce potential rebound effects (Möller et al., 2019).

### 14.8.3 Policy Recommendations

To summarise replacing all cars has the best impact for reducing external costs but due to the expected problems with the citizen acceptance a political problem for implementing would probably raise. AM in MaaS seems thus to be the best solution for the cities as external costs can decrease substantially. At the same time, positive externalities through network effects can further be expected making the transport system more efficient for the TPOs and reliable for the citizen. The combination of AM and MaaS would enable a change of the mobility paradigm enabling sustainable mobility and a reliable and improved transport system. Last but not least, AM in MaaS would consequently enable SUMP without dos and don'ts. Traditional (unpopular) push and pull strategies (see, e.g. (TUMI, 2018)) could so become obsolete: 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 AM 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 higher (Korbee et al., 2022).

**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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<figure><img src="image_169.png" /></figure>
<figure><img src="image_170.png" /></figure>

**Chapter 15**
# Social Impact Assessment: Changing Mobility Behaviour by Understanding Customer Needs and Attitudes

Dorien Duffner-Korbee, Gabriele Naderer, Niklas Liebhauser, and Guy Fournier

**Abstract** This chapter presents results from the AVENUE social impact assessment. The assessment focuses on the social impacts of the deployment of automated minibuses in the four official AVENUE cities: Luxembourg, Copenhagen, Geneva, and Lyon. The following studies have been conducted: (1) a qualitative study on mobility needs, mobility gaps, and expectations on automated minibuses in the pilot site Nordhavn, in Copenhagen, Denmark, as well as in a potential replicator site in Singen, Germany; (2) a quantitative, representative study on mobility behaviour, attitudes on automated minibuses, and social acceptance of automated minibuses in the four AVENUE cities; and (3) a study on user experiences of passengers of the automated minibuses service in Nordhavn, Copenhagen. This social impact assessment revealed that the majority of citizens have not yet adopted a clear position towards automated minibuses (AM) but rather a positive, open-minded (benevolent) attitude. As there are no stable attitudes yet, there is a possibility to influence (nudge) preferences through well-targeted communication campaigns. The willingness to use the automated minibus service is higher when it is offered on-demand, door-to-door, integrated into a public transport system. The automated minibus is considered as an alternative to the use of their private car, with 45% of car drivers '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. In addition, users of the automated minibus are generally satisfied with this experience, which even leads to increasing acceptance and a reduction of perceived risks compared to non-users. An important

<footnote>D. Duffner-Korbee (✉)
Pforzheim University, Pforzheim, Germany</footnote>

<footnote>Fraunhofer ISI, Karlsruhe, Germany
e-mail: dorien.duffner-korbee@isi.fraunhofer.de</footnote>

<footnote>G. Naderer · N. Liebhauser
Pforzheim University, Pforzheim, Germany</footnote>

<footnote>G. Fournier
Pforzheim University, Pforzheim, Germany

CentraleSupélec, LGI, Gif-sur-Yvette, France</footnote>
prerequisite for users to shift from using their private car to an automated minibus service is to increase flexibility of use by providing an on-demand, door-to-door service. If an automated minibus is integrated into the urban public and private transport system, it has the potential to become a real game changer for urban mobility. The integration of automated minibuses therefore represents a real system innovation that takes into account the mobility needs of potential users.

## 15.1 Introduction

The current private car-based mobility paradigm is facing severe challenges, such as meeting climate goals, reducing congestion and air pollution (Banister, 2008; Fournier et al., 2020; Geels et al., 2017; Kuss & Nicholas, 2022; Pribyl et al., 2020). To respond to these challenges, several trends have emerged, including electrification, automation, low-carbon transition, shared transportation, and intermodal transport (Fagnant & Kockelman, 2015; Geels, 2012; Hirschhorn et al., 2019). Innovative mobility modes and systems are developing, such as mobility as a service (MaaS), car-sharing systems, and automated vehicles (Firnkorn & Müller, 2011; Pangbourne et al., 2020). Of particular interest in this chapter is the automated minibus. Automated minibuses are compact, electric-powered, pod-like vehicles deployed in public transport systems (Huber et al., 2022; Nemoto et al., 2021; Nordhoff et al., 2018). This chapter presents the AVENUE social impact assessment, focusing on social acceptance as the capability to attract and retain users.

Social acceptance of an innovation is a prerequisite for its use. Acceptance does not inevitably lead to the adoption (use) of the innovation but rather signifies/rePRESENTS a positive intentional attitude towards the innovation (Kilian-Yasin et al., 2016). Assessing social acceptance is thus crucial for predicting whether, and under which circumstances, an innovation will be accepted and used. One consideration that is often forgotten in studies on social acceptance is the previously unmet needs that impact social acceptance.

Therefore, in this chapter, an approach based on needs and attitudes is applied to assess social acceptance. Attitudes towards automated minibuses are comprised of the subsequent dimensions: perceived risks and benefits, performance expectations, trustworthiness of the technology, effectiveness, accessibility, price, and willingness to use (Banister, 2008; Hirschhorn et al., 2019; Kyriakidis et al., 2015; Nordhoff et al., 2018). Recent studies demonstrate that attitudes on automated minibuses, such as willingness to use, are strongly influenced by previous experiences with the automated minibuses (Zoellick et al., 2019). These prior experiences are also a vital predictor to evaluate behavioural intentions. Hence, to assess the potential of automated minibuses, it is important to incorporate insights on user experiences.
The following research questions are guiding the research:

* What mobility needs does the customer have, and how can these be met by automated minibuses?

* What attitude do customers have towards automated minibuses, and how does that influence acceptance?

* And finally, how does the specific user experience affect the attitude towards and acceptance of automated minibuses?

## 15.2 Research Approach

To answer these research questions, a multi-method study was designed, combining qualitative and quantitative methods. Table 15.1 provides an overview of the studies. The mobility needs assessment (RQ1) consists of 20 in-depth, semi-structured interviews with citizens; 8 citizens live in a pilot site and 12 citizens live in an area with potential automated minibus service.

To evaluate the attitudes towards automated minibuses and mobility in general (RQ2), a quantitative, representative survey among 1.816 potential users of four cities (Copenhagen, Geneva, Luxembourg, and Lyon) that host pilot sites of the AVENUE project was conducted. User experiences (RQ3) were evaluated through a survey among 68 users in the AVENUE pilot site in Copenhagen.

### 15.2.1 Mobility Needs, Mobility Gaps

Two qualitative studies were conducted to identify mobility requirements and their associated deficiencies. One of the studies was conducted in Germany, specifically in Singen, a small village situated between Karlsruhe and Stuttgart, while the other

<table><caption>**Table 15.1** Overview studies conducted for the social impact assessment</caption><thead><tr><th>Objective</th><th>Study</th><th>Target group</th><th>Method</th><th>Sample size</th></tr></thead><tbody><tr><td rowspan="2">Mobility needs assessment (RQ1)</td><td>Mobility needs, mobility gaps and expectations of automated minibuses</td><td>Citizens</td><td>Qualitative interviews</td><td>n = 12</td></tr><tr><td>Mobility behaviour at pilot site level</td><td>Residents of the pilot site</td><td>Qualitative, longitudinal study interviews</td><td>n = 8</td></tr><tr><td>Attitudes towards automated minibuses (RQ2)</td><td>Expectations, attitudes, and acceptance of automated minibuses</td><td>Potential users</td><td>Quantitative, online survey</td><td>n = 1816</td></tr><tr><td>User experiences (RQ3)</td><td>The user experience of actual users</td><td>Users of the automated minibus</td><td>Quantitative, online questionnaires</td><td>n = 68</td></tr></tbody></table>
was conducted in Copenhagen's Nordhavn area. The objective of the study con-
ducted in Singen was to gain an understanding of the current situation, problems,
and needs of the residents regarding their mobility. Furthermore, the study aimed to
analyse expectations and concerns regarding possible future mobility solutions,
such as the automated minibus. The objective of the study conducted in Copenhagen
was to gain insight into the mobility needs, attitudes, and mobility behaviour of citi-
zens living near an AVENUE pilot site. Moreover, our objective was to determine
whether the presence of an automated minibus in their neighbourhood had an impact
on their mobility needs, attitudes, and mobility behaviour.

Participants were selected based on a theory-based sampling method. A qualita-
tive approach does not strive for statistical representativeness but rather psychoso-
cial representativeness. Therefore, theoretically relevant criteria were defined for
the selection of the respondents: age, gender, PRM, place of residence, perceived
mobility gaps, satisfaction with PT, as well as current mobility behaviour. Twelve
citizens were selected from Singen and eight from Copenhagen. Qualitative, semi-
structured interviews were conducted in Singen. In Copenhagen, the data collection
was divided into different steps. First, exploratory interviews were conducted to
gain an understanding of the participants' attitudes towards mobility and their
mobility behaviour. In order to assess a change in attitude, participants were sent six
online surveys over the course of 6 weeks. After the completion of the six question-
naires, a concluding interview was conducted to evaluate the alterations in per-
ceived needs, attitudes, and mobility behaviour.

15.2.2 Attitudes and Social Acceptance

To understand how automated minibuses are perceived and accepted in cities across
Europe, a large-scale, representative online survey was launched in the four
AVENUE cities in 2021. The online questionnaire was fully structured and pro-
grammed in ‘Questback/Tivian’. A total of 1816 citizens participated in the survey
across the four AVENUE demonstrator cities. The survey invitation was dissemi-
nated through a sample procured from a market research institute. The sample was
representative based on gender and age distribution. The sample structure was
diverse, reaching all age groups, female and male potential users, employees, as
well as students and households with or without children (see Table 15.2). For data
analysis, the statistical software SPSS was used.

A three-step approach was followed in developing the questionnaire. First, the
main parameters and indicators were assessed based on theoretical insights and lit-
erature. Second, a repository of questions used in previous questionnaires on auto-
mated driving was created (based on Bernauer & Wicki, 2018; Keolis Downer,
2018; Kilian-Yasin et al., 2016; Schoettle & Sivak, 2014). The questions were
grouped according to our main parameters and indicators. Where necessary, addi-
tional questions were formulated. Third, experts, such as public transport operators
in the cities of focus, provided feedback on the questions.
<table><caption>**Table 15.2** Sample structure representative online survey</caption><thead><tr><th></th><th>Total respondents</th><th>1816</th></tr></thead><tbody><tr><th>City</th><td>Lyon</td><td>501 (28%)</td></tr><tr><th>City</th><td>Copenhagen</td><td>491 (27%)</td></tr><tr><th>City</th><td>Geneva</td><td>284 (16%)</td></tr><tr><th>City</th><td>Luxembourg</td><td>540 (30%)</td></tr><tr><th>Age</th><td>Younger than 36 years</td><td>25%</td></tr><tr><th>Age</th><td>Older than 36 years</td><td>75%</td></tr><tr><th>Gender</th><td>Male</td><td>47%</td></tr><tr><th>Gender</th><td>Female</td><td>53%</td></tr></tbody></table>

For the majority of the questions, we applied a 5-point Likert scale (Brosius et al., 2012). Only the endpoints are named with (1) describing the negative configuration, e.g. ‘fully disagree’ or ‘not important at all’, and (5) describing the positive form, e.g. ‘fully agree’ or ‘very important’. To not overstrain respondents, an additional answer option (6) ‘I can’t judge’ or ‘I don’t know’ was added in specific questions. To avoid cancelling none of the questions was set as mandatory.

