still technical limitations regarding the sensors. Because the vehicle could not inde-
pendently monitor dead angles, the intervention of the operator onboard was neces-
sary for safety reasons.

11.4.2 Remaining Technical Obstacles

In Fig. 11.2, the remaining technical obstacles and their evaluated relevance are
consolidated.

The assessment showed that the implementation of automated minibuses in pub-
lic transport is dependent on numerous technical obstacles and success factors that
need to be addressed and overcome. The deployment of automated driving in vari-
ous settings, both urban and rural, requires that the technology is capable of han-
dling all safety-related scenarios and that it is effective, intelligent, connected, and
fully integrated into the digital traffic infrastructure.

In terms of automated shuttle capabilities and environment, LiDAR and camera tech-
nologies in particular have been classified as key drivers for the implementation of auto-
mated vehicles. The importance of these technologies lies mainly in the fact that they
detect and avoid obstacles and are ultimately decisive for the trajectory of the vehicle.
The use of machine learning algorithms and AI technologies will also play an important
role in enabling vehicle positioning in diverse traffic patterns and infrastructures. The
perception and determination capabilities of automated vehicles will also be considered
here as object differentiation in traffic becomes increasingly important, according to
experts. The combination of the various technologies and a high processing perfor-
mance in terms of sensor data is the determining factor in this context in order to achieve
safety-relevant functions (e.g. braking) and thus make the vehicle more reliable and
safer in its driving behaviour. All sensors should be functional under all conditions.
Currently, heavy rain and snow cause disturbances to the sensors and lead to many
impairments of the operation. GNNS and the ability to locate the vehicle with precise
measurements also need continuous improvement. However, through the use of an Ntrip
real-time kinematic (RTK) service, with which differential GNSS data is disseminated
via the Internet, the localization and the positioning of the vehicle could be enhanced
and therefore supported the smooth operation of the vehicle. The urban infrastructure is
another key factor for the implementation of automated minibuses and should be con-
sidered in local land use planning. Moreover, cybersecurity measures are imperative to
safeguard automated vehicles against potential threats, necessitating the evolution of
laws and standards to address emerging challenges that come with increased processing
power and complex sensory signal streams.

Additionally, human involvement remains crucial in the operation of automated
minibuses, as safety operator had to intervene in various situations, including blind
spots or when overtaking parked vehicles. However, a safety operator is generally
required, due to legal considerations. For the future the transition from an on-board
safety driver to remotely working supervisors has to be developed and tested
accordingly.
<table><caption>Fig. 11.2 Evaluation of technical obstacles</caption><thead><tr><th>Criteria</th><th>Keolis</th><th>TPG</th><th>SLA</th><th>AM</th><th>Ø</th><th>How relevant</th></tr></thead><tbody><tr><td colspan="7"><strong>1. Automated Shuttle Capabilities</strong></td></tr><tr><td>Construction Quality</td><td>5</td><td>4</td><td>2</td><td>4</td><td>3,8</td><td>Very</td></tr><tr><td>Traffic Regulation &amp; Choice of Roadway</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>Medium</td></tr><tr><td>Perception &amp; Ability to determine</td><td>5</td><td>4</td><td>3</td><td>3</td><td>4</td><td>Very</td></tr><tr><td>Driving Strategy</td><td>3</td><td>2</td><td>1</td><td>3</td><td>2,3</td><td>Less</td></tr><tr><td>Interoperability</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>Less</td></tr><tr><td colspan="7"><strong>1.1. Hardware-related Factors</strong></td></tr><tr><td>Sensor-Position (LIDAR 360°)</td><td>3</td><td>4</td><td>5</td><td>4</td><td>4</td><td>Very</td></tr><tr><td>Sensor-Resolution (VLP16 LIDAR)</td><td>3</td><td>4</td><td>2</td><td>3</td><td>3</td><td>Medium</td></tr><tr><td>UMTS &amp; 3G-Modem</td><td>3</td><td>3</td><td>1</td><td>2</td><td>2,3</td><td>Less</td></tr><tr><td>Charging Time</td><td>3</td><td>2</td><td>1</td><td>2</td><td>2</td><td>Less</td></tr><tr><td>Switch</td><td>3</td><td>2</td><td>1</td><td>2</td><td>2</td><td>Less</td></tr><tr><td colspan="7"><strong>1.2. Software-related Factors</strong></td></tr><tr><td>Hit-Ratio</td><td>3</td><td>3</td><td>1</td><td>1</td><td>2,3</td><td>Less</td></tr><tr><td>Gateway to Fleet Management System</td><td>5</td><td>4</td><td>1</td><td>3</td><td>3,3</td><td>Medium</td></tr><tr><td colspan="7"><strong>2. Automated Shuttle Environment</strong></td></tr><tr><td>Mapping &amp; Modification of Routes</td><td>5</td><td>4</td><td>5</td><td>4</td><td>4,5</td><td>Most</td></tr><tr><td colspan="7"><strong>2.1. Weather-related Limits</strong></td></tr><tr><td>Sensor-related</td><td>5</td><td>4</td><td>5</td><td>3</td><td>4,3</td><td>Very</td></tr><tr><td>Battery-related</td><td>3</td><td>3</td><td>5</td><td>1</td><td>3</td><td>Medium</td></tr><tr><td>Power-related</td><td>3</td><td>3</td><td>5</td><td>1</td><td>3</td><td>Medium</td></tr><tr><td colspan="7"><strong>2.2. City Infrastructural Limits</strong></td></tr><tr><td>Reference Points</td><td>3</td><td>4</td><td>5</td><td>4</td><td>4</td><td>Very</td></tr><tr><td>Road Markings</td><td>3</td><td>3</td><td>1</td><td></td><td>2,3</td><td>Less</td></tr><tr><td>Surface of Roadway</td><td>4</td><td>3</td><td>1</td><td>3</td><td>2,8</td><td>Medium</td></tr><tr><td colspan="7"><strong>2.3. Digital Infrastructural Limits</strong></td></tr><tr><td>GPS &amp; GNSS Signal</td><td>3</td><td>4</td><td>4</td><td>4</td><td>3,7</td><td>Very</td></tr><tr><td>3G/4G Signal</td><td>3</td><td>4</td><td>5</td><td>2</td><td>3,5</td><td>Very</td></tr><tr><td>Data Transfer &amp; Updates</td><td>5</td><td>3</td><td>1</td><td>3</td><td>3</td><td>Medium</td></tr><tr><td colspan="7"><strong>3. Human Involvement</strong></td></tr><tr><td>Necessity of on-board attendant</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>Most</td></tr><tr><td colspan="7"><strong>4. Additional Technical Findings</strong></td></tr><tr><td>Processing Power</td><td>5</td><td>5</td><td>3</td><td>4</td><td>4,3</td><td>Very</td></tr><tr><td>Speed of the Shuttles</td><td>3</td><td>3</td><td>4</td><td>4</td><td>3,3</td><td>Medium</td></tr><tr><td>Cyber Security</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>Most</td></tr><tr><td>5G Networks</td><td>3</td><td>3</td><td>2</td><td>2</td><td>2,7</td><td>Medium</td></tr><tr><td>Number of Shuttles on the same Route</td><td>3</td><td>1</td><td>4</td><td>4</td><td>2,7</td><td>Medium</td></tr><tr><td>Safety demonstration</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>Most</td></tr><tr><td>Disabled access</td><td>5</td><td>5</td><td>5</td><td>5</td><td>5</td><td>Most</td></tr></tbody></table>
### 11.4.3 Remaining Specific Obstacle-Related API

#### The Manufacturer's API

Based on the elaboration of the experts, the so-called Navya API is an interface that enables the communication between Navya vehicles and external systems. This API makes the link between the Navya drive software and the items that are necessary to share with the fleet management system. With this linkage, the fleet management system is able to monitor and command shuttles and missions. Regarding the on-demand features, the expert stated that Navya proposes a specific mode that allows an external partner to send punctual on-demand missions to the vehicles. Those missions are executed among other missions that are automatically created by manufacturer's system itself. Nevertheless, there is now a new mode (called partner mode) available within the latest software version. According to the experts, this new mode enables the fleet management system to not consider predefined missions but only those sent by the operator. Hence there are two options with which a partner can send missions to the automated minibus:

1. The certain partner can be the only mission provider and have control over the fleet.

2. The partner can be a provider of punctual missions that complete a predefined service with on-demand missions.

These missions are then stacked in a queue. The operator can modify this queue (e.g. by removing them). Nevertheless, it was stated that the overriding of an on-going mission is not possible once a mission is assigned to a vehicle. Within the AVENUE project, the assignment of missions through the Navya API is possible. A conducted update of API to the vehicles in 2021 was released, enabling some basic on-demand functionalities. However, the maturity of the technologies is still a considerable aspect to stabilize and make on-demand a fully functional product for all PTO's applying Navya vehicles. Since now, multiple operators (such as AM and TPG) within AVENUE have been trying to use the automatic assigning missions features and received important learnings to prepare and organize for this future way of orchestrating vehicle start and end destinations.

#### On-Demand Challenges

With the reference to on-demand services, several challenges are existing for its full deployment. Hereby it was outlined that overwriting of ongoing missions is not possible. This limitation encompasses the cancelling of ongoing missions and dynamic mission changes which are currently not implementable. According to the experts, this aspect is the key limitation for enabling shared rides as it has significant operational impact and is the most critical feature for the purpose of on-demand mobility services. Especially when a mission is already executed, no other mission can be inserted to modify the vehicle's plan regarding its current route. Besides these aspects, there are additional challenges restricting the implementation of on-demand transportation services. Currently it is not possible for the fleet management platform to control the routing of the vehicle. Additionally, it is conductible to detect
that the person entering the vehicle actually booked his or her place. The start of the missions is also currently restricted as the vehicle cannot automatically recognize when a passenger enters. Consequently, the automated minibus was not able to detect when it has to close the doors and start the underlying mission. Furthermore, recognizing that the right persons exit the vehicle—before starting a new mission (or going back to its garage)—is also a functionality that is currently not enabled. However, it was stated that there will be a new software set-up enabling to override an ongoing mission. Regarding this set-up, it is being worked on providing more accurate information of a mission, as a prerequisite to override an ongoing mission.

## 11.5 Technical Improvements Through AVENUE

Proven by this study, technical advances have been made in the use of automated minibuses within the scope of the AVENUE project. These advances are concisely summarized in Table 11.3.

The AVENUE project demonstrates notable advancements in the capabilities of automated minibuses, enhancing their functionality and reliability within the public transport domain. Among these advancements is the integration of on-demand features, facilitating a more flexible assignment of missions within the project framework. This development allowed for greater adaptability to changing passenger demand and traffic conditions, ultimately improving the efficiency of the minibus service. Moreover, significant progress has been made in object recognition technology, enabling minibuses to detect overtaking vehicles on the road with greater accuracy and precision. This enhancement enhances passenger safety and contributes to a smoother and more efficient flow of traffic. In addition to improved object recognition, software enhancements have enabled the differentiation of objects based on their speed vectors. This capability

<table><caption>**Table 11.3** Technical improvements through AVENUE</caption><thead><tr><td>Aspect</td><td>Description of improvement</td></tr></thead><tbody><tr><td>On-demand automated Minibus service</td><td>There are some on-demand features working within the project (e.g. assignment of missions)</td></tr><tr><td>Recognition of overtaking vehicles</td><td>Not as earlier, the minibus can now detect overtaking vehicles on the road</td></tr><tr><td>Object differentiation based on speed vectors</td><td>Some software features now allow the speed vector-based differentiation of objects (and road participants)</td></tr><tr><td>V2I Communication at traffic light section</td><td>A bidirectional communication between the automated minibus and a traffic light section is possible</td></tr><tr><td>System stability</td><td>Higher operational stability and a lower system downtime rate</td></tr><tr><td>Smoothness of driving</td><td>The vehicle's driving behaviour is perceived to be more human-alike</td></tr><tr><td>Ntrip RTK correction</td><td>Better navigation and localization of the automated minibuses with the transmission of differential RTK correction data via the internet</td></tr></tbody></table>
enhances the minibuses' ability to navigate complex traffic scenarios by providing more nuanced and context-sensitive responses to their surroundings. Another noteworthy development in the AVENUE project is the establishment of bidirectional communication between minibuses and traffic light sections. This communication enables real-time coordination between minibuses and traffic signals, optimizing traffic flow and reducing congestion on roadways. Furthermore, advancements in system stability have been achieved, resulting in higher operational stability and a reduced system downtime rate. These improvements enhance the overall reliability of the minibus service, ensuring a more consistent transportation experience for passengers.

## 11.6 Conclusion and Outlook

The Vienna Convention on Road Traffic composed by the United Nations in 1968 is an international treaty aimed at increasing safety in international road traffic, primarily through uniform regulations (United Nations, 1968). As of 2021, this road transport convention has so far been ratified by 84 countries (United Nations, 2021). Since 2014, it is permitted to use automated and assistance systems (UN Economic and Social Council, 2014). This change was enacted in 2016 (United Nations, 2015). Even if it is allowed to use such automated systems, which influence the driving of vehicles, it must still be able to be switched off and controlled by the driver (UN Economic and Social Council, 2014). In terms of this convention and the related need for monitoring and possible control of these systems by the driver, the application of automated vehicles is still limited to a level 3 autonomy. This underlying research has indicated that, above all, the demonstration of safe driving in road traffic is a success-critical factor for automated driving. All experts rated this aspect as significant for convincing the authorities on the safety of automated vehicles in public transport and thus fully validating their deployment. The safety of automated driving nevertheless depends strongly on the maturity of the technologies applied to enable overall reliable navigation of the vehicle. Accordingly, it was also pointed out that public transport operators act as customers throughout the ecosystem, specifying requirements for their use cases to the automated minibuses manufacturers. Due to the limitations of the technologies used, their optimized use by the automated driving software was thus recommended. With an increasing reinforcement of the software-hardware interaction, cybersecurity issues may be of higher importance, even if no hacking incidents are known within the AVENUE project yet. It was demonstrated that there have been technical developments that have advanced AVENUE. Nevertheless, technical obstacles to the use of the automated minibuses remain: in certain situations the intervention of the safety operator is still required. Demonstrating the safety of the vehicles is highly important not only from a regulatory perspective but also potentially for public perception, which may influence the acceptance of automated minibuses. For the course of possible further similar projects, it thus remains to continuously review what technical developments are made and what obstacles exist to the full implementation of automated minibuses in public transport.
The study showed that the demonstration of safe vehicle operation is important in terms of local authority regulations. The principle of safe automated driving is closely related to the extent to which a safety driver must intervene. Due to technical limitations (e.g. sensors), there are still interruptions in operation that require the vehicle to be controlled manually. Another recommendation in relation to this challenge is the integration of further sensors, although this task would depend on the manufacturer. Despite positive developments in the localization and navigation of the automated minibuses with the Ntrip RTK correction data service, operational interruptions and system failures still occur. It is important to document and analyse these cases in detail. This may facilitate the identification of potential sources of error and the development of solutions. However, there is a risk that some solutions may be limited to the technical capabilities of the hardware. Another important aspect, which was also outlined by the experts, is the access to the automated minibuses for elderly people or people with a restricted mobility. Associated with the vehicle, it is a significant task for the PTOs and also for the manufacturer to ensure that these people are capable of accessing the transportation service without any inconveniences.

There are additional limitations to the full implementation of an on-demand transport service, mainly related to the API and fleet orchestration platform. It is important to focus on the extension of on-demand functionalities because dynamic cancellation and overwriting of started vehicle missions were not possible. In order to provide a flexible minibus service to potential passengers, the routing must be made more flexible. Here, too, the exchange of information between the vehicle manufacturer and the mobility platform provider is of enormous importance. The exact description of the API problem, starting from the booking of the vehicle by a customer and the provision of the minibus is therefore necessary. As the objective is to deploy and integrate automated minibuses in public transport, the respective service has to be provided in a way that is flexible for potential clients and that it is connectable to other transport means in the multi- and intermodal mobility environment. Due to this aspect, it is suggested focus on enabling the flexible route planning and dynamic control of the vehicle based on passenger bookings. This may include new software configurations to provide the full compatibility to the fleet orchestration platform. Ultimately, the availability and effective utilization of data will be essential in overcoming these challenges and realizing the full potential of automated minibus services by integrating them into extended systems such as mobility-as-a-service, which incorporates different travel modes in one application (Arias-Molinares & García-Palomares, 2020; Narayanan & Antoniou, 2023). The integration of automated minibuses in an intelligent transport system (ITS) would enable further advantages for the stakeholders like self-learning systems but also raise new technical challenges which will have to be addressed (see Chap. 18).

**Acknowledgements** We would like to express our sincere gratitude to Jeroen Beukers from Transports Publics Genevois, Quentin Zuttre from Keolis Lyon, Johan Larsson from Holo Copenhagen, and Daniel Kaeding from Luxembourg who contributed to the successful completion of this study. Their dedication, expertise, and commitment were instrumental in the realization of our research objectives.
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_134.png" /></figure>
Chapter 12
# Economic Impact Assessment: Local Service Costs of Automated Vehicles for Public Transport

<figure><img src="image_135.png" /></figure>

Check for
updates

Fabio Antonialli, Sylvie Mira-Bonnardel, and Julie Bulteau

**Abstract** While most of the current research on automated public transport focuses on improving operational and technical aspects, as well as tackling policy and user behavioural factors, the integration of these vehicles into public networks is mainly dependent on costs and breakeven points. Research quantifying costs and return on investment specifically in academic settings are sparse. This chapter aims to introduce an economic assessment tool, designed as a decision-making tool to support public policies on the decision of implementing innovative mobility services. The simulation contributes to the Total Cost of Mobility (TCM), a vision that accounts all alternative modes of travel and is therefore more adapted to today's fleets which are evolving towards automated electro mobility, pool management, etc. The presented assessment tool was tested with real data from pilot sites within the AVENUE project, and the results prove it to be a relevant tool to aid decision-makers on whether to adopt services with such vehicles.

**12.1 Introduction**

Technology and social acceptance are no longer the major obstacles to the deploy-
ment of automated vehicles for collective transports (Mira-Bonnardel, 2021).
However, the question of their economic impact remains. For that reason, one of the
AVENUE project's ambitions was to propose a methodology to assess the economic
feasibility of an automated-based public transport service.

F. Antonialli (✉)
PPA Business School, Paris, France

École Centrale Lyon, Écully, France
e-mail: fantonialli1@reseau-ges.fr

S. Mira-Bonnardel
École Centrale Lyon, Écully, France

J. Bulteau
Université de Versailles Saint-Quentin-en-Yvelines, Versailles, France
Based on the studies from Bösch et al. (2018), Henderson et al. (2017), and Kalakuntla (2017) as well as by applying the Total Cost of Ownership (TCO) approach as done by Ongel et al. (2019), a simulation tool for assessing the economic impact of services with Automated Mobility (AM) was developed.