### 15.2.3 User Experience

User surveys were conducted to acquire a more in-depth understanding of the users’ interaction with the new service and their perceptions of it. In the user survey, users were asked to reflect on their experience using the automated minibus and to evaluate its usability. The survey consisted of two blocks. A first block of the survey was dedicated to the evaluation of the service. A second block of the survey addressed more general questions about attitudes towards automated minibuses. The second block contained questions that were also part of the large-scale representative survey.

A total of 68 respondents in Copenhagen completed the survey. The survey consisted of two blocks. These participants were asked to conduct the survey, while using the automated minibus. Therefore, the decision to use the service was not influenced by the researchers. The user survey was conducted online, using Questback/Tivian software. Passengers received a flyer from the safety operators with concise information and a link to the online survey. Since the questionnaire could also be completed via mobile devices, passengers had the choice whether they wanted to answer the questionnaire during their ride with the automated minibus or afterwards.

Additionally, respondents were recruited through the aforementioned large-scale representative survey (see Sect. 2.2). In this survey, respondents were asked whether they had previously used an automated minibus. If they had, they were subsequently requested to respond to a set of questions from the user survey. A total of 126 respondents of the large-scale representative survey indicated that they had previously used an automated minibus service and completed the additional user survey questions. In the presentation of the results of the user survey (Sect. 5.1), this group is not mixed with the users of the Nordhavn site. In comparing users to non-users (Sect. 5.2), these respondents are treated as a separate group.
<table><caption>**Table 15.3** Mobility needs</caption><thead><tr><th></th><th>Mobility needs</th><th>Reliefs</th></tr></thead><tbody><tr><td rowspan="10">Mobility needs</td><td rowspan="3">Needs for effectiveness</td><td>Spatial flexibility</td></tr><tr><td>Temporal flexibility</td></tr><tr><td>Reliability</td></tr><tr><td rowspan="2">Needs for cognitive relief</td><td>Ease of use</td></tr><tr><td>Convenience</td></tr><tr><td rowspan="2">Needs for emotional relief</td><td>Safety</td></tr><tr><td>Security</td></tr><tr><td>Needs for physical relief</td><td>Comfort</td></tr><tr><td>Needs for environmental relief</td><td>Impacts on climate change</td></tr><tr><td>Needs for financial relief</td><td>Cost of mobility</td></tr></tbody></table>

## 15.3 Mobility Needs

A crucial element for the successful implementation of innovations is their compatibility with current, actual, and latent needs. The first step in understanding how new mobility systems like automated minibuses can meet these needs is to understand current, actual, latent, and unmet needs in both prospective and actual AVENUE pilot sites. Therefore, the main aim of this needs assessment is to understand what the relevant needs are. In the analysis and in presenting the results, we divide between the mobility needs and the aspects that are required or are in place to fulfil these needs. The latter is termed ‘reliefs’ in Table 15.3.

According to Schmitt and Altstötter-Gleich (2010), attitude is defined as the ‘degree to which a person likes or dislikes an object’ (2010, p. 146). The attitude thus always refers to a specific attitude object. In the context of this study, the attitude object is the ‘integration of automated minibuses’, which is expressed by the perceived suitability of the automated minibus to best meet personal mobility needs.

### 15.3.1 Needs Assessment: Singen and Nordhavn

Singen has a population of approximately 3800 and is approximately 10 square kilometres in size. The village is characterized by a significant difference in altitude. Due to the difference in altitude, the village is divided into two distinct sections, namely ‘Unterdorf’, which is located in the lower part, and ‘Oberdorf’, located in the upper part. The majority of the stores, as well as the train station, are situated on the outskirts of Unterdorf. The village is well connected to major roads, such as the B10. Additionally, there is a bus route that connects Singen to other nearby villages. Cars are the most used transportation mode among our participants, followed by walking and cycling, while trains and buses are used only rarely or not at all (Fig. 15.1).
<figure><img src="image_171.png" /><figcaption>Fig. 15.1 Location of Singen, Remchingen</figcaption></figure>

Nordhavn is a dynamic industrial port that is currently being transformed into Copenhagen's new international waterfront district, accommodating both residential and commercial structures. Upon completion of Nordhavn's development, the area will accommodate more than 40,000 residents and 40,000 employees. The vision is for Nordhavn to become an environmentally friendly neighbourhood and contribute to Copenhagen's image as an environmental metropolis. The introduction of new energy sources and eco-friendly modes of transportation will help establish Nordhavn as a leading example of eco-friendly construction and design. Currently, the Nordhavn area is served by a nearby S-train station and bus stops. However, there are no direct buses or trains operating in the area. This presents a significant opportunity for automated vehicles to serve as a novel public transport (PT) solution and enhance the connectivity of the region beyond its current status. The automated minibus route is located in the Århusgadekvarteret area.

Our participants use all available public transportation options. They consider the metro to be an integral part of their daily mobility, alongside walking and cycling. At the beginning of the study, participants expressed a strong preference for walking. Private cars were less attractive to the study respondents, and in some cases, even a strong aversion against their use was identified (Fig. 15.2).
<figure><img src="image_172.png" /><figcaption>Fig. 15.2 Location of Nordhavn</figcaption></figure>

15.3.2 Needs for Effectiveness

The analysis indicates that effectiveness is the primary need for mobility. The abil-
ity to travel swift and uninterrupted from point A to point B is essential for effective
mobility. In order to achieve effectiveness, both spatial and temporal flexibility are
essential. A combination of spatial and temporal flexibility allows users to access
and use the transport mode whenever and wherever they want, providing users with
a certain degree of freedom.

Respondents in Singen stress the importance of being able to reach and leave the train station at any given moment to catch a scheduled train and then return to the station. This mobility need is not being met at this time. The perception of impracticality is based on factors such as the distance of bus stops and departure times, as well as the frequency of bus stops. Hence, travelling by bus is currently perceived as ineffective and time-consuming.

The bus stop is so far from my house. Until I have walked to the bus stop, I may as well walk right down to the station. (Participant from Singen)

In Copenhagen, participants expressed satisfaction with the spatial flexibility of the transportation system, owing to the tightly knit network of stations that can be quickly accessed from any location. Furthermore, metro and train can bypass traffic congestion on the streets and provide a direct connection between two locations. Nonetheless, the requirement for temporal flexibility freedom is not always fully fulfilled, as trains and metros in Copenhagen operate with reduced frequency in certain areas or at specific times of the day, such as at night. The advantages of
the bus network in Copenhagen are seen in the accessibility of the bus stops. However, some bus lines and stations have recently been eliminated due to the extension of the metro. Therefore, some participants complained about longer travel times or poorer access to the bus stops. Due to the substantial traffic volume in Copenhagen, private cars and buses are not regarded as flexible and effective mobility alternatives.

### 15.3.3 Cognitive Relief

The cognitive needs are those mobility needs that involve stress reduction and psychological well-being, such as being able to travel spontaneously without requiring detailed planning and not having to change the means of transport several times on one trip. Using bicycles provides cognitive relief because no planning is required, but it can also lead to other stressful problems, such as fear that the bike will be stolen. Copenhagen has a relatively flexible public transportation system, which provides a certain degree of cognitive relief. Nonetheless, certain issues remain, such as the positioning of bus stops, as well as the frequency and network of the public transportation system. As for cycling, the well-developed cycling paths provide an alternative to the congested streets, thus offering cognitive relief from the stress of being stuck in traffic with the car. On the contrary, the situation is sometimes referred to as 'cycling chaos', which may also result in stress and diminish the cognitive relief derived from cycling (see also the section on safety).

### 15.3.4 Reliability

The need for reliability involves a need for the mode of transport to arrive punctually and sufficient information supply. To attain a high level of effectiveness, a high level of reliability of the respective means of transport is required.

> I always ask myself, which is the fastest transport? There are two things I'm considering, firstly with which mode of transport I'm reaching my destination faster and also in time regarding the punctuality. So that I can trust it. (Participant from Copenhagen, a man without handicap, age 55)

The bus system in Singen fails to meet the needs for reliability and punctuality due to delays and an inadequate information policy. Therefore, the need for reliability can be better fulfilled through using the car. In Copenhagen, the need for reliability is also considered to be inadequately met by the metro and train. At times, the trains are subject to delays owing to disruptions, maintenance, or train failures. Hence, the reliability of the public transportation system is compromised and fails to provide adequate freedom, flexibility, and cognitive relief, thereby compromising its effectiveness.
### 15.3.5 Needs for Physical Relief

Needs for physical reliefs relate needs that are associated with the physical environment, such as the need for comfort. This need is particularly acute in Singen due to its steep, hilly roads. These areas are easily accessible by car but can be challenging when walking, using a bike, or taking the bus. The need for physical relief is especially acute if larger purchases must be transported.

> ... And then there is again the question of how to get up the hill with the purchase? That's why I use the car again. (Participant from Singen)

In addition to the topography, poor road conditions (few bike lanes, parked cars, detours over railroad tracks) and bad weather are problems for physical relief when using the bicycle. Thus, bicycles provide less physical relief in comparison to a car.

In the Copenhagen study, we identified several elements of physical relief, including the ability to transport purchases, provide protection from adverse weather conditions, and maintain high standards of hygiene. A specific need is expressed by respondents with reduced mobility (PRM) stating a need for physical relief due to the physical burden of their handicap. The reduction in the number of bus lines and stations in Copenhagen has led to a decrease in the accessibility of bus stops, which has resulted in a reduction in physical relief.

### 15.3.6 Needs for Emotional Relief

The need for emotional relief is multifaceted. Among other aspects, it encompasses perceptions of safety and security. In general, all forms of transportation should be safe and secure. In Singen, a special need for safety when returning home from the train station at night was explicitly addressed. The area around the train station, including the underpass, has been called a particularly dangerous area, so using the car is considered a better way to ensure emotional well-being. In Copenhagen, public transport doesn't always meet the need for emotional comfort and security because trains and metros run at a lower frequency in certain areas or at certain times of the day. In general, female participants expressed a sense of discomfort while awaiting trains at night.