The tool, named EASI-AV proposes an *Economic Assessment of Services with Intelligent Automated Vehicles* by providing the fleet dimensioning for the service, calculating the total service costs (accounting both investments (CAPEX) and operating costs (OPEX) and comparing those with a given baseline vehicle, as well as calculating the local external costs for the communities where the vehicles are to be deployed (also in a comparative manner with a baseline vehicle).

EASI-AV was designed with the objective of helping policymakers in cities and regions, Public Transport Operators and Authorities (PTOs and PTAs), and even other interested stakeholders that may wish to implement services with Automated Mobility. The tool aims to assess the economic impact of different implementation scenarios like supply-pushed or demand-pulled strategies, fixed-routes, or geo-fenced on-demand services. Thus, a comparison between the automated service and other transport modes can be drawn up.

The overall scope, methodology, and previous results obtained with EASI-AV are elaborated in detail in Antoniali et al. (2021). The simulation tool was developed and validated by the PTOs from the demonstrator cities in the AVENUE project and is freely available online on the project's website.<fnref n="1" /> In this chapter, we focus on analysing the results of the local economic simulation.

As stated by Nemoto et al. (2021), for the economic impact analysis, the Total Cost of Ownership (TCO), Total Cost of Mobility (TCM), costs and revenues analysis, and calculation of mobility externalities are methods that can support estimating the costs and economic attractiveness for users, public transport operators, transport services providers, and municipalities.

As stated by Nemoto et al. (2021), and PwC (2019), the concept of Total Cost of Mobility brings a more recent and holistic understanding of mobility, which has not been well defined until now, going beyond the fact of owning and operating a fleet of vehicles; it is a response to the changes towards current and future service-oriented mobility and presents a dedicated emphasis on the demand (passenger/citizen and trip) perspective. This change of perspective is closer to the reality of citizen mobility use case. It allows evaluating customer-/citizen-centric business strategies.

The benefits and costs of integrating Automated Mobility in a transport system can be found in Jaroudi (2021) and on part 5 of the AVENUE project deliverable D8.6.<fnref n="2" />

The chapter is structured as follows. Section 12.2 provides a literature review by presenting a brief overview of AVCT's economic impact assessment for the applicable domain. The tool, EASI-AV, is presented in Sect. 12.3. Section 12.4 presents the

<footnote n="1">¹EASI-AV web application: https://h2020-avenue.eu/avenue-economic-calculator/. Copyright by École CentraleSupélec (Université Paris-Saclay)</footnote>

<footnote n="2">²AVENUE D8.6 Economic Impact, available at: https://h2020-avenue.eu/wp-content/uploads/2023/03/20220512_D8.6-Economic-Impact_WP8_final-version_DK-not-approved.pdf.</footnote>
economic assessment for the four AVENUE demonstrator sites, and different scenarios are discussed, and in Sect. 12.5, a conclusion is derived.

## 12.2 Theoretical Framework: Automated Mobility Impact Assessment

With automated vehicles expected to be an accepted technology by 2030 (Litman, 2018), their market penetration rate is dependent on investment costs as well as operating costs. By not requiring a driver and with expected lower energy consumption due to smoother driving, AVCTs may have lower operating costs than their human-driven counterparts (Fagnant & Kockelman, 2015; Fournier et al., 2020). However, their current embedded autonomy package constitutes the major cost components—with LIDARs, sensors, cameras, processing unit, and V2X equipment ranging from around 25,000 to 30,000 dollars, not to mention that automated vehicles are generally equipped with an electric battery and powertrain, which also increases the costs and reduces the overall lifecycle of the product to currently around 5 years (Ongel et al., 2019).

On the other hand, it is expected that the prices for the automation package as well as battery prices will decrease over time and that, if necessary, batteries and other fast-moving parts will become replaceable, so that the buses could have a much longer service life. Hence automated vehicles may become cost-effective compared to conventional vehicles in the long term (Bansal & Kockelman, 2017; KPMG, 2015). Consequently, cities and PTOs/PTAs should consider the costs and benefits of implementing a public transport service using automated vehicles over traditional services, and several recent studies have sought to provide answers to these demands.

In order to investigate possible changes in the urban mobility behaviour in the cities of Berlin and Stuttgart (Germany), Fournier et al. (2020) proposed an analytical model that simulates the impacts of a shared automated electric vehicles fleet (AEV) versus private vehicles with an internal combustion engine. Their results showed that a shared AEV fleet system could reduce externalities (accident avoidance, traffic jams, free spaces, parking costs, and lifetime losses) in cities and generate cost benefits for customers.

Kalakuntla (2017) carried out a prospective comparative study of costs and benefits of Automated Mobility fleets versus traditional regular diesel buses for the city of Austin (Texas, USA) with the aim of guiding PTOs/PTAs on the feasibility of Automated Vehicles (AV). The author concluded that AVs could save PTOs/PTAs from capital and operational costs, reduce the environmental effects, and increase the quality of life of the people.

The study carried out by Henderson et al. (2017) aimed at finding useful and efficient ways to use AVs in the campus of the Ohio State University (USA); the authors conducted an analysis to compare the current fleet of traditional vehicles used on campus to the costs of purchasing and maintaining a fleet of AVs (in their
case the shuttle Olli from Local Motors). It was concluded that the automated shut-
tle exceeded the fleet of traditional vehicles in several categories. The costs and the
carbon emissions per mile (0.91 lbs) as well as the annual maintenance costs ($600/
yr) were comparatively lower. However, the automated shuttle was currently not
cost-effective due to its high initial price in contrast to traditional shuttles.

Bösch et al. (2018) carried out a substantial cost-based analysis comprising of a bot-
tom-up calculation of the cost structures (including besides the fixed costs, the overhead
costs of shared services) for different types of AVs in various operation models, such as
dynamic ride-sharing, taxi, shared vehicles fleets and AVs. The authors stated that their
methodology allows the determination of different cost components’ importance and
differentiation of vehicle automation effects on individual cost components. Their
results showed that more than half of AVs’ fleet operating costs will be service and man-
agement costs. Furthermore, they have concluded that automated driving technology
will allow taxi services and buses to be operated at substantially lower costs.

At last, the study from Ongel et al. (2019) aimed at determining the Total Cost of
Ownership (TCO) of AVs and comparing them to regular internal combustion
engine buses and minibuses. Their TCO analysis included three major cost compo-
nents: acquisition costs, operating costs, and end-of-life costs. Their simulations
have shown that, although the acquisition costs of AVs are higher than those of
conventional buses, they can reduce the TCO per passenger-km up to 75% and 60%
compared to conventional minibuses and regular buses, respectively.

Although bringing several promising and interesting results regarding the eco-
nomic feasibility of services with AVs, none of the studies proposed a holistic meth-
odology for dimensioning and assessing the economic impact of AVs services,
which could be easily applied by decision makers in the economic evaluation of AVs.

Therefore, the simulation tool EASI-AV was designed that helps to assess the
economic impact of AVs integration into public transport networks and to simulate
different scenarios by allowing the users to adjust cost variables and revenue vari-
ables. The next section explains how EASI-AV is structured and how its different
parts work.

12.3 The EASI-AV Simulation Tool

12.3.1 *Design Methodology*

The Economic Assessment of Services with Intelligent Automated Vehicles (EASI-AV) tool was developed as a support tool to assist decision-makers in cities, as well as transport operators and other organizations to estimate the economic assessment of implementing a service with AVs. EASI-AV has been developed within the European project AVENUE and in collaboration with the transport operators in charge of the collective transport networks which were responsible for the demonstrators in the four cities of the project (Copenhagen, Geneva, Lyon, and Luxembourg). Their data on the
automated service as well as on traditional human-driven services were collected in order to test the tool and the reliability of its algorithms.

## 12.3.2 EASY-AV Structure and Implementation in the AVENUE Program

The EASI-AV tool provides different types of assessments in a comparative manner (between the shuttle and different baseline transport modes), such as the total service costs (based on the TCO analysis)—including investment costs and operational costs, the local impact of externalities, as well as the global impact assessment. EASI-AV is composed of five different parts that may be carried out sequentially or independently according to the needs of the user.

### 12.3.2.1 Part 1: Service Contextualization

This part focuses on qualitatively defining the local context envisioned for the new services with AVs. Contextualizing the service helps to build more accurate scenarios and allows decision-makers to have a holistic view of the service context to be implemented. The tool helps to properly frame the territorial typology (urban, peri-urban, rural), the zoning (residential, commercial, industrial, or mixed areas), and define the public transport supply (if there is already existing public transport in the area) and the area's population density as well as surface area and extension of roads. Figure 12.1 illustrates the EASI-AV web application data entry for one of the AVENUE project's testing sites (Antonialli et al., 2021).

<figure><img src="image_136.png" /><figcaption>**Fig. 12.1** EASI-AV web application data entry—example of an AVENUE testing site. Source: prepared by the authors</figcaption></figure>
### 12.3.2.2 Part 2: Fleet Size Dimensioning

EASI-AV proposes four alternatives for the fleet size calculation (Table 12.1) that are guided by two main drivers: (1) service type (supply-push or demand-pull), and (2) road environment (fixed-routes or geofenced-on-demand). The tool allows the fleet size to be calculated for all combinations of service type/route environments. Once the category is selected, decision-makers enter data on selected cells if they work with the spreadsheet tool or ask for data collection online if they work on the web application (Antonialli et al., 2021).

As explained by Antonialli et al. (2021), each scenario prompts the algorithms to calculate both the local service costs (CAPEX and OPEX) and well as the local external costs (such as accidents, pollution, noise, and congestion). In addition, each scenario allows the simulation of different revenue models, not only based on ticketing or subsidies (as the current public transport offerings) but also innovative tariffs for on-demand with different revenue sources, such as custom commute to schools, hospitals, and private companies with different onboard services integrated into a mobility app, allowing new revenue streams, and a different economic balance.

For the scenario with option type 1 (fixed-route), the fleet size dimensioning is based on traditional fleet size calculations. Besides the usual general parameters characterizing the territory (route length, average speed, layover time, capacity, etc.) and specific parameters characterizing local mobility uses (percentage of public transport users in the area or numbers of operating hours per day), we considered some other specific for parameters as a way of leading to a finer calculation, such as the average operational speed (taking into account the idle time on each stop), as well as the battery autonomy and its charging time (which allows us to make a time differential to integrate in the calculation for how long a vehicle will be out of service to recharge). Simple algorithms compute these data and propose an optimum fleet size. The scenario with option type 2 (geofenced on-demand) is more complex since the algorithms must evaluate how many kilometres the vehicle may drive across the serviced area to comply with any user's demand for any direction at any time. Key elements of calculation in that option are the passenger waiting time (i.e. how long should a requester wait before a vehicle arrives) and the maximum distance between the requester and the vehicle at the time of the request. After computing these elements in addition to all elements considered for option 1, EASI-AV proposes an optimum fleet size (Antonialli et al., 2021).

Still according to the authors, for service type 1 (demand-pull), EASI-AV proposes calculations via the demand side, that is, for the cases where the demand for mobility

<table><caption>**Table 12.1** Service scenarios encompassed by EASI-AV; Source: adapted from Antonialli et al. (2021)</caption><thead><tr><th></th><th>Service 1: demand-pull</th><th>Service 2: supply-push</th></tr></thead><tbody><tr><th>Option 1: Fixed-route</th><td>Copenhagen (Nordhavn)<br>Luxembourg (Contern)</td><td>Lyon (Groupama Stadium)<br>Geneva (Meyrin)<br>Luxembourg (Pfaffenthal)</td></tr><tr><th>Option 2: Geofenced on-demand</th><td>Lyon (Groupama Stadium)</td><td>Geneva (Belle-Idée)</td></tr></tbody></table>
is known (i.e. areas where public transport is already in place). Three calculation scenarios are proposed depending on the degree of knowledge of data concerning the existing transport demand (the number of passengers or the expected percentage of passengers during the peak and off-peak hours, etc.). The objective is to offer a flexible, modular tool depending on the transport demand and/or the future transport service offer. It is worth noting that from an economic standpoint, incentives will certainly be different depending on the type of vehicle chosen (e.g. one large bus versus several small buses), thereby, the input demand variables for this service type proposal must always be adjusted accordingly. For service type 2 (supply-push), the tool offers calculations via supply (i.e. areas where public transport is not yet available); thus, where demand on public transport is unknown or the service will be offered as a new transport offering in a supply-pushed strategy (Antonialli et al., 2021).

With the tool being tested with data from several testing sites in the AVENUE project, the EASI-AV-simulated presented results are consistent with the field data. For instance, the fleet size simulations carried out on EASI-AV for the Groupama Stadium testing site in Lyon (KEOLIS), the Nordhavn testing site in Copenhagen (Holo), and the Ormøya testing site in Oslo (Holo) yield the same fleet size and the real number of shuttles used for the operators. Figure 12.2 illustrates the results for one of the AVENUE testing sites yielded by the EASI-AV's web application.

### 12.3.2.3 Part 3: Local Service Cost Assessment

In the scope of the Total Cost of Mobility as presented by Nemoto et al. (2021), the service cost assessment can be used as the follow-up of part 2 (fleet size dimensioning), or for cases where the fleet size is already known, it can be started by entering the current fleet size the users seek to evaluate.

For this part, information about the lifetime of the vehicles as well as the number of onboard safety drivers and off-board supervisors are requested. The former will allow for the calculation of vehicle depreciation, while the latter two will allow a better characterization of operating costs and possible economies of scale in terms of required staff.

<figure><img src="image_137.png" /><figcaption>**Fig. 12.2** EASI-AV web application fleet size results—example of an AVENUE testing site. Source: prepared by the authors</figcaption></figure>
The main internal costs are investment costs (or capital expenditures (CAPEX)) and operations expenditures (OPEX), both must be determined. Once all cost sources are registered, EASI-AV calculates useful KPIs such as the costs per passenger/km and per vehicle/km as well as other indicators. These ratios will be used afterwards for a detailed comparison between transport modes (Antonialli et al., 2021).

To help the user of the tool, based on an extensive literature review and benchmark with the PTOs, a list was created that explains the most relevant CAPEX and OPEX cost sources on a specific side document and via drop-down menus for the web application. In order to integrate economies of scale, the user can choose if the cost applies to a single vehicle or to the entire fleet (e.g. infrastructure works is CAPEX applied to the whole fleet, whereas acquisition is a per vehicle cost). In case the user does not know the exact cost values for the automated shuttle, the calculator also provides the option of using the standard costs (determined based on the average results obtained in the AVENUE project).

As shown in Fig. 12.3, with the summary results for one of the testing sites on the AVENUE project (anonymized due to confidentiality agreements), currently services with automated minibuses are not economically viable when compared to the baseline vehicle (6 meters human-driven bus). Since the fleet size service for this site was comprised of a single shuttle, the capital expenditures are 67% higher, and the operating costs are 29% higher as for CAPEX and the main cost differences between the Automated Vehicle and the baseline vehicle lie in commissioning costs (1567% higher) and in the purchase price of the vehicle (73% higher), in addition, it is worth mentioning the economies of scales that exist for the baseline vehicles due to their higher number on the roads.

As Konstantas (2021) explains, due to current regulatory limitations in some countries (such as Switzerland and Denmark) as well as to the experimental nature of the current projects of Automated Mobility, the commissioning costs for automated vehicles, besides being expensive (the extreme difference in commissioning costs depends very much on the test character and the innovativeness of the mobility approach), are imperatively carried out individually for each vehicle in the fleet, while for traditional human-operated vehicles, the legislation allows such costs to be applied to the whole fleet. As for the acquisition cost, it is evident that automated vehicles are currently more expensive (mainly due to the onboard automation technology and the electric powertrain and batteries). However, as Heineke et al. (2022), Cortright (2017), and Fagnant and Kockelman (2015) have pointed out, the expectation is that the costs of these vehicles will gradually decrease in the next years.

Regarding operating costs (OPEX), the major factor to be considered (in the current state of affairs) is the salary of onboard safety drivers. Since legislation still requires them inside AVs, this raises personnel costs, bringing this expense in line with that of traditional human-driven vehicles. Once the legislation waives the requirement for such professionals, this expense will be eliminated for automated vehicles, rendering them more cost-competitive (more details on these aspects are presented in Session 5). Furthermore, it should be noted that currently, the taxes and fees and maintenance costs are higher than the ones of
<figure><img src="image_138.png" /><figcaption>**Fig. 12.3** Service cost assessment results—example of an AVENUE testing site. Source: prepared by the authors on EASI-AV</figcaption></figure>

the baseline vehicle (73% and 296%, respectively). Finally, there is a high value of additional services (3.025% higher for AVs), which is due to the choice of the transport operator to offer the testing of additional services to the user (such as the Follow-my-Kid application of the start-up and partner of the Mobile Thinking project).

The KPIs on cost per passenger/km and cost per vehicle/km also corroborate the current economic infeasibility of the services with AVs, being 201% higher in terms
of cost per passenger/km compared to the baseline vehicle and 51% higher considering cost per vehicle/km.

### 12.3.2.4 Part 4: Local External Cost Assessment

At both local and global scales, public actions are considered in terms of sustainability. In this regard, the transport sector is no exception (Bulteau, 2016). The objective of policymakers is to reduce negative externalities of transport for the community, such as congestion, environmental pollution, and accidents. Therefore, the economic assessment takes into account externalities generated by the transport service implemented in the area, as well as at the macrocity level.

On EASI-AV, several sources of external costs for the cities are considered: congestion, accidents, air pollution (NOx and particulate matter), and noise. The monetarized values of these externalities come from the handbook of the externalities of transport (European Commission, 2019) being adjusted for inflation for the year 2020 and adapted to fit AVs. To get the results for externalities valuation, all that needs to be done is to select the country of where the shuttle will be deployed. Everything else is automatically calculated. A comparative analysis is provided between the external costs generated by the fleet size of shuttles and the chosen baseline vehicle (Antonialli et al., 2021).

It is worth noting that since this assessment is based on secondary data from the handbook of externalities of transport (which data was compiled for the year of 2016), the analysis is an approximation and only available for the countries listed in the handbook. However, the results do already provide an overview of the possible impacts (and eventual gains) of implementing AVCTs services.