> When it is really late in the night I never use busses, to be honest. And also, the trains are only every 20 min, so I do not feel comfortable waiting at the station or even being in the s-train. (Participant from Copenhagen, woman, age 40)

Moreover, not all participants in Copenhagen felt safe when riding the bicycle. Several participants even referred to the current situation in Copenhagen as 'cycling chaos', which results in a sense of unease. Fun, entertainment, or other emotional experiences while travelling were not mentioned as a major need. This need only exists for long-distance trips and is not relevant for shorter trips.
### 15.3.7 Needs for Environmental Relief

Environmental relief and impacts on climate change are a mobility need that is not important to the majority of respondents in Singen. Only participants who already have a strong awareness of environmental protection value this need highly.

To be honest, the environmental aspect was not a reason for me. Or just like everyone tries to be a bit resource-conserving, but that wasn't really a motive for me. (Participant from Singen)

In Copenhagen, two groups of respondents can be distinguished. One group consists of ‘engaged environmental advocates’, who are highly involved in climate change and sustainability. These individuals disapprove of the fact that the majority of buses in Copenhagen utilize fossil fuels. Nonetheless, they express optimism regarding the public transportation system, which is regarded as an environmentally friendly system, wherein resources are utilized effectively. The other group are ‘environmental friends’ who are aware of climate change but for whom sustainable mobility is less relevant. Thus, the current transportation system meets their need for sustainable transportation.

### 15.3.8 Needs for Financial Relief

Price was not a mobility need for the participants in case the price is seen as reasonable for the mode of transportation.

The costs are important when thinking of which mode of transport. But it is more that it should be a justified price, like when I get a high standard of service and good quality, I'm willing to pay more. (Participant from Copenhagen, a man without handicap, age 55)

In Singen, for example, the high bus fares were criticized. The degree of current need fulfilment is—in the view of the citizens—disproportionate to the ticket prices.

### 15.3.9 Wrap Up

The needs identified among the respondents show one thing above all: there is no acute need for ‘automated’ minibuses. Neither the need for being electrically powered in the sense of greater environmental friendliness nor for being automated in the sense that no driver is needed has priority. Other needs are the focus. The main needs identified are needs for a high level of effectiveness, gained by a high level of flexibility and reliability and specific types of relief: cognitive relief, emotional relief, and physical relief. Environmental relief and financial relief are secondary needs.
# 15.4 Attitudes and Acceptance

The following chapter presents the results of the representative, large-scale quantitative study on attitudes and acceptance of automated minibuses by the citizens of the four AVENUE pilot cities.

## 15.4.1 Awareness and Willingness to Use Automated Minibuses

A slight majority of respondents (55%) knew about the existence of automated minibuses before the survey. In 2021, the awareness of automated minibuses was greatest in Luxembourg (72%) and lowest in Copenhagen (40%). The difference in awareness is most likely due to difference in visibility of the pilot sites. The site in Luxembourg was situated in a central area, while the site in Copenhagen was located in a new neighbourhood. There is a wide range of information sources through which participants had gained awareness of the automated minibuses: newspaper (37%), radio/tv (50%), the Internet (33%), social media (15%), seen on the test site (22%), or via friends (13%). Only a small number of respondents (7% overall) have used the automated minibus service before (Table 15.4). That could be due to the small portion of the cities covered by the automated minibus service, and in addition, the Corona pandemic decreased the willingness to use PT in general.

To gain insight into the acceptance of citizens to use the automated minibus services, respondents were asked to think about their willingness to use the automated minibus. The consensus in academic literature (see among others Nordhoff et al., 2020; Hirschhorn et al., 2019; Zoellick et al., 2019) is that there is generally a high level of acceptance and willingness to use automated minibuses. We inquired for willingness to use the automated minibuses in three different scenarios of deployment to obtain a more nuanced picture of the willingness to use automated minibuses. The respondents were presented with information on these three deployment scenarios before the questions; see Box 1.

<table><caption>**Table 15.4** Awareness of automated shuttles</caption><thead><tr><th></th><th>Copenhagen<br>(n = 491)</th><th>Geneva<br>(n = 284)</th><th>Luxembourg<br>(n = 540)</th><th>Lyon<br>(n = 501)</th><th>Overall<br>(n = 1816)</th></tr></thead><tbody><tr><td>Awareness of<br>automated shuttles<sup>a</sup></td><td>197<br>40%</td><td>141<br>50%</td><td>387<br>72%</td><td>278<br>56%</td><td>1003<br>55%</td></tr><tr><td>Experience with<br>automated shuttles</td><td>21<br>4%</td><td>22<br>8%</td><td>40<br>8%</td><td>43<br>8%</td><td>126<br>7%</td></tr></tbody></table>

No statistical dependence between city and experience with automated minibuses

<sup>a</sup>Statistical dependence between city and awareness of automated minibuses, (chi-square (3) = 107.917, p < 0.001)
Box 1 Explanation Provided to the Respondents on the Three Automated Minibus Modes of Deployment

The automated minibuses can be integrated in the public transport system to bridge the distance between your place of departure or destination to a public transport station. This is called a service that bridges the first and last mile.

The automated minibuses can provide a service that is not based on a fixed timetable. The automated minibus comes at a time requested by you, to a location specified by you. This location can be the nearest bus stop, or even your doorstep. This is called an on-demand, door-to-door service.

The automated minibuses service can be integrated in a system together with other means of transportation. This means that you can easily combine a trip with the automated minibus with a trip on the train, bus or taxi. As the automated minibuses will operate on-demand, the system will allow you to plan your trip seamlessly. This is called a seamless, intermodal trip.

The willingness to use is high. Irrespective of the scenario, more than 50% ‘would be willing’ or even be ‘very willing’ to use automated minibuses, even two-thirds (63%) in case an on-demand service is offered (see Table 15.4). The willingness to use the automated minibuses as an on-demand service is high in all four cities, with the highest level observed in Geneva, where a generally higher use of buses is observed. Willingness is lowest in Copenhagen. Here, potential users believe that automated minibuses are best suited for short to medium distances. For these distances, however, bicycles already offer an adequate alternative due to the topography of Copenhagen (Fig. 15.3).

Likewise, the willingness to use the automated minibuses as a service that links the first and last mile is high in all four cities, once again highest in Geneva, where a higher usage of buses is observed, followed by Lyon. By contrast, it is lower in Copenhagen and Luxembourg, but differences are only partially significant (Fig. 15.4).

<figure><img src="image_173.png" /><figcaption>**Fig. 15.3** Willingness to use automated minibus per city if the automated minibus offers an on-demand, door-to-door service (in %, *n* = 1816)</figcaption></figure>
<figure><img src="image_174.png" /><figcaption>**Fig. 15.4** Willingness to use automated minibus per city if the automated minibus offers a service that bridges the first and last mile (in %, *n* = 1816)</figcaption></figure>

<figure><img src="image_175.png" /><figcaption>**Fig. 15.5** Willingness to use automated minibus per city if the automated minibus is part of a seamless, intermodal trip (in %, *n* = 1816)</figcaption></figure>

Similar effects can be seen in the scenario where the service is offered as part of a seamless trip (Fig. 15.5).

In addition to the willingness to use, the respondents were also asked to think about their willingness to reduce their car use. Of interest are those respondents that actually (field time 2021) own a car; therefore, the results exclude respondents who do not own a car. Hence, the presented figures are based on a slightly smaller sample, as only 1526 out of the 1816 respondents own a car. The willingness to reduce the use of one's car is generally high in all scenarios. In the case of an on-demand, even more than 50% would be 'willing to reduce use of own car' (Fig. 15.6).

Similar to the preceding question, respondents were asked to consider their willingness to give up the use of their car completely. Approximately 50% 'would be willing' to give up the use of their own car, in case of an on-demand service. If the automated minibus service would bridge the first and the last mile, 45% of car drivers are 'willing' (22%) or even 'very willing' (23%) to give up using their own car to use automated minibuses (Fig. 15.7).
<figure><img src="image_176.png" /><figcaption>**Fig. 15.6** Willingness to reduce the use of your car if the automated minibus... (in %, n = 1526*).
*Reduced base, as non-car owners are excluded</figcaption></figure>

<figure><img src="image_177.png" /><figcaption>**Fig. 15.7** Willingness to give up the use of your car if the automated minibus... (in %, n = 1526*)
*Reduced base, as non-car owners are excluded</figcaption></figure>

The respondents were also asked to reflect on their willingness to increase the use of public transport if automated minibuses offer an on-demand service. At least 50% 'would be willing' to increase the use of public transport if the automated minibus offers an on-demand service (Fig. 15.8).

The respondents were also asked to think about their willingness to pay for the automated minibus service. The willingness to pay more for an automated minibus, compared to current public transport, is low—see Fig. 15.9. About 6% are very willing, and 30% are willing to pay at least a bit more if the service would be on-demand door-to-door. The automated minibus is not regarded as a truly inspiring innovation (see two-factor model, Herzberg 1968) but rather as a service that removes existing deficits in terms of effectiveness and flexibility (hygiene factor), i.e. it merely reduces the existing dissatisfaction. This could explain why most people are not willing to pay more. The subjective measure might even be that respondents feel that they are paying too much for the current, rather unsatisfactory service.
<figure><img src="image_178.png" /><figcaption>**Fig. 15.8** Willingness to increase the use of public transport systems (including current offers), if the automated minibus... (in %, *n* = 1816)</figcaption></figure>

<figure><img src="image_179.png" /><figcaption>**Fig. 15.9** Willingness to pay to use the automated minibus, if the automated minibus... (in %, *n* = 1273*). *This question was not asked in Luxembourg, as Luxembourg offers free public transport</figcaption></figure>

In addition to the different scenarios, respondents were asked to select a mobility option for a specific journey. The options differ in terms of flexibility and duration, cost, and environmental friendliness, see Fig. 15.10. Two of the options included the automated minibus. The results show that 25% prefer a fast but expensive option, either by private car or robotaxi, 26% prefer a cheap, environmentally friendly option, even if this implies a longer travel time, and 28% choose a service that incorporates the automated minibus. The latter seems to be mostly driven by the travel time. These results are in line with identified factors for selecting prefered mode of transport (see Fig. 15.11). Here, 59% of the respondents indicated that speed and travel are ranked first, second or third most important factor in selecting their preferred mode of transport.
<figure><img src="image_180.png" /><figcaption>Fig. 15.10 Preference for mobility options</figcaption></figure>

## 15.4.2 Advantages and Concerns

The general impression is that the respondents have a positive perception of the advantages of automated minibuses. About two-thirds expect the automated minibuses to ('agree' or 'fully agree') provide more freedom for people with reduced mobility (64%) that they will increase flexibility by providing a greater number of
<figure><img src="image_181.png" /><figcaption>**Fig. 15.11** Importance of factors for selecting the preferred mode of transport, divided by the market segments (in %)</figcaption></figure>

start and stop locations (64%) and by increasing the frequency of PT offers (63%)—
see Fig. 15.12.

Additionally, a positive environmental impact is expected, e.g. reducing the neg-
ative impact on the environment (60%) and congestion (49%). A pleasant and com-
fortable journey (54%) with guaranteed security due to installed video cameras
inside the buses is expected (52%) by every second respondent. More prudent are
the positive expectations concerning the question of whether the automated minibus
will truly lead to fewer accidents by eliminating human errors.

When it comes to perceived concerns, the picture is slightly more nuanced.
Respondents show a high agreement with concerns regarding the operation of the
automated minibus (this includes the interaction with motorized (49%) and nonmo-
torized traffic (46%), its reaction to unforeseen situations (54%), and issues of lia-
bility in the case of an accident (44%). Social concerns are also pertinent to
respondents. 41% of the respondents are in agreement with the statement ‘jobs get
lost’. These concerns are consistent with the results of our social media analysis.
Here, more than 1200 comments were analysed. The comments were associated
with different online textual contents, images, or video clips from around the world
associated with the topic of automated public transit stemming from a variety of
channels ranging from the online press to internet communities and blogposts
through the multiple social networks. The study suggests that ‘social concerns’ is
undoubtedly the most represented of the previously identified categories (concern-
ing 43% of the comments). In particular, the negative consequences for the labour
market (14.9% of the occurrences) seem to be a concern (Fig. 15.13).

Respondents are also worried that the software could be hacked, as 44% agree
that this could happen. 43% would not feel safe in the event of harassment and
assault, and 41% also express concern that the technology may not yet be fit to
drive. These concerns also match the results of our social media study. Following
social concerns, ‘security concerns’ (18% of the comments) are the most important
concerns for people. Still, there seems to be a certain level of trust in ‘the system’.
Only a small share of the respondents are worried that the system may not be secure
Automated minibuses will...

<figure><img src="image_182.png" /><figcaption>**Fig. 15.12** Perceived advantages of automated minibuses (in %, n = 1816)</figcaption></figure>

(29%) or is not reliable (24%). Since many respondents still believe that it is unclear how the automated minibuses will react in critical situations, the presence of a safety operator is still important for the majority of participants.

Generally, criticisms and concerns towards the automated public transit system remain high, especially worries about safety, security, and society. These findings can be seen both in the large-scale, representative online survey and in our social
The idea that automated minibuses will be introduced
everywhere worries me, because...

<figure><img src="image_183.png" /><figcaption>**Fig. 15.13** Perceived concerns over automated minibuses (in %, n = 1816)</figcaption></figure>

media analysis. Nonetheless, both studies reveal that people are aware of the benefits of automated minibuses. This means that while the attitude towards the automated minibuses is still not exclusively positive, a change in attitude is observable. For example, the social media study described in Chap. 7.3 in Korbee et al. (2021) found that the most quoted comment is that automated PT is needed and its
introduction is very welcome (17.6% of the comments). Moreover, the share of comments expressing that accidents with automated PT will be decreased has risen by 142.9% indicating that trust in automated PT also seems to have improved. At the same time, the part of negative remarks has mostly decreased in comparison to the first monitoring. For instance, the share of remarks asserting that automated public transports are dangerous has decreased (–27.8%), same for those claiming that automated minibuses are unreliable (–71.4%) or susceptible to being hacked, bugged, or used for terrorist attacks (–39.4%).

## 15.5 User Experience

A user survey is a widely accepted research tool for gaining valuable insights into the experiences of individuals using specific services and for investigating the usability of the new service (Korbee et al., 2019). The participants in this survey were approached by a safety operator while travelling with the automated minibus, i.e. they weren't travelling with the automated minibus for the sole purpose of taking part in a user survey. In the online user survey, they were asked to reflect on their experience and assess the usability, their overall satisfaction, as well as their satisfaction with specific aspects.

### 15.5.1 User Experience and Acceptance

There is a wide range of information sources through which users became aware of the automated minibus: the most important sources are the internet (28%), direct contact on test site (28%), or informal sources such as word of mouth (17%) and friends (10%). Only one in five users read about it in newspapers (18%) and social media (16%) or heard a news item on the radio (16%).

The analysis of the motivation behind the use of the automated minibus reveals that the users did not plan on using the automated minibus but rather made a spontaneous decision, following emotions such as curiosity (88%), or they cannot cite a specific reason at all (43%). This demonstrates that the barriers to use the automated minibuses are quite low, but it also indicates that there is still a lack of real conviction for this new mobility system. Positive attitudes that could result in planned use (see Theory of Planned Behaviour (Ajzen, 1991)) have not yet been established. Only 4% report that they use the automated minibus due to previous positive experiences. The analysis shows that use is presently not motivated by the market presence in the sense of concrete knowledge and conviction but is mainly driven by spontaneous interest.

Approximately half of the respondents (46%) utilized the automated minibuses as a substitute for cycling, while 17% substituted walking (as depicted in Fig. 15.14). A high substitution of active mobility could be due to the length and location of the line and the high modal share of cycling in Copenhagen.
<caption>**Fig. 15.14** Means of transport replaced by the automated minibus (in %, n = 66)</caption>

**What would you have used if there wasn't an automated minibus service?**

<figure><img src="image_184.png" /></figure>

Users were asked to reflect on their satisfaction with their last ride. In general, the users expressed a high level of satisfaction with their previous ride, with a mean score of 4.5 out of a possible 5, with 5 being the highest level of satisfaction. Almost two-thirds (59%) of the users expressed a high level of satisfaction. An analysis of the aspects that users were most satisfied with indicates that especially the overall atmosphere in the automated minibus was experienced as pleasant: temperature (4.6), cleanliness (4.6), noise level in the bus (4.5), atmosphere in the bus (4.4), and security from outside the bus (4.4). Items that are more difficult to evaluate are related to the quality of service, such as punctuality, due to the lack of experience of the users. Most of the respondents had experienced the automated minibus for the first time. Consequently, more than 42% of the respondents did not answer these questions. If they were able to assess the quality of service, the subsequent features were satisfying:

- Location of stops (4.3)

- Punctuality (4.2)

- Reliability (4.1)

- Waiting time (4.1)

Compared to the highly satisfying aspects, the following aspects were rated slightly worse on average:

- Speed/travel time (3.9)

- Frequency of service (3.9)

- Connection to other transport means (3.0)

The information offered on the bus is again satisfying (4.5); other information offers are rarely evaluated. This again underscores the fact that current users did not schedule their ride and therefore did not require more detailed information in advance.

Users appear to experience the ride in a relaxed atmosphere. This is confirmed by the results of the question ‘what describes your feeling/emotions towards automated minibuses best?’ These feelings are reported as being characterized by optimism (74%) and curiosity (68%). Suspicious or anxious feelings occur extremely rarely (less than 5%). These positive feelings are reinforced by the low incidence of
<figure><img src="image_185.png" /><figcaption>Fig. 15.15 Willingness to use again (in %)</figcaption></figure>

<figure><img src="image_186.png" /><figcaption>Fig. 15.16 Perceived benefits of automated minibuses (in %, n = 57)</figcaption></figure>

problems. When asked explicitly, 26% of the respondents (16 users out of 62 users that answered this question) encountered problems during their trip: unplanned stops (7 users), conflicts with other road members (8 users), long interruptions (5 users), or sudden braking manoeuvre (8 users).

The willingness to use the automated minibuses again is very high; 76% are very willing to use the automated minibus again, as shown in the first bar of Fig. 15.15. Only 2% of the users are hesitant to use it again, and another 2% of the users indicate that they are not willing to use it again.

In order to assess the acceptance of automated minibuses, it is essential to gain a more profound understanding of the perceived benefits (Fig. 15.16) and concerns (Fig. 15.17). Overall, the impression is that the respondents have a positive
The idea that automated minibuses will be introduced
everywhere worries me, because...

<figure><img src="image_187.png" /><figcaption>Fig. 15.17 Perceived concerns about automated minibuses (in %, n = 54)</figcaption></figure>

perception about the benefits of automated minibuses. All items asked had a mini-
mum agreement of 50% for the perceived benefit that automated minibuses can
reduce congestion and a maximum agreement of 95% that automated minibuses can
be used for routes that are less popular. The perceived benefit that automated mini-
buses can reduce congestion received the most critical assessment, with only a small
majority of 51% agreeing. These figures are similar to those in the original survey
of Keolis Downer in Australia (Keolis Downer August, 2018). The potential benefits
were scored on a 10-point scale, ranging from 8.1 for more efficient use of time to
8.9 for enhanced freedom for people with reduced mobility (PRM).

When it comes to the perceived concerns, the picture is somewhat more differen-
tiated. Five out of ten concerns are not classified as such by most users. Hence, over
50% of the users disagree with the concerns about privacy, the pleasure of driving,
learning to use the new system, job losses, and the reliability of the systems. A
smaller percentage of users disagree with apprehensions regarding the functioning
of the automated minibus. This includes the interaction with motorized and nonmo-
torized traffic, its reaction to unforeseen situations, and issues of liability in the case
of an accident.

AM development also includes creating services beyond transportation. An argu-
ment made in support of the development of such services is that it can increase the
number of users and add an additional benefit to the system compared to other transport systems. One service discussed is the presence of a safety operator. Opinions differ on whether a safety operator is needed on board the automated minibus. For a smaller share of users (18%), the presence of a safety operator is (very) important. A larger proportion of users (53%) believe that a safety operator is not important (at all).

Suggested services, such as providing feedback via QR codes, in-vehicle entertainment, access to WIFI, and an app to help with trip planning, are generally not considered as important by the respondents of this survey (Fig. 15.18). In the answer category 'other', respondents suggested the following services:

- Feedback from the vehicle concerning its decision-making and status

- Screen indicating the journey status (number of stops, estimated time of arrival, etc.)

- More handles in the bus, primarily for the passenger seat in the middle

- A plan indicating the location of the bus stops

The price of the automated minibuses is an essential selection criterion for the users for using the automated minibus. A majority of 58% state that the price is (very) important for their decision to use the automated minibus. No one indicated that price is not important at all. To explore the opportunities for future use and business cases, a question was integrated to compare the willingness to pay for the automated minibus in comparison to other means of transport. Only 24% of the users refuse to pay the same or an even higher price. However, the fact that only 11% of users are

<figure><img src="image_188.png" /><figcaption>**Fig. 15.18** Evaluation of services (in %, n = 60)</figcaption></figure>
willing to pay more indicates that users do not see such a large improvement that it would justify a higher willingness to pay. Again, this can be interpreted to imply that the automated minibus cannot entirely replace other systems but that it is seen as an obvious necessity that does not justify the additional cost for the user.