Figure 12.4 exemplifies the local externalities gains for the same AVENUE testing site exemplified on Fig. 12.3. In comparison with the 6-meter internal combustion engine baseline bus, a 69% local externality cost reduction is observed in all

<table><caption>**Fig. 12.4** Local external costs assessment results—example of an AVENUE testing site. Source: prepared by the authors on EASI-AV</caption><thead><tr><th></th><th>6m ICE</th><th>Autonomous Shuttle</th><th>% Change</th><th></th><th>6m ICE</th><th>Autonomous Shuttle</th><th>% Change</th></tr></thead><tbody><tr><td>EXTERNAL COST per passenger/km</td><td>0.04 €</td><td>0.01 €</td><td>-69%</td><td>ONE WAY COST per passenger</td><td>0.09 €</td><td>0.03 €</td><td>-69%</td></tr><tr><td>EXTERNAL COST per vehicle/km</td><td>0.83 €</td><td>0.26 €</td><td>-69%</td><td>ONE WAY COST per vehicle</td><td>1.73 €</td><td>0.54 €</td><td>-69%</td></tr></tbody></table><table><thead><tr><th></th><th>6m ICE</th><th>Autonomous Shuttle</th><th>% Change</th><th></th><th>6m ICE</th><th>Autonomous Shuttle</th><th>% Change</th></tr></thead><tbody><tr><td>Vehicle DAILY cost</td><td>28.42 €</td><td>8.78 €</td><td>-69%</td><td>Fleet DAILY cost</td><td>28.42 €</td><td>8.78 €</td><td>-69%</td></tr><tr><td>Vehicle MONTHLY cost</td><td>625.23 €</td><td>193.19 €</td><td>-69%</td><td>Fleet MONTHLY cost</td><td>625.23 €</td><td>193.19 €</td><td>-69%</td></tr><tr><td>Vehicle YEARLY cost</td><td>7,502.74 €</td><td>2,318.31 €</td><td>-69%</td><td>Fleet YEARLY cost</td><td>7,502.74 €</td><td>2,318.31 €</td><td>-69%</td></tr></tbody></table>
<table><thead><tr><th rowspan="2">KPIs</th><th colspan="2">Luxembourg (Sales-Lentz)</th><th colspan="2">Copenhagen/Oslo (Holo)</th><th colspan="2">Geneva (TPG)</th><th colspan="2">Lyon (Keolis)</th><th rowspan="2">AVENUE average</th><th rowspan="2">Baseline vehicle 6m-EV (AVENUE average)</th></tr><tr><th>Pfaffenthal (2 vehicles)</th><th>Contern (2 vehicles)</th><th>Nordhavn (1 vehicle)</th><th>Ormøya (2 vehicles)</th><th>Meyrin (1 vehicle)</th><th>Groupama (2 vehicles)</th><th></th><th></th></tr></thead><tbody><tr><td>Cost per passenger/km</td><td>0.73 €</td><td>0.79 €</td><td>1.35 €</td><td>1.01 €</td><td>2.52 €</td><td>1.22 €</td><td></td><td>1.27 €</td><td></td><td>0.63 €</td></tr><tr><td>Cost per vehicle/km</td><td>11.01 €</td><td>11.78 €</td><td>20.33 €</td><td>15.21 €</td><td>37.86 €</td><td>18.29 €</td><td></td><td>19.08 €</td><td></td><td>9.48 €</td></tr></tbody></table>

NOTE: detailed figures on CAPEX and OPEX have been omitted due to confidentiality requirements.

<caption>**Fig. 12.5** Total cost of services (CAPEX and OPEX)—AVENUE demonstrator sites. Source: prepared by the authors on EASI-AV</caption>

listed KPIs. This results in significant long-term savings for local taxpayers, especially regarding accident costs (94% lower for AVs), local pollution costs (99.7% lower), and noise costs (100% reduction).

The next section details the global results obtained for each testing site of the AVENUE project for which data was provided by the PTOs. Finally, an analysis of scenarios considering service offerings without onboard safety drivers and with commissioning costs applied to the entire AVs fleet is also performed.

## 12.4 Global Impact Evaluation and Scenarios

### 12.4.1 Overall AVENUE Results: KPIs

Figure 12.5 depicts the global results for the AVENUE demonstrator sites based on the simulation outcomes from EASI-AV. The presented results are from the demonstrator sites where the operators were able to provide detailed data (results have been adjusted for inflation (December 2022)).

The variations in the CAPEX values among the operating sites differ due to individual prices and levels of investments needed mainly on feasibility studies, commissioning costs, infrastructure works, and certification and standardization for each country. Those values vary according to the specificity of each site as well as based on local legislation.

The variations seen in the OPEX values are mainly due to the costs of personnel. The average salary paid for the operators and supervisors in each country varied as well as the number of operators needed for the daily operation of a single shuttle. For Lyon and Switzerland, the reported average annual salary for the safety drivers is about 90.000,00 euros, while for Luxembourg the values are around 43.133,48 euros, for Denmark around 48.000,00 euros, and for Sweden 55.700,00 euros.

It is worth noting that the KPIs calculations (Fig. 12.5) are based on the OPEX for the service and on the maximum daily mileage that a shuttle can run, which is dependent on the route length, operating hours, and frequency of the service. Consequently, the KPIs (cost passenger/km and cost shuttle/km) vary accordingly. For instance, in the Meyrin site and in Pfaffenthal the shuttles are operated much
less than on the other sites (an average of 31.66 km/day and 43,27 km/day respectively versus the average of 72.73 km for the other four testing sites).

At the current state of affairs, it is safe to say that services with AVs are not yet cost-effective. The KPIs for all demonstrator sites, as well as the averages for the project (column in green on Fig. 12.5), have higher costs than the 6 m EV human-driven baseline vehicle<fnref n="3" /> used here to provide a global comparison to the AVENUE services (column in red on Fig. 12.5). The costs of the automated vehicles used in the project are 50.39% higher in terms of cost per passenger/km and 50.31% regarding the costs per vehicle/km, considering an average occupancy of 15 passengers. In the following subsection, different scenarios are presented to show the potential economic viability of the services once certain regulatory barriers are overcome.

## 12.4.2 Simulation of Scenarios

### 12.4.2.1 CAPEX Savings

As for possible CAPEX reductions, a possible evolution in the regulatory framework can lead to a reduction in costs for certification and standardization, feasibility studies, as well as commissioning costs. As pointed out by Konstantas (2021) feasibility studies and commissioning costs deserve special attention according to the local legislation of each country. According to the author, in Denmark, due to the lack of clear legal requirements, feasibility studies were not only time-consuming but also more cost-intensive than planned. In Switzerland, on the other hand, current legislation requires commissioning costs to be incurred for each vehicle individually and not for the fleet as a whole, leading to higher capital expenditures (particularly for the Belle Idée site, where two shuttles were used and not just one as in Meyrin). Secondly, as pointed out by Fagnant and Kockelman (2015), not only do the acquisition costs of these vehicles tend to decrease in the next years, but also their life cycle is assumed to decrease due to more modern sensors and cameras. Thus, it is possible to consider an important reduction in fleet acquisition costs.

Overall, the current average CAPEX values for the AVENUE project presented in the green column in Fig. 12.5 (both for a single AV and for the fleet) are not disproportionately higher than the average calculated for the human-driven baseline vehicle (red column in Fig. 12.5), being only 8.41% higher for a single vehicle and 21.71% higher for the total fleet.

In fact, by analysing each demonstrator site individually, (apart from Groupama in Lyon—due to its particularities that rendered the CAPEX higher than the AVENUE average), all other demonstrators already present—for a single vehicle—CAPEX values lower than the average calculated for the baseline vehicle. Therefore, even with a

<footnote n="3">³Values for the baseline vehicle were calculated based on the baseline average for the AVENUE demonstrator sites, considering a 6-meter Bolloré electric bus (https://www/bluebus.fr/bluebus-6-metres) with an average occupancy of 15 passengers (maximum capacity 30 passengers)</footnote>
potential margin for investment cost reductions, CAPEX is not the main elements that make the current offering of services with AVs unfeasible, these fall on operating costs. The following subsection describes the possible ways to reduce these costs.

### 12.4.2.2 OPEX Savings

Undoubtedly, the main competitive advantage in terms of operating costs for AVs when compared to traditional vehicles is the absence of drivers. However, the current regulatory framework in most countries does not allow services and projects without the presence of an onboard safety driver. Thus, the constant presence of this professional inside the vehicle suppresses their competitive promise in terms of cost reduction.

However, advances are being made in the legal frameworks, and in some countries, legislation is starting to allow trials of these services without the presence of a full-time onboard safety driver. In these cases, an off-board supervisor in a control room is responsible for ensuring the safety and operation of the service for the AV fleet. Within the scope of the AVENUE project, the first tests of this type began in Geneva at the Belle Idée site and were scheduled to begin at the Lyon site. In a medium to long-term horizon, considering technological and regulatory advances, one can imagine a 100% automated service (SAE level 5), where the vehicle would not require either an onboard safety driver or off-board supervisors (SAE, 2016).