## 15.5.2 Users vs Non-users

An important question is whether respondents who have used an automated minibus are more willing to use the automated minibus in different scenarios and whether an experience with the automated minibuses decreased the perception of risks and improved the perception of benefits. In this section, therefore, we compare responses of non-users with those of users. In designing the questionnaires, we took care to keep the questions the same for the different target groups. Table 15.5 and Table 15.6 show a comparison between the responses of citizens who have never used an automated minibus (gathered through the survey in the four AVENUE cities, 2021) with citizens who have experience with an automated minibus service. For the second group, we present the results in two groups: the users from the survey in the four AVENUE cities (2021) and the users of the Copenhagen site (2020). Overall, users in Copenhagen appear to be most convinced. Slightly less but still partially more

<table><caption>**Table 15.5** Perceived benefits of automated minibus services in non-users and users</caption><thead><tr><th rowspan="2">Mean<sup>a</sup></th><th>Top boxes 4 and 5 (agree)</th><th colspan="2">Users survey</th><th>Users Copenhagen</th></tr><tr><th>Low boxes 1 and 2 (do not agree)</th><th>Non-users survey 2021 (n = 1690)</th><th>Users survey 2021 (n = 126)</th><th>(n = 57)</th></tr></thead><tbody><tr><td>... reduce the negative impact on the environment</td><td>3.9 (n = 1577)</td><td>60<br>10</td><td>3.8 (n = 123)</td><td>66<br>12</td><td>4.6 (n = 57)</td><td>95<br>2</td></tr><tr><td>... reduce congestion</td><td>3.6 (n = 1509)</td><td>49<br>17</td><td>3.7 (n = 120)</td><td>61<br>17</td><td>3.9 (n = 46)</td><td>51<br>5</td></tr><tr><td>... be more efficient, as you would be able to use your time better than in a car, walking or cycling</td><td>3.6 (n = 1520)</td><td>47<br>13</td><td>3.7 (n = 113)</td><td>55<br>13</td><td>3.9 (n = 53)</td><td>61<br>5</td></tr><tr><td>... provide enhanced freedom for people with mobility issues.</td><td>4.0 (n = 1577)</td><td>64<br>7</td><td>4.0 (n = 119)</td><td>60<br>12</td><td>4.5 (n = 51)</td><td>80<br>2</td></tr><tr><td>... be pleasant and comfortable</td><td>3.8 (n = 1497)</td><td>54<br>6</td><td>3.9 (n = 122)</td><td>68<br>8</td><td>4.1 (n = 55)</td><td>73<br>2</td></tr><tr><td>... be booked on-demand in the future</td><td>3.8 (n = 1480)</td><td>52<br>8</td><td>3.7 (n = 113)</td><td>52<br>14</td><td>4.4 (n = 49)</td><td>75<br>2</td></tr><tr><td>... cause fewer accidents, as they avoid human errors</td><td>3.3 (n = 1483)</td><td>38<br>20</td><td>3.7 (n = 114)</td><td>56<br>16</td><td>4.1 (n = 55)</td><td>75<br>6</td></tr></tbody></table>

<footnote><sup>a</sup>Means: 1, is fully disagree; 5, fully agree. Hence, the higher the mean, the more positive. For the calculations of the means, we excluded those respondents that selected ‘I cannot judge’; therefore, the basis varies—and is depicted after every means</footnote>
<table><caption>**Table 15.6** Perceived risks of automated minibus services in non-users and users</caption><thead><tr><th rowspan="2">Mean</th><th>Top boxes 4 and 5 (agree) in %</th><th colspan="2">Non-users survey 2021 (n = 1690)</th><th colspan="2">Users survey 2021 (n = 126)</th><th colspan="2">Users Copenhagen (n = 57)</th></tr><tr><th>Low boxes 1 and 2 (do not agree) in %</th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>... it is not clear how automated minibuses react in unforeseen situations</td><td>3.8 (n = 1501)</td><td>54<br>10</td><td>3.6 (n = 117)</td><td>31<br>14</td><td>3.0 (n = 47)</td><td>24<br>31</td></tr><tr><td>... it is not clear how automated minibuses interact with motorized road users</td><td>3.7 (n = 1502)</td><td>49<br>13</td><td>3.4 (n = 118)</td><td>47<br>18</td><td>2.4 (n = 49)</td><td>16<br>50</td></tr><tr><td>... it is not clear how automated minibuses interact with nonmotorized road users</td><td>3.7 (n = 1477)</td><td>46<br>14</td><td>3.3 (n = 117)</td><td>43<br>25</td><td>2.7 (n = 47)</td><td>19<br>40</td></tr><tr><td>... it is not clear who is liable in the event of an accident</td><td>3.6 (n = 1417)</td><td>24<br>14</td><td>3.3 (n = 110)</td><td>24<br>42</td><td>2.9 (n = 39)</td><td>21<br>30</td></tr><tr><td>... the software may be hacked or otherwise misused</td><td>3.6 (n = 1439)</td><td>44<br>15</td><td>3.4 (n = 118)</td><td>43<br>23</td><td>2.8 (n = 44)</td><td>19<br>35</td></tr><tr><td>... the technology is not yet ready to drive on public roads</td><td>3.5 (n = 1402)</td><td>31<br>16</td><td>3.3 (n = 115)</td><td>44<br>26</td><td>X</td><td>x<br>x</td></tr><tr><td>... the systems are not reliable</td><td>3.1 (n = 1253)</td><td>24<br>20</td><td>2.8 (n = 109)</td><td>19<br>36</td><td>2.0 (n = 42)</td><td>5<br>53</td></tr><tr><td>... the systems are not secure</td><td>3.2 (n = 1332)</td><td>29<br>20</td><td>2.9 (n = 109)</td><td>28<br>31</td><td>X</td><td>x<br>x</td></tr></tbody></table>

convinced than non-users are the users identified in the representative survey. These effects can be explained by several reasons: users may be more open-minded than non-users in the first place. Especially in Copenhagen, usage was strongly motivated by curiosity. This may also explain that these very open-minded users even show higher goodwill. Users in Copenhagen see the lowest risks and the highest benefits. This is especially the case for the statement that the automated minibuses will ‘reduce the negative impacts on the environment’. 95% of users in Copenhagen approve with this statement compared to 60% of the non-users. Both user groups display higher levels of agreement with the statements that ‘automated minibuses will be pleasant and comfortable’, that the ‘automated minibuses will cause fewer accidents’, and that the ‘automated minibuses will be more efficient’. However, non-users are not more likely to agree with the statement that the ‘automated minibuses can reduce congestion’. A higher percentage of users agree with the assertion that ‘the technology is not yet ready to drive on public roads’. Users identified in the representative survey do not differ substantially from non-users in terms of perceived risks.

<footnote>ªMeans: 1, is fully disagree; 5, fully agree. Hence, the higher the mean, the more negative. For the calculations of the means, we excluded those respondents that selected ‘I cannot judge’; therefore, the basis varies—and is depicted after every means</footnote>
## 15.6 Conclusions

The AVENUE social impact assessment demonstrates that the majority of citizens are inclined towards a positive, receptive (goodwill) attitude. More than half of the potential users ‘would be willing’ or even ‘very willing’ to use the automated minibuses irrespective of the three different scenarios tested. Of the car owners, 50% would be willing to reduce or even give up the use of their own car if the automated minibus service was offered as part of an on-demand, door-to-door service.

It is critical to understand the perspective of users and potential users. For potential users, the benefit for them (as individual users) is the most important. Potential benefits include greater temporal and spatial flexibility, reduced waiting time, high speed, and more affordable transportation. Thus, it is essential that the automated minibus can compete with other means of transport in these aspects. Since automated minibuses also compete with private cars in terms of a possible lack of private atmosphere, especially for the critical reserved and unconvinced refusers, privacy must also be the focus. Given the current state of technology, the automated minibus operates at low speed (between 8 and 20 km/h). This low speed leads to low ratings by users (Nordhoff et al., 2018). These results are corroborated by Krueger et al. (2016) who define travel time and waiting time as critical determinants for the use and acceptance of the automated minibus. In addition, it is essential for customers that automated minibuses can fulfil the need for cognitive relief. The service option that best meets respondents’ needs is the on-demand service, which has the highest willingness to use of all three scenarios, with more than 60%. There are many unfulfilled needs due to gaps in the current PT offer but also resulting from gaps experienced in using one’s own car. This creates/leads to at least latent needs which could potentially be met by the automated minibuses. In particular, the need for more flexibility could be satisfied here.

In general, the impression is that respondents have a positive perception of the benefits of automated minibuses. When it comes to the perceived concerns, the picture is somewhat more nuanced. Respondents show a high level of agreement with concerns relating to the functioning of the automated minibus. Particularly, an increase in fears about how the automated minibuses may interact with other traffic members can be observed. As long as it is still unclear to many citizens how the automated minibuses may interact with other motorized or nonmotorized traffic members, a supervisor is still required. One way to positively influence attitudes is through well-targeted communication campaigns. An additional way to shift attitudes is to bring people in contact with the automated minibuses. This is because real experience in the automated minibus generally has a positive impact on the trust in the system: a comparison of the findings of the quantitative survey with potential users and the quantitative survey with users in Nordhavn demonstrates that user experience is an essential factor in reducing the perceived concerns and increasing acceptance of the automated minibus. Nevertheless, a high level of goodwill among potential users and a high level of satisfaction among users lead to a high level of willingness to (re)use and experience the new system several times. Yet, this does not result in a regular usage or substitution of privately owned cars, which can be
observed especially in Copenhagen. The explanation for this is that, according to the findings of all included studies, citizens do not perceive an acute need for a complete substitution of current public transport offers. The extent to which classical transport modes can be substituted by automated minibuses also depends heavily on the satisfaction with the existing transport options. In cities like Copenhagen, where satisfaction with the offer of public transportation is already elevated, the need for new transportation services is thus lower. Potential users do not expect the automated minibus service to completely replace all current mobility options; it is not expected that the service will replace the use of private cars or bicycles completely, but they see the potential to substitute the current bus system.