Figure 12.6 presents the OPEX and KPIs results from the AVENUE project as well as simulation results for a (1) short-term scenario without an onboard safety driver and with an off-board supervisor and (2) a medium to the long-term scenario without any human intervention (either inside or outside the vehicle) for the operation of the service. It is worth noting that for both scenarios, all other operating costs were kept unchanged, meaning that further savings can still be made when

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Luxembourg (Sales-Lentz)</th><th colspan="2">Copenhagen/Oslo (Holo)</th><th colspan="2">Geneva (TPG)</th><th colspan="2">Lyon (Keolis)</th><th rowspan="2">AVENUE average</th><th rowspan="2">Baseline vehicle 6m-EV (AVENUE average)</th></tr><tr><th>Pfaffenthal (2 vehicles)</th><th>Contern (1 vehicles)</th><th>Nordhavn (1 vehicle)</th><th>Ormøya (2 vehicles)</th><th>Meyrin (1 vehicle)</th><th>Groupama (2 vehicles)</th></tr></thead><tbody><tr><td rowspan="2">AVENUE results</td><td rowspan="2">KPIs</td><td colspan="10"></td></tr><tr><td>0.73 €</td><td>0.79 €</td><td>1.35 €</td><td>1.01 €</td><td>2.52 €</td><td>1.22 €</td><td>1.27 €</td><td>0.63 €</td></tr><tr><td colspan="2"></td><td>11.01 €</td><td>11.78 €</td><td>20.33 €</td><td>15.21 €</td><td>37.86 €</td><td>18.29 €</td><td>19.08 €</td><td>9.48 €</td></tr><tr><td rowspan="2">Scenario 1</td><td rowspan="2">KPIs</td><td colspan="10"></td></tr><tr><td>0.49 €</td><td>0.79 €</td><td>1.18 €</td><td>0.63 €</td><td>1.86 €</td><td>0.82 €</td><td>0.96 €</td><td>0.63 €</td></tr><tr><td colspan="2"></td><td>7.39 €</td><td>11.78 €</td><td>17.81 €</td><td>9.22 €</td><td>27.95 €</td><td>12.37 €</td><td>14.42 €</td><td>9.48 €</td></tr><tr><td rowspan="2">Scenario 2</td><td rowspan="2">KPIs</td><td colspan="10"></td></tr><tr><td>0.41 €</td><td>0.57 €</td><td>0.84 €</td><td>0.48 €</td><td>1.20 €</td><td>0.69 €</td><td>0.70 €</td><td>0.63 €</td></tr><tr><td colspan="2"></td><td>6.19 €</td><td>8.66 €</td><td>12.75 €</td><td>7.23 €</td><td>18.04 €</td><td>10.29 €</td><td>10.53 €</td><td>9.48 €</td></tr></tbody></table>

NOTE: detailed figures on CAPEX and OPEX have been omitted due to confidentiality requirements.

<caption>**Fig. 12.6** OPEX and KPIs—simulation of scenarios Source: prepared by the authors on EASI-AV</caption>
considering future reductions in fees and charges, maintenance, additional services, advertising, etc.

Considering the average values for the AVENUE project (green column in Fig. 12.6), it is noted that for scenario 1 (with an onboard safety driver and without an off-board supervisor), there is a 24.41% reduction in the cost per passenger/km (dropping from an average value of 1.27€ to 0.96€) and a similar 24.42% reduction in the cost per vehicle/km (dropping from 19.08€ to 14.42€). However, these average values are still above the average values found for the baseline vehicle (red column in Fig. 12.6).

On the other hand, this scenario is already economically feasible for two of the six demonstration sites studied. In Pfaffenthal (Luxembourg) and Ormøya (Olso), the replacement of onboard safety drivers with off-board supervisors has resulted in lower costs per passenger/km and vehicle/km than the average values for the baseline vehicle (even without considering a reduction in the other listed operating costs). This represents a 32.87% reduction for both costs per passenger/km and vehicle/km in Pfaffenthal. In Ormøya a 37.62% reduction in costs per passenger/km and 39.38% reduction in costs per vehicle/km was recorded.

The results observed for scenario 2 (no onboard safety drivers and no off-board supervisors) bring the operational costs to competitive values when compared to the baseline vehicle. Although the overall average costs within the project are still above the values observed for the baseline vehicle (0.70€ versus 0.63€ for the cost per passenger/km, and 10.53€ versus 9.48€ for the vehicle/km cost), it is worth noting that in half of the demonstration sites studied, the service would prove to be economically competitive.

Both sites in Luxembourg show average values lower than those of the baseline vehicle. For Pfaffenthal, the cost per passenger/km would drop to 0.41€ and per vehicle/km to 6.19€, a respective reduction of 34.91% and 34.70% compared to the baseline vehicle. In Contern, the reductions would be 9.52% regarding the cost per passenger/km and 8.64% for vehicle/km. When comparing the figures to the actual results of the project, in Pfaffenthal the potential for reductions in cost per passenger/km would drop from 0.73€ to 0.41€ (a reduction of 43.83%) and from 11.01€ to 6.19€ in cost per vehicle/km (reduction of 43.77%). In Contern, the reductions would be 27.84% for the cost per passenger/km and 26.48% for the cost per vehicle/km.

The Ormøya site in Olso also provided results that would make it feasible to implement the service with AVs. With respect to the baseline vehicle, the cost per passenger/km would be 0.48€ (23.8% less than the baseline vehicle) and the cost per vehicle/km would be 7.23€ versus 9.48€ for the baseline EV (23.73% reduction). When comparing the results with the actual values obtained in the AVENUE project, the savings in terms of cost per passenger/km would be 52.47% (falling from 1.01€ to 0.48€) and 52.46% regarding the cost per vehicle/km (falling from 15.21€ to 7.23€).

It is important to emphasize the following for the three other project sites considered in the analysis: in Nordhavn (Copenhagen), Meyrin (Geneva), and Groupama Stadium (Lyon), high operating costs such as insurance, taxes and fees, maintenance, and additional services have pushed the average costs up, causing the KPIs
to yield higher values than those obtained for the baseline vehicle (0.84€ and 12.75€ for Nordhavn; 1.20€ and 18.04€ for Meyrin; and 0.69€ and 10.29€ for Groupama Stadium).

Nevertheless, when comparing the results of scenario 2 for these sites with the actual results obtained in the AVENUE project, a significant reduction in costs can also be observed. For the Nordhavn site, costs per passenger/km would fall from 1.35€ to 0.84€ (a 37.77% reduction) and from 20.33€ to 12.75€ for costs per vehicle/km (37.28% reduction). At Meyrin, the reductions would be 52.38% for the cost per passenger/km and 52.35% for the cost per vehicle/km. Finally, for Groupama Stadium the reductions would be 43.44% for costs per passenger/km and 43.73% for costs per vehicle/km.

Therefore, there is a high potential in both the short and medium to long term for the economic viability of AV services. Once technological advances (allowing reductions in vehicle prices, maintenance costs, insurance, and fees) and regulatory framework advances (eliminating the need for safety-driver and/or off-board supervisor, as well as better accommodating feasibility studies and commissioning costs) are accomplished, the potential gains for passengers, transport operators, and consequently for cities are undeniable.

The main expected results would then be an important increase in the number of passengers commuting in the automated vehicles, thus rendering the services not only viable but attractive to the general public. On the other hand, if none in-vehicle services and reliable safeguard systems are provided, privacy and security issues can lower the number of passengers willing to use the service.

## 12.5 Conclusion

The large-scale deployment of automated collective vehicles, combined with online services, user profiling, and dynamic itinerary optimization, will have a disruption effect on today's public transport. The disappearance of drivers will allow transport operators to deploy more vehicles, resulting in a scaling down of vehicles. This, in turn, will allow vehicles to divert from the predefined itineraries and start offering on-demand, door-to-door services (based on dynamic online reservations and optimization), transforming public transport into a personalized transit service.

This transformation will require a high level of investments. Anticipating the economic impact of investments is a usual task for any decision-maker or investor. Therefore, the tool was developed in collaboration with transport operators and local city governments to enable an economic assessment of the implementation of automated vehicles into their transport network and the valorisation of deployment scenarios.

With public transport being a complex ecosystem, not only transport operators, passengers, and policymakers are included but a lot of different stakeholders such as software providers, mobility platforms, vehicle manufacturers, insurance companies, telecom companies, infrastructure construction companies, maintenance
companies, and data provider companies. Each stakeholder may facilitate or hinder the deployment of automated collective transport. Therefore, they should be able to analyse scenarios from their own economic viewpoint. EASI-AV was successfully tested on the AVENUE experimentation sites in four European cities and proved to be a relevant support tool for decision-making in designing new mobility solutions.

**Acknowledgements** The authors would like to thank the AVENUE project consortium, in special CentraleSupélec and Professor Danielle Attias scientific coordinator of the AVENUE project, the Automated Mobility OEM Navya, and the transport operators Keolis, TPG, Sales-Lentz, and Holo. A special thank you also goes to the colleagues from HSPF Guy Fournier and from Siemens Michael Thalhofer for the concept of integrating externalities, TCM and incorporation revenues into account, as well as Eliane Nemoto, Inès Jaroudi, and Adrian Boos for their precious insights and collaborative work in the AVENUE project. The AVENUE project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 769033.

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_139.png" /></figure>
**Chapter 13**

**Environmental Impact Assessment:**
**Automated Minibuses for Public Transport**

<figure><img src="image_140.png" /></figure>

Check for
updates

Tobias Viere, Adrian Boos, Nicole van den Boom, Meriem Benyahya, Maher Ben Moussa, and Guy Fournier

**Abstract** This chapter examines the environmental impacts of integrating automated minibuses into public transport systems, focusing on the trade-offs between energy savings and increased energy demand due to connectivity and automation features. A comprehensive life cycle assessment (LCA) is used to analyse the potential environmental benefits and problems of automated minibuses. The results show that the environmental benefits of these vehicles depend significantly on the electricity mix for charging, passenger occupancy, and vehicle utilisation throughout their lifetime. In particular, scenarios with renewable energy sources, high passenger occupancy, and optimal vehicle utilisation show the significant potential of automated minibuses to contribute to a more sustainable transport system. This study highlights the importance of holistic considerations, including technological, operational, and infrastructural factors, in maximising the environmental benefits of automated minibuses in urban mobility. It provides a nuanced understanding of the conditions under which automated minibuses can act as a catalyst for the transition to greener public transport.

**13.1 Introduction**

The following chapter presents an environmental impact assessment concerning the deployment of automated minibuses in public transport systems. Therefore, we present a study of the potential energy demand and savings of automated driving and an environmental life cycle assessment for automated electric minibuses in two

T. Viere (✉) · A. Boos
Pforzheim University, Pforzheim, Germany
e-mail: tobias.viere@hs-pforzheim.de

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

M. Benyahya · M. B. Moussa
University of Geneva, Carouge, Switzerland
subchapters. Another subchapter summarises the results into environmental indicators, which are used for the sustainability assessment in Chap. 17 of this book.

The main findings of this chapter are:

* Predictive, adaptive, and information sharing through vehicle communication with infrastructure and other vehicles improves vehicle braking performance and consequently energy consumption. However, a highly connected vehicle means more processing required by the infrastructure, remote, or cloud servers which may outweigh the V2X sustainability.

* Based on results from the AVENUE pilots, the automated driving components for an automated minibus driving at 30 km on an 8-hour day require 82.1 Wh km⁻¹ (304.4 W x 8 h/30 km) or 15.6% of the total energy use of 520 Wh km⁻¹.

* The data transmission and energy consumption for 3GPP and 5GAA use cases were estimated for automated driving connectivity for 11 scenarios: platooning, sensor and state map sharing, remote driving, lane change, infrastructure-based perception of environment, collision avoidance, collective information sharing, see-through for passing, emergency trajectory alignment, intersection crossing, and cooperative driving.

* Significant potential in energy savings can be achieved in particular from intelligent route optimisation and velocity control.

* Data on energy saving for predictive functions are presented for selected cases based on literature. Among the functions, the eco route planning and traffic light assistant are cited for being urban scenarios that require little exchanged information between the vehicles and the infrastructure. This makes them very promising candidates for real energy savings achieved through the implementation of automated urban mobility.

* The energy efficiency for exchanging data within the automated minibuses eco-system depends on the number of connections and the advancement of the deployed technologies.

* The cooperative V2X is undoubtedly the key sustainable communication mode and plays an important role in energy demand.

* A fixed route or a mature on-demand service would have different energy consumption due to different numbers of involved servers.

The life cycle assessment (LCA) of the automated minibus shows that the automated technologies in the automated minibuses, as deployed in the AVENUE pilots, are around 5% of the total energy used. When considering the near-future use case, the study points that 59% of the automated minibuses impact stems from the use phase, while component production accounts for 39%. The use phase climate impacts are mostly due to the burning of fossil fuels to produce the electricity required for driving the automated minibus. The global warming potential for each pkm is 78 g CO2eq.

The assessment of the automated minibus based on the environmental indicators shows that at the current stage, the automated minibuses face challenges to be deployed as an environmentally friendly mode of transport. These results are confirmed by the LCA study, pointing that the automated minibus at the current
deployment does not show significant environmental benefits, but future use cases
are likely to improve substantially. In addition, the automated minibuses qualifica-
tion as environmentally friendly depends on many factors such as occupancy, vehi-
cle speed, mileage, and lifetime. Taking into consideration the perspective on the
mobility system, the automated minibuses are seen as a complementary service in
public transport. In combination with door-to-door, on-demand, and driverless ser-
vices, the automated minibuses are expected to improve and strengthen public trans-
port, hence bring benefits by reinforcing shared, multi- and intermodal mobility
as well.

13.2 Assessment of Energy Demand of CAV/AV

Automated driving technologies are likely to reduce energy demand for driving compared to traditional vehicles, e.g. by functions such as platooning and eco-driving. However, the increased demand for data transmission and processing might increase energy demand. This part elaborates on both sides, the energy demand side of automated driving technologies and the potential energy savings of automated driving. Such an assessment is crucial from an environmental perspective as energy increase or decrease effect the overall environmental performance of automated vehicles significantly. In this chapter, we would be mainly referring to automated vehicles as CAV (connected automated vehicles) instead of AV, as we aim to address various aspects of connectivity in automated driving.

13.2.1 Energy Demand of Automated Driving Technologies

Throughout the scientific literature, researchers debated the energy demand of CAV to be either greedy or efficient depending on the implemented automation units, Internet technologies, and deployed services, though they clearly agreed on the direct impact of the data exchange on the overall energy consumption. This section sheds the light on the key publications that showcased the vehicle connectivity and data transfer impact on energy consumption. It also reported the efforts from literature in translating the exchanged bytes and bits into energy units.

Noroozi et al. (2023) conducted a systematic literature review on the automation impact on energy consumption. They review recent literature focusing on how energy consumption of automated vehicles is influenced by advancements in powertrain operation and driving patterns. The papers they found were organised based on various factors affecting power consumption, but it's noted that existing studies vary greatly in their design and implementation, suggesting a need for more specific and comprehensive research to accurately benchmark the energy consumption impact of vehicle automation.
Liu, Tan, et al. (2019) provided a quantitative study on the negative effects of smart vehicles on energy consumption. The authors draw attention to the fact that automated and intelligent vehicles are equipped with computing devices, advanced sensors, controllers, and actuators, in combination with connecting communication technologies, resulting in higher energy consumption compared to conventional vehicles. The authors suggest that computing platform performance, connection strength, and radar performance are the three main factors impacting the energy consumption of CAV. Their study led to the assessment of fuel consumption per 100 km for different levels of automation—primary, intermediate, and advanced intelligence (corresponding to SAE levels 3, 4, and 5 accordingly)—and the identification of different factors that potentially influence vehicle's consumption costs. The study has shown an increase in energy consumption depending on the level of intelligence with 0.78 L/100 km in primary intelligent vehicles, 1.58 L/100 km in intermediate intelligent vehicles, and 1.86 L/100 km in advanced intelligent vehicles.

Chen et al. (2021) explore how vehicle automation affects fuel consumption for electric and gasoline powered vehicles with automation levels 0, 2, and 5 across different scenarios, by analysing design changes and performance impacts. Results suggest energy savings of 4–8% in an optimistic level 5 scenario and a 10–15% increase in fuel consumption in a pessimistic scenario. It also notes that power demand varies between urban and highway driving, with inertial power dominating in urban conditions and aerodynamic power in highway conditions.

Song et al. (2023) analyse the impacts of level 2 automation on traffic efficiency and energy consumption of expressways, considering travel time, road capacity, and energy consumption. The study proposes a benefit evaluation framework and uses microscopic traffic simulation software for experiments. Different market penetration rates and traffic flow statuses are considered, along with dedicated lanes for CAVs. Results show that they generally save travel time and reduce energy consumption per vehicle, with positive economic benefits increasing with larger market penetration rates.

Gawron et al. (2018) present a life cycle assessment (LCA) of CAV sensing and computing hardware with SAE level 4 of automation exploring the potential energy and greenhouse gas (GHG) emission impacts of CAV based on six scenarios. Three of the scenarios simulate sensing and computing hardware configurations of Tesla Model S, Ford Fusion (AV test vehicles), and Waymo's Chrysler Pacifica respectively integrated into an internal combustion engine vehicle (ICEV), and the other three scenarios simulate the hardware configuration on a battery electric vehicle (BEV). They reported that the additional hardware resulted in an increase of 3–20% of energy consumption compared to conventional vehicles. However, when considering the automated driving functions (e.g. eco-driving, platooning, and intersection connectivity) facilitated by the additional hardware, the net result is up to 9% of energy (and emission) reduction based on the Tesla and Ford hardware configuration. The authors claim that data transmission is one of the four factors contributing to an increase of energy consumption. Their research studied data transmission over 4G wireless networks, which was estimated to 1.4 MB/mile and to a consumption of 1.25 MJ/GB.
Figure 13.1 depicts Gawron et al. (2018) life cycle energy estimation for a medium CAV.

Pihkola et al. (2018) evaluated the environmental impact of mobile access networks and sustainability of services within the IoT (Internet of Things) ecosystem using the LCA methodology. In their study, the authors constructed a trend of kWh per transferred gigabyte where they linked the network electricity consumption to the transferred data within the network. However, their computations were limited to the 4G mobile network consumption in Finland that can be extended to any IoT model.

Greenblatt and Shaheen (2015) focused their research on environmental impacts of CAV's on-demand services, which reduce the vehicle ownership, the number of households owning a car, and the vehicle miles (kilometres) travelled.

<figure><img src="image_141.png" /><figcaption>**Fig. 13.1** Medium CAV life cycle energy consumption (reprinted (adapted) with permission from (Gawron, James H./Keoleian, Gregory A./Kleine, Robert D. de/Wallington, Timothy J./Kim, Hyung Chul (2018). Life Cycle Assessment of Connected and Automated Vehicles: Sensing and Computing Subsystem and Vehicle Level Effects. Environmental science & technology 52 (5), 3249–3256. https://doi.org/10.1021/acs.est.7b04576). Copyright {2024} American Chemical Society</figcaption></figure>
Based on some environmental background data from Gawron et al. (2018) and Baxter et al. (2018), we calculated the specific energy usage of fully automated minibuses used within AVENUE. The vehicle components required for automated driving are of particular interest and listed in Table 13.1. For each component, reference technologies and nominal power figures have been derived from the component manufacturer's information. In total, automated driving components in automated minibuses demand about 300 W. According to Gawron et al. (2018), the additional power required for a medium-sized, automated vehicle sums up to 240 W, while Baxter et al. (2018) state that 200 W is caused by the sensor layout for a mid-sized vehicle. The higher value of this study might be explained by a more detailed list of components in comparison to the studies by Gawron et al. (2018) and Baxter et al. (2018), which focus on primary hardware technology, such as sensors, radars, cameras, LiDARS, computers, and location detection.