Since there is a risk that negative experiences with conventional public transportation systems may also reduce acceptance of the automated minibuses, a clear differentiation of the new system from the existing bus offers must be communicated. The automated minibuses should not only be introduced as a new bus offer but as a completely new service that combines the advantages of a car (high level of flexibility) with the advantages public transport (higher cognitive and environmental relief as well as lower costs). We concluded that automated minibuses have the potential to fundamentally transform urban mobility when integrated as an on-demand, door-to-door service in a public transport or MaaS system. That said, it is not the innovation of automated minibuses itself that is key but rather its integration in the urban public transport systems and, at a later stage, in a MaaS / ITS. This integration will fulfil the mobility needs of potential users, taking the introduction of automated minibuses from a product innovation to a system innovation. Lastly, the AVENUE social impact assessment reaches the conclusion that by integrating automated minibuses into a public transport system, public transport can become as flexible as private cars. This will lead to high acceptance. Automated minibuses also have additional potential for improvement, as they are more efficient in use (shared) and thus contribute to a more sustainable urban mobility system.

**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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Zoellick, J. C., Kuhlmey, A., Schenk, L., & Schindel., D., Blüher, S. (2019). Amused, accepted, and used? Attitudes and emotions towards automated vehicles, their relationships, and predictive value for usage intention. *Transportation Research Part F: Traffic Psychology and Behaviour, 65*, 68–78. https://doi.org/10.1016/j.trf.2019.07.009

**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.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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**Chapter 16**
# Governance Impact Assessment, Regulatory Recommendations and Challenges

Lionel Binz

**Abstract** This chapter critically examines the regulatory and governance challenges posed by the deployment of automated vehicles (AVs) and highlights the need for adaptable laws and governance models to accommodate AVs in public transport systems. Various governance approaches are described, including global, state, self-directed, cooperative, social, technological and financial governance, and their importance in the context of AV regulation is highlighted. The discussion highlights the need for an integrated, collaborative regulatory approach that recognises the unique characteristics of AVs and argues for the harmonisation of laws at different levels to facilitate this transition. Through a comprehensive analysis, the chapter highlights the multiple impacts of AVs on legal and societal domains and presents a set of legal recommendations for authorities. These recommendations emphasise governance that not only promotes societal benefits but also addresses critical ethical, privacy and liability concerns. By charting a path towards a governance framework that supports the safe and beneficial integration of AVs into urban mobility, this chapter contributes valuable insights into overcoming the legal and regulatory hurdles that stand in the way of realising the full potential of automated public transport systems.

**16.1 Introduction**

The impact of AV on governance is not well researched yet. The aim of this chapter is to raise first insights on how governance should be adapted to the upcoming change in the mobility paradigm based on AVs as a game changer. In this chapter, the impact of automated vehicles (AVs) on legislation will be briefly exposed, as well as the types of governance that are and could be used for their actual and future

<footnote>L. Binz (✉)
Cantonal Transports Office in Geneva (Etat de Genève), Petit-Lancy, Switzerland
e-mail: Lionel.binz@protonmail.ch</footnote>
regulation. A second part will briefly present a selection of legal recommendations to public authorities that were drafted within the AVENUE project.

### 16.1.1 The Deployment of AVs Will Impact All Areas of the Law

Automated vehicles (AVs) and the mobility services they provide will require changes in all areas of the law:

* Administrative/public law (through modifications to road traffic rules and regulations (World Economic Forum, 2019, p. 5))

* Civil law (amongst others by updating the regulations of civil liability and product liability in cases of accidents (PUNEV, 2020, p. 99))

* Criminal law (for the case of accidents with bodily injuries, misuse cases of AVs and, potentially, for infringements of road traffic rules committed by AVs (Council of Europe CDPC, 2020))

These areas of the law can be either:

1. Strongly harmonised and regulated at international and/or regional (European) levels, with small to no room for different material rules (e.g. having rules with different content) at national level

2. Mostly or completely regulated by national legislation, which means the material legal rules are defined at a national level and can therefore be different from country to country (for instance, the position of the driver's wheel on the left or ride side of the vehicle)

3. A mixture between both, with for instance international principles leaving room for national differences (UNECE, 2022)

### 16.1.2 Types of Governance

As the deployment of AVs has the potential to significantly impact all areas of the law, questions related to the best or more efficient governance and regulatory frameworks arise.

Governance can be described as the process of making and enforcing decisions within an organisation, state or society. It is the process of interactions through the laws, social norms, power (social and political) or language as structured in communication of an organised society over a social system (family, social group, formal or informal organisation, a territory under a jurisdiction or across territories). It is done by the government or the parliament of a State, by a market or by a network. It is the process of choosing the right course amongst the actors involved in a collective problem that leads to the creation, reinforcement or reproduction of acceptable conduct and social order (Möltgen-Sicking & Winter, 2019).
As regards the word “law”, we will use the following terminology that makes the
distinction between:

* Laws in the formal sense, i.e. norms that are adopted by the State’s legislative body (e.g. the parliament) in the course of the regular legislative process (entitled “law”).

* Laws in the material sense, i.e. general and abstract norms that can be adopted by other bodies than the legislative, such as the State’s executive body (e.g. the government). They can be called regulations, ordinances, acts, rules and so on.

Here are some example of different approaches to governance and regulation
(International Association of Public Transport, 2021):

* *Global governance:* Global governance involves the creation of international agreements, treaties and institutions to regulate the development, deployment and use of new technologies on a global scale, providing harmonisation such as seen above. This type of governance is particularly important for technologies that have cross-border implications, such as AVs. In the field of automotive, the UNECE and other bodies are already playing this role and are working on topics related to AVs (Lagrange, 2021).

* *Governmental regulation:* This type of governance involves the creation and enforcement of laws and regulations by government bodies to control the development, deployment and use of new technologies. Governmental regulation is seen as necessary to protect public safety and security, privacy and other public interests. In the case of automated vehicles, governmental regulation will be necessary for areas such as road traffic safety, cybersecurity, data protection and liability, for instance (World Economic Forum, 2019, pp. 8 et seq.). Governmental regulation at state level takes place into global governance, where it can be partly defined by binding international instruments. In the field of new technologies, the European Commission has already enacted various acts regulating amongst others the processing of data (such as the General Data Protection Regulation, the upcoming Data Act, the Data Governance Act, the Digital Markets Act, the Digital Service Act, the AI Act etc.).

* *Self-governance:* This type of governance is based on the principle of voluntary compliance by individuals or organisations that are involved in the development, deployment or use of new technologies such as AVs. Self-governance may involve the creation of industry standards, codes of conduct or best practices that guide the behaviour of the stakeholders in a particular industry. For AVs, the interests of public safety and security (amongst others) do not allow relying on self-governance alone, and governmental regulations are required. In particular fields, where safety and security are not at stake, lawmakers and governments might allow the stakeholders (and the markets) to regulate themselves, following a so-called laissez-faire strategy.

* *Collaborative governance or co-regulation:* This involves collaboration between lawmakers (e.g. governments and legislative bodies), industry, researchers and other stakeholders to develop and implement policies and standards (Law
Commission of England and Wales and the Scottish Law Commission, 2022a). This approach should be followed for AVs, especially at this early stage of AV deployment, as it helps ensuring that the interests of all stakeholders are taken into account and can promote greater acceptance of automated vehicles amongst the public. Safety, with, for instance, the definition of passive safety requirements (and the necessary trade-offs related to vehicle architecture), might be solved efficiently through collaborative governance (International Association of Public Transport, 2021).

*   **Social governance:** Social governance refers to the use of non-governmental organisations, community groups and other civil society actors to shape the development and deployment of new technologies. Social governance may involve public education, advocacy or activism to promote responsible innovation and ensure that the benefits of new technologies are shared equitably. Social governance will be needed to ensure the acceptation of AVs by the public and to ensure, for instance, that PRMs get involved in the development and will benefit from AVs.

*   **Technological governance:** Technological governance involves the development of technical standards and protocols that ensure the interoperability and compatibility of automated vehicles with other systems and technologies. This approach can help ensure that automated vehicles can operate safely and efficiently within existing transportation systems. In the field of AVs, AVENUE, for example, has shown the necessity to provide open datasets and application protocol interfaces (APIs) for the common good.

*   **Financial governance:** Financial governance involves the development of policies and regulations related amongst others to the financing of automated vehicles, such as tax incentives or subsidies for the development and purchase of automated vehicles by public transport operators. Regulation also plays an incentive role, as “strict” regulations with high type-approval costs or triggering high costs for the deployment of automated vehicles have an important financial impact for vehicles manufacturers or public transport operators. Besides, it should be noted that an optimal use of AVs might require massive investments in public infrastructure.

During AVENUE, voices have been raising the fact that European regulations, for instance, in the field of type-approval and/or getting an approval for AV experiments, as well as in the field of data privacy with the General Data Protection Regulation, tend to be more restrictive than other regulations (viz. in the USA or in Asian countries). According to these, from an economical point of view, this would result in a comparative disadvantage for research and vehicle manufacturers in Europe. A counter-argument is that, in the long run, these higher standards will also support achieving better products, output and results, increasing safety and security and avoiding unnecessary incidents. As of today, according to the author's opinion, the approach to vehicle automation followed in the USA has resulted in more accidents with body injuries or even death cases involving advanced driver assistance systems or automated driving systems than in Europe (Tesladeaths, n.d.).
Overall, effective governance of automated vehicles will likely require a combination of these different approaches, tailored to the specific needs and characteristics of different regions and countries but also to the specific AV domain to regulate.

### 16.1.3 Major Impacts on Legislation and on the Societal Activities they Regulate

The deployment of automated vehicles will have major impacts in many fields involving public authorities and interests, far beyond formal regulations provided by public, civil or criminal laws:

*   **Legal and regulatory framework:** As mentioned before, the introduction of automated vehicles will require a re-evaluation of many existing legal and regulatory frameworks, as it will be briefly summarised in the following chapters. Lawmakers and executive bodies at different levels will need to consider new international and national regulations and instruments to address the unique characteristics of automated vehicles. AVs are part of the upcoming technological revolution that will be triggered by artificial intelligence and the internet of things which will require updates and changes in many existing regulations as well as the creation of new legal frameworks.

*   **Infrastructure and urban planning:** Automated vehicles will require new infrastructure and urban planning (Martin et al., 2023) to support their safe and efficient operation. They could also completely change the landscape of our roads and cities. Some changes have already begun: vehicle manufacturers are developing the use case of “Remote Controlled Parking” systems (RCP), where an “Automatically Commanded Steering Function” (ACSF) of the vehicle performs the dynamic driving task (DDT) of the vehicle without any human intervention. In this use case, the driver and passengers get out of the vehicle, which is empty of any human driver and parks itself into a free parking lot “alone” by using the ACSF. This allows gaining parking space, as vehicles equipped with RCP can park themselves in much narrower spaces than the existing ones, where space is needed for the driver and the passengers to open the doors of the vehicle in order to enter or exit the vehicle (Huonder & Raemy, 2016).