In the current trial mode of the AVENUE project, the distance-weighted average speed of all sites equals 11.4 km/h (this calculation is based on Table 13.4). Assuming that all automated components run at full nominal power, the energy demand for 11.4 km of driving is 304.4 Wh, which equals 26.7 Wh/km. The distance-weighted average of the trial site's energy demand is 554 Wh/km. Hence only 4.8% of total energy demand is caused by the use of components required for automated driving.

<table><caption>**Table 13.1** Nominal power of automated driving components installed in one automated minibus</caption><thead><tr><td>Automated driving component</td><td>Number of components</td><td>Nominal power (W)</td></tr></thead><tbody><tr><td>180° Mono-Layer LiDARS</td><td>6</td><td>48.0</td></tr><tr><td>360° Multi-Layer LiDARS</td><td>2</td><td>24.0</td></tr><tr><td>Computer</td><td>2</td><td>160.0</td></tr><tr><td>Module GNSS</td><td>1</td><td>5.6</td></tr><tr><td>Inertial Unit</td><td>1</td><td>0.2</td></tr><tr><td>World Shuttle Router</td><td>1</td><td>25.5</td></tr><tr><td>Front/Rear Cameras</td><td>4</td><td>4.0</td></tr><tr><td>Wheel Encoder</td><td>4</td><td>0.6</td></tr><tr><td>3G & Ethernet Router</td><td>2</td><td>12.0</td></tr><tr><td>15" Touchscreen</td><td>1</td><td>15.0</td></tr><tr><td>Steering Encoder</td><td>2</td><td>1.2</td></tr><tr><td>Radio Module GNSS</td><td>1</td><td>0.2</td></tr><tr><td>4G Antenna</td><td>1</td><td>5.0</td></tr><tr><td>GPS Antenna</td><td>2</td><td>3.2</td></tr><tr><td>Total power consumption (watt)</td><td></td><td>304.4</td></tr></tbody></table>
### 13.2.2 Energy Demand of CAV Connectivity

To compute CAV's energy consumption, it is important to consider the connection operations and strength. Such calculations depend on the connection hardware and its related power and time, the vehicle automation level, and the amount of exchanged data (Liu, Tan, et al., 2019). The following subsections present an in-depth classification of the different sources of consumption related to the vehicle data exchange. It addresses the energy consumption of the vehicle communication to external servers or devices, including the vehicle communication to other vehicles (V2V), to the infrastructure (V2I), to the cloud (V2C), to pedestrians (V2P), and to the grid (V2G).

### 13.2.3 CAV Connectivity Technologies

The CAV's connectivity is built through multiple channels (El-Rewini et al., 2020): radio (AM/FM/DAB/RFID), Wi-Fi (IEEE 802.11), Bluetooth, cellular (3/4/5G), and bidirectional communication (IEEE 802.11p, DSRC, WAVE), or using IoT networks (IEEE 802.15.4, Zigbee). With the presence of wireless connections, virtual ad hoc networks (VANET) can be spontaneously created among CAVs, leading to V2V communication. With the increase of modern concepts, infrastructure (V2I) and additional devices (V2X) are required to assist the VANETs for data storage and data transmission for long distances (El-Rewini et al., 2020; Lee & Atkison, 2021). V2X also compasses cloud (V2C) and grid (V2G) communication in addition to any further devices or peripherals interacting with the vehicle such as smartphones (V2P), car keys or Bluetooth devices (Domínguez et al., 2019). Being hyper-connected by nature, CAVs sustainability embeds the inherited energy consumption of data transfer technologies through their communication networks. Furthermore, the V2X network topology requires further processing either at the RSU (roadside unit) or at a remote server, which would cost additional computation resources, cause delays, and hence increase the energy consumption (Belogaev et al., 2020).

In academic literature, multiple approximations and estimations are used to quantify wireless cellular networks' energy consumption. In 2020, the 4G energy consumption was assessed to be around 0.1 kWh/GB (Andrae & Edler, 2015; El-Rewini et al., 2020; Pihkola et al., 2018), including the network, data centre computations, and data storage. Masoudi et al. (2019) added that 5G networks promise higher efficiencies (up 1 Mbit/J) to the energy consumption within the IoT ecosystem. Further research recommends new IoT technologies such as Zigbee for efficient energy consumption (Gheorghiu & Minea, 2016).
To improve the vehicular energy consumption related to data transmission, researchers have studied some protocols for higher energy efficiency. Pihkola et al. (2018) highlight that new efficiency measures that have been deployed within the last decade lower the energy consumption of Internet data transmission to 0.1 kWh/GB in 2020 instead of 12.35 kWh/GB in 2010. Dong et al. (2016) proposed an optimum cluster management method to reduce the V2V transmission power while using DSRC and LTE. Passafiume et al. (2020) proposed a battery-less transponder plugged to an RSU supporting the V2I communication.

### 13.2.4 Data Transmissions of CAV

In the last decades, various consortia have been active in defining and studying various V2X data transmission technologies and protocols and their application in real-world use cases of automated driving with 3GPP and 5GAA alliances have been the most active in this domain. An early 3GPP report (3GPP, 2015) defined data transmission use cases using 4G-based long-term evolution-vehicle (LTE-V) and 3GPP (2018), 3GPP (2019), as well as 5GAA (2020) and defined data transmission use cases using 5G based C-V2X technologies. More advanced real-world scenarios with real traffic situations are being evaluated in various EU-funded telecommunication projects such as METIS 2020 and METIS 2020-II and V2X projects such as 5GCAR, 5GCroCo, and 5G CARMEN. It is not clear, though, whether these projects measure data transmission levels and whether such measures are accessible. Furthermore, simulation platforms such as Fraunhofer's Simulation Platform for Cellular V2X Fraunhofer IIS (2021) may also be useful as tools to collect data about real-world traffic situations.

Based on 3GPP and 5GAA use cases and data and the summarisation of the use cases as conducted by Kanavos et al. (2021), we calculate the energy consumption for V2X for various automated driving functions. We employed the 4G energy consumption measure of 0.1 kWh/GB (Andrae & Edler, 2015; El-Rewini et al., 2020; Pihkola et al., 2018) to calculate the average energy usage of automated driving connectivity for each of the eleven scenarios.

Table 13.2 presents the estimated data transmission rates for each use case based on 5GAA and 3GPP estimations along with our calculated energy consumption for each use case. Kanavos et al. (2021) point out that 3GPP data transmission estimations are on the higher level to prepare the telecom networks to support extreme cases of each use case, while 5GAA has more modest values based on more efficient implementation of the services. We believe more in the modest values of 5GAA as we believe that every market-conscious car manufacturer would strive to lower their vehicle's energy consumption and is more inclined to develop energy-efficient implementation of their automated driving services as long as it does not affect the driving security.
<table><caption>**Table 13.2** Data transmission and energy consumption for different automated functions</caption><thead><tr><th rowspan="2">UC</th><th rowspan="2">Title</th><th colspan="2">Data transmission</th><th colspan="2">Energy consumption</th></tr><tr><th>5GAA</th><th>3GPP</th><th>5GAA</th><th>3GPP</th></tr></thead><tbody><tr><td>1</td><td>Platooning</td><td>8–48 kbps</td><td>1.5–325 kbps</td><td>0.0001–0.0006 W</td><td>0.00002–0.004 W</td></tr><tr><td>2</td><td>Sensor and state map sharing</td><td>4–47 mbps</td><td>25 mbps</td><td>0.05–0.59 W</td><td>0.31 W</td></tr><tr><td>3</td><td>Remote driving</td><td>400 kbps–36 mbps</td><td>1–20 mbps</td><td>0.005–0.45 W</td><td>0.013–0.25 W</td></tr><tr><td>4</td><td>Lane change</td><td>120 kbps</td><td>–</td><td>0.0015 W</td><td>–</td></tr><tr><td>5</td><td>Infrastructure-based perception of environment</td><td>4–155 mbps</td><td>1 Gbps</td><td>0.05–1.9 W</td><td>12.5 W</td></tr><tr><td>6</td><td>Collision avoidance</td><td>10 mbps</td><td>–</td><td>0.125 W</td><td>–</td></tr><tr><td>7</td><td>Collective information sharing</td><td>120 kbps</td><td>50 mbps</td><td>0.0015 W</td><td>0.625 W</td></tr><tr><td>8</td><td>See through for passing</td><td>8 mbps</td><td>10–700 mbps</td><td>0.1 W</td><td>0.125–8.75 W</td></tr><tr><td>9</td><td>Emergency trajectory alignment</td><td>48 kbps</td><td>30 mbps</td><td>0.0006 W</td><td>0.375 W</td></tr><tr><td>10</td><td>Intersection crossing</td><td>8–25 kbps</td><td>50 mbps</td><td>0.0001 W</td><td>0.625 W</td></tr><tr><td>11</td><td>Cooperative driving</td><td>–</td><td>384 kbps</td><td>–</td><td>0.0048 W</td></tr></tbody></table>

### 13.2.5 Potential Energy Savings Through Predictive Driving Functions

Automated and connected driving functions do not only control the perception, decision-making, and driving command execution to move the vehicle in a safe and convenient way, but they also enable energy savings by optimising the driving route selection, motion planning, and powertrain operation. This is an important aspect when considering the connectivity options to be implemented since a few additional data provided by the infrastructure plus a few megabytes of additional bandwidth needed might pay off significantly in terms of energy saved.

Hu et al. (2017) and Connor et al. (2021) discussed the direct correlation between the vehicle connectivity, velocity, and the battery consumption and their impacts to the environment. The authors studied real-world driving scenarios for electric buses using V2I and V2V technologies. According to their findings, the V2I and V2V communications provide energy savings that are up to 27% of battery cost reduction. Bo et al. (2019) also asserted the beneficial impact