*   **Public investments in new technologies and infrastructure** will be needed, forcing lawmakers and executive bodies to define the AV environment they wish for their roads, such as the type of communication networks (vehicle-to-vehicle, vehicle-to-infrastructure etc.) and related sensors and communication channels (Nationale Plattform Zukunft der Mobilität, 2020), charging stations etc. to support the deployment of AVs. These developments will also be pushed by the fact that conventional vehicles are becoming connected vehicles, and AVs are a subcategory of connected vehicles. In this field, decision-makers will have to
proceed to technological, political and financial trade-offs, for instance, whether they want AVs that do not rely on sensors placed in the road infrastructure or whether the road infrastructure should be able to communicate with AVs (National Plattform Zukunft der Mobilität, 2021a, 2021b) to provide them information, and/or whether redundancies are needed, in particular for the sake of safety and security. An example: using its sensors, a fully automated vehicle could detect on its own whether a traffic light is red or green, but an “intelligent” or connected traffic light could also communicate through wireless communication channels if it is red or green and provide this information to the AV. These decisions will have important financial impacts on public budgets and might also impact the rhythm of AV deployment, by accelerating or slowing it down.

*   **Employment and workforce:** The introduction of automated vehicles will have a significant impact on the transportation industry, leading to inevitable job losses in certain sectors such as public transportation, trucking and taxi services (International Association of Public Transport, 2017). Governments will need to consider the impact on these workers and develop policies to address the potential disruption. Driver unions have, for instance, started to protest against the development of automated vehicles in California in the beginning of 2024 (TheLastDriverLicenseHolder, 2024a), and a few cases of attacks against automated vehicles have been reported recently in San Francisco (TheLastDriverLicenseHolder, 2024b). In parallel, AVs will also create new jobs that didn’t exist previously.

*   **Social and economic impact:** Automated vehicles will significantly impact society and the economy, by (possibly) reducing traffic congestion, improving safety and increasing mobility for persons with reduced mobility. Governments will need to consider the potential social and economic impact of automated vehicles and develop policies to support their deployment in a way that benefits society as a whole (Smith, 2016). According to the research of the AVENUE projects, some current forms of AVs, such as robotaxis or private automated vehicles, will not benefit to society as much as shared and automated forms of public transportation, which should be sustained by public authorities.

*   **Ethical considerations:** The development and deployment of automated vehicles also raises ethical considerations, such as how to program the automated driving system to take decisions in situations where competing interests are at hand, as described in the famous “trolley dilemma” (Foot, 1978) where an automated driving system would need to choose between hurting the AVs’ passengers or hurting third parties such as pedestrians on the road. Governments will need to consider these ethical considerations and develop policies to ensure that automated vehicles operate in an ethical and socially responsible manner. Germany, for instance, has already tackled this question with its Ethics Commission that released guidelines for AVs in 2018 (Ethik-Kommission, 2017).
## 16.2 AVENUE Recommendations

The following sections will briefly present the most important legal recommendations for public authorities that were drafted during the AVENUE project. They enter into the categories of global governance, governmental regulation and collaborative governance or co-regulation defined above.

The first section will be dedicated to cross-sectoral recommendations, followed by another dedicated to specific topics.

### 16.2.1 General Recommendations Not Limited to Specific Areas of the Law

#### 16.2.1.1 Prioritising the General Interest Through Encouraging Legislation

Roughly summarised and without going into the details of the SAE Levels, the current development of AVs can be categorised in three different approaches or use cases:

* Privately owned automated vehicles, e.g. conventional vehicles with a driver and a steering wheel, improved with advanced driver-assistance systems (ADAS) or automated driving systems (ADS), answering private mobility needs of individuals owning these vehicles.

* Robotaxis, which are automated vehicles that can be similar to privately owned vehicles, but are owned and deployed by commercial companies with the purpose of providing ride-hailing services and generating profits for the companies.

* Shared automated vehicles integrated in a MaaS (Mobility as a Service) or an ITS (Intelligent Transport System), often falling into the category of minibuses/shuttles, deployed by transport operators providing traditional transport services and/or used door to door and on demand in a MaaS or an ITS as mobility gap filler to complement private or public transport services enabling positive externalities (see Chap. 18 about vision).

If we look at the current regulations being adopted by public authorities in the field of automated vehicles, the current lawmaking momentum lies mostly with the regulation of the first two categories, namely, privately owned automated vehicles and/or robotaxis:

* The first AV-specific regulation enacted at international level (as a result of global governance defined above) was the regulation dedicated to the Automated Lane Keeping System (ALKS), a (SAE Level 3) highway traffic jam assistant designed for conventional, privately owned vehicles with automated driving systems. It was enacted by the United Nations Economic Commission for Europe (UNECE) in 2020 and consolidated to 130 km/h in 2022 (UNECE, 2022). Since then, in a
few European countries, ALKS has been type-approved and can be lawfully used on certain highways (KBA, n.d.).

* At national levels, most of the experimentations and legislative output are focused on the regulation of robotaxis (see, for instance, in San Francisco with Cruise and Waymo amongst others (TheLastDriverLicenseHolder, n.d.)).

This can be explained by the fact that these types of vehicles (e.g. privately owned vehicles with ADAS or ADS or robotaxis) and their use cases are promoted by private stakeholders with very important resources wishing to enter in what they consider a new promising market. These stakeholders are either “tech” companies (such as Waymo, a company curated by Alphabet/Google (Waymo, n.d.), or Mobileye, curated by Intel (Mobileye, n.d.)) or manufacturers of conventional vehicles vying to join the automation trend (such as Mercedes (The Verge, n.d.), Cruise/General Motors (Cruise, n.d.) etc.).

They can also be both type of actors, working together in the form of joint ventures and consortiums. As a consequence, the legislation enacted by legislative bodies and governments follows the technical developments proposed by these stakeholders and reacts to it with a “bottom-up” approach, similar to collaborative governance, although often the public authorities lack of the necessary knowledge and involvement.

Shared automated minibuses, which are, according to the AVENUE research results, the more desirable automated driving use cases from a societal point of view, did not stand in the focus of lawmakers until recently.

Thus, priority should be given to their regulation, as research within AVENUE (and other projects as well (Fabulos, n.d.) has demonstrated that shared automated minibuses are the use case of AVs that will provide greater and better societal benefits (International Association of Public Transport, 2021) than privately owned automated vehicles or robotaxis, which might trigger negative externalities such as an increase in traffic congestion and weaken public transportation and accordingly generate additional costs for public transport authorities.

Therefore, governments and lawmakers should play an orientating role by encouraging the most promising use cases and by creating incentives. Accordingly, they should also regulate and mitigate use cases considered to be less desirable from a societal point of view.

### 16.2.1.2 Cooperation and Harmonisation at European Level

For the development of automated vehicles, at European level, an aligned and holistic legal European approach towards automated vehicles is desirable (European Commission, 2020a).

As seen during the AVENUE project, the same automated minibuses went through very different approval proceedings in the four countries of experimentations, with important time differences, to receive an approval from public authorities. The cost and documentation required from each of the partners in France,
Luxembourg, Denmark and Switzerland were also very different, although the automated minibuses were of the same model and manufacturer.

Therefore, an aligned and holistic legal European approach is desirable and necessary for many reasons, starting from road safety to a commercial, single market perspective, with the promotion of the European industry and encouragement of technological leadership. Such an approach and harmonisation can be defined through European institutions and regulations, which have yet to be created and defined.

### 16.2.1.3 Cooperation Between All Stakeholders

In order to cope with the fast evolving technologies and business models involving automated vehicles and minibuses, various governance models can be implemented, such as collaborative governance or co-regulation, where all stakeholders get involved in the definition of the regulation/policy through an inclusive approach and with the sharing of knowledge and resources (International Association of Public Transport, 2021).

Such approaches are needed as private stakeholders (vehicle manufacturers, “tech” companies, software developers etc.), public transportation operators (PTOs), public transportation authorities (PTAs) and further stakeholders (organisations in charge of accident analysis, insurers etc.) already involved in the deployment of AVs can identify their needs and legislative issues through their experience already gathered while experimenting.

Similar results might also be achieved through self-governance of non-institutional platforms and/or private associations, representing the interests of the various stakeholders. Harmonisation through public institutions or involving public institutions might be more desirable, as private associations might follow and defend the interests of their members that may differ and strive for different goals such as maximisation of profits. This might result in unsatisfying results from a societal point of view, as the potential positive societal benefits and outcomes studied in the AVENUE project might not be reached.

### 16.2.1.4 Coordination Between Authorities

AVs will stand in the focus of different governmental authorities (Law Commission of England and Wales and the Scottish Law Commission, 2022b; Monti & De Streel, 2022), as they function based on multiple technologies supervised by different authorities: type approval authorities for the “road vehicle” components, telecommunication authorities for the use of antennas and radiocommunications, data protection authorities as regards the processing of personal data and even competition authorities in the case of potential concentration of market power by actors involved in the AV chain.
As of today, these authorities rarely work together. These authorities will also need to coordinate themselves and develop new skills and competences related to digitalisation and automation.

### 16.2.1.5 Standardisation

As a result of self-governance, the vehicle industry often relies on proprietary standards and formats (for instance, each vehicle manufacturer uses its own format of a high definition map, which is generally not compatible with other manufacturers' maps). This creates high curation costs when data is transferred from one company to another and high costs to develop new services (Iacob et al., 2021; Easme & Cosme, 2018). Standardisation and interoperability in all domains should be encouraged as much as possible.

### 16.2.1.6 Definitions and Vocabulary

The vocabulary used to describe automated driving technologies should not mislead the public and create false expectations. A certain level of harmonisation that would increase the public's awareness and comprehension of these new technologies is desirable (Nationale Platform Zukunft der Mobilität, 2021b).

## 16.2.2 Recommendations Related to Road Traffic Rules and Conventions

### 16.2.2.1 Amendments to the Existing Road Traffic Rules and Conventions

During many decades, the presence of a human driver, located inside the vehicle and performing the “dynamic driving task”, was a central assumption of the international road traffic conventions such as the Vienna and Geneva Conventions, as well as for many national road traffic laws. With the deployment of AVs, a fundamental paradigm change is currently operating, and states should depart from this twentieth-century approach to driving (Smith, 2022; Vellinga, 2019).

At international level, the UNECE is currently working to deliver a regulation for ADs in 2026 (Pichereau, 2021). However, the timeframe for the final adoption for such regulation might be too slow and take too long before the effective deployment on AVs on the roads.

Each country should therefore proceed as soon as possible to an extensive assessment of its road traffic regulations, in order to prepare and amend:

* Legal or technical provisions that implicitly or explicitly relate to the presence of a human driver performing the dynamic driving task from inside the vehicle
* Legal or technical provisions that might pose interpretation problems with the introduction of automated vehicles

* Legal or technical provisions involving human negotiation, interpretation or communication in specific situations (for instance, when a policeman is regulating the traffic at a specific spot through hand signals)

It should also be noted that automated vehicles are programmed to comply strictly with the existing traffic rules and not infringe them. In the road traffic reality with human drivers, however, various situations can occur that require a trade-off between obstructing the traffic flow and infringing road traffic rules. For instance, after an incident with the damaged vehicles located on the lane, vehicles driven by human drivers will carefully cross the lane to circumvent the obstacle. This simple arbitration might be difficult to implement in the case of automated vehicles, as this solution might imply to infringe traffic rules.

Solutions to such problematic situations requiring trade-offs and arbitrations should be discussed with the various concerned authorities (such as road safety, police, traffic law, politicians etc.), the developers of automated vehicles and researchers.

### 16.2.3 Supervision of Automated Vehicles

#### 16.2.3.1 Allow Remote Supervision of AVs

Having a safety operator located inside each automated minibus (as required by today's experimentations' approvals) generates important functioning costs and does not constitute an economically viable solution in the long term for commercial and large-scale deployment (Probst, 2021). Therefore, legislative and executive bodies should promote further research and create innovative regulations, such as "regulatory sandboxes", permitting the deployment of automated minibuses (and AVs in general) without a safety operator, the safety operator being replaced by a (human) remote supervision that monitors several automated minibuses in parallel and intervenes if/when necessary.

Enabling the remote supervision of automated minibuses and vehicles (e.g. without a safety operator located inside the vehicle) and creating a dedicated legal framework are crucial steps for PTOs to deploy automated minibuses commercially at large scale and to provide economically viable transportation solutions in the future.

Some European Member States such as France and Germany, as well as the European Implementing (European Commission, 2022) and Delegated Regulations (European Commission, 2022) issued in July and August 2022, as well as the interpretation report published in February 2024 (EU Regulation 2022/1426), are paving the way from a legal point of view in Europe, by permitting experimentations and/or by enacting legal frameworks allowing remote supervision at distance under an
ordinary approval regime. In February 2023, the Law Commission of England has published its advice related to remote driving for drivers beyond line of sight (Lawcom, n.d.), which includes the question of remotely driving AVs.

### 16.2.3.2 Qualifications of the “Safety Operators”

In parallel and during a “transition phase”, the qualifications currently expected from safety operators located inside automated minibuses are often as high or even higher than for drivers of conventional public transport vehicles (Roedl, n.d.), generating important functioning costs. Therefore, the requirements for safety operators should be watered down and adapted to the reality of operating automated minibuses. After a first period of practice where the AVs’ operational design domain is tailored to the physical location of their deployment, and problematic “hotspots” get identified, AVs are able to circulate with operation time over 90% in automated mode, where the automated driving system is successfully performing the automated driving task without any intervention of the safety operator.

The situation in Europe is quite behind in comparison to other countries. In 2022, Cruise and Waymo received commercial licenses to deploy small fleets of robotaxis in San Francisco, operating only with remote supervision: robotaxis are cruising the streets without any human driver inside, besides the passengers (TheLastDriverLicenseHolder, n.d.). Beginning in March 2024, Waymo received an authorisation to expand its robotaxis beyond San Francisco and south of the city to the Bay Area, as well as in parts of Los Angeles (TheLastDriverLicenseHolder, 2024c).

Other important topics shall be covered, as mentioned within the AVENUE research, for instance, as regards the passive safety of automated minibuses. Their particular (and new) architecture and features require urgently the definition of passive safety requirements. Reflexions and studies are necessary in order to arbitrate on important trade-offs in this field.

## 16.2.4 Recommendations Related to the Regulation of AV Data

### 16.2.4.1 Competition

The European Commission has published various legal acts and drafts related to current data issues, such as the Data Act and the Data Governance Act (amongst others). These acts are a positive first step, but further specific acts dedicated to the automotive industry, and automated vehicles, should be enacted quickly. Governments and lawmakers should provide their best efforts in creating legislations that anticipate and guide the technological developments. It should be avoided that, as seen until recently in the field of digitalisation, one or a few players get into
a position where they can distort competition, due to their actual power on such market, generated by creating “winner takes it all” and “lock-in” effects within a nearly closed ecosystem (see details in chapter below about the vision of AVENUE). Besides open data (see below), open API and even open map are key issues to keep competition but also to better satisfy the citizen needs and to enable wealth (see vision).

The AVENUE project has demonstrated that vehicle manufacturers and/or tech companies developing automated vehicles and minibuses benefit of a “de facto” control over vehicle data. This means that they have control and can monitor key steps related to the collection of car data. They may, for instance, decide on the collection (for instance, sensors such as video cameras recording the AVs’ surroundings), the processing (storage of the data collected on temporary or permanent data storage devices), the transmission of data to third parties, the encryption for data etc.

Thus, vehicle manufacturers can decide, through agreements with other parties, to whom and to which type of data they provide access to. Such a “de facto” position places them in an undesirable gatekeeper position that should be mitigated (Ernst & Young, 2021; Andrasko et al., 2021) by creating a legal framework regulating data-related issues, as contractual arrangements between operators (in a form of self-governance) will not be sufficient.

One of the suggestions of the AVENUE project is to define through dedicated regulations open datasets according to the rules of the Data Governance Act that should be freely shared between the stakeholders, as well as open application programming interfaces to enable the transmission of data and leverage it for the greater public interest (Roedl, n.d.). The trustworthy mobility data-sharing system should accordingly create wealth for the society and provide control to the citizen and trust to the companies. The availability of mobility data also raises the question of privacy (see below) but also of competition as foreign competitor could use AI technology and less trustworthy data to take competitive advantage on European OEM (Original Equipment Manufacturer) competitors. A symmetry in competition should accordingly be guaranteed for European OEMs.

At international level, mutual assistance and questions related to data sovereignty should also be discussed, amongst others, as the data flow might imply that data collected from an automated vehicle located in country A is transmitted, processed and stored in another country B, sometimes outside of the European Union. This will cause problems when personal data gets transmitted to jurisdictions that do not have an adequate level of personal data protection.

### 16.2.4.2 Data Privacy

The deployment of AVs will raise many privacy concerns, as they are able to collect, store and transmit large amounts of personal data through their various sensors and communication channels.
Here are some of the key privacy issues posed by automated vehicles:

* Definition of personal data: In the context of automated minibuses and mobility-related applications, what data is to be considered personal and non-personal (technical data) is not straightforward in all cases. Further guidance in this area would be necessary to avoid the proliferation of different interpretations of this concept, which has an important impact in practice, for example, on the anonymization of personal data (Campmas et al., 2021; Benyahya et al., 2022).

* Processing of personal data: AVs can collect and store a large amount of personal data that can be used for various purposes, such as research and quality improvement, but also for marketing and establishing data profiles for commercial purposes. This may conflict with important data protection principles such as the data minimisation and the purpose limitation principles (Balboni et al., 2020; Finck & Biega, 2021).

* Transparency: AVs should be transparent about the data they collect, store and share, so that the passengers are aware of the data that is being collected and how it is being used (Horizon 2020 Commission Expert Group, 2020). Achieving transparency towards third parties located outside AVs such as other drivers, cyclists and pedestrians will be harder to achieve, but solutions might be achieved through further research and discussion.

### 16.2.4.3 Cybersecurity

The storage and transmission of both personal and vehicle data must be protected to prevent unauthorised access and use of the data. This requires strong data security measures to ensure the protection of the data but also to prevent third parties to take control of AVs (see chapters above).

It is important for governments and manufacturers of automated vehicles to address these privacy issues (Vellinga, 2022), to build public trust in the technology and ensure that the personal data of the vehicle's occupants is protected. Adequate privacy protections will help promote the widespread adoption of automated vehicles and ensure that they are used in a responsible and ethical manner.

### 16.2.4.4 Liability

The general opinion amongst legal scholars (Lohmann & Müller-Chen, 2017) today considers that automated vehicles in their current stage of deployment will not create major gaps in the applicable European liability framework, amongst others, due to the Motor Vehicle Insurance Directive, which requires that all vehicles registered in the EU hold mandatory third-party liability insurance to cover civil liability for the use of motor vehicles. However, various potential issues have been identified.

It should be avoided to place the burden of proof with the victim of an incident (as it might be the case in product liability cases, for instance (Piantoni et al., 2021;
Buiten et al., 2021). Due to the complexity of AI products and their algorithms, liability claims would become difficult or overly costly to prove for victims, and consequently they may not be adequately compensated. The same applies to (complex) decision-making processes, where the decision-making chain of AI products cannot be explained afterwards and how the (problematic) output resulted (Horizon 2020 Commission Expert Group, 2020).

Information asymmetry is another issue. As mentioned in the chapter dedicated to data, vehicle manufacturers currently benefit of a “de facto” control over the data collected and processed by the sensors of automated vehicles. To understand the liability chain (and causality between a defect and a damage), accessing vehicle data will be of paramount importance. Access to this data, and understanding the data, will be challenging for victims and their representatives.

One solution would lie in the reversal of the burden of proof, or in lowering the burden of proof, as suggested in the current revision of the Product Liability Directive. The creation of a trusted third party like it is existing in the automotive industry could be a solution (see vision chapter below).

In addition, the deployment of automated vehicles and minibuses implies a complex ecosystem with a plurality of actors involved in the services. This could result in increasing difficulties to assess where a potential damage originated and to assign liability (European Commission, 2020b). Forms of shared responsibility, as well as a shift towards liability of vehicle manufacturers and tech companies etc., should be studied, discussed and envisaged.

## 16.3 Accessibility

In the field of automated minibuses, the applicable accessibility requirements could still be improved by vehicle manufacturers, as demonstrated by the existing vehicle constructions, amongst others, in the AVENUE project. Moreover, the mobile applications related to the services provided by automated vehicles and minibuses also lack accessibility.

On the one side, deployers of automated minibuses, public transport operators and public transport authorities should include accessibility as an important criterion in the tender process. In many countries, accessibility is a prerequisite for the funding of vehicles by public authorities.

On the other side, vehicle manufacturers shall apply accessibility requirements defined by European standards in the earliest construction and design phase of automated vehicles (European Commission, 2018) (“accessibility by design” in a similar way than “privacy by design and by default” for data privacy). As a general principle, discriminatory design and service provision should be prevented (Horizon 2020 Commission Expert Group, 2020).

Accessibility of future automated public transport of all types needs to be thoroughly researched, and findings need to be designed into guidelines issued on public transport accessibility levels. Persons with reduced mobility (PRMs) need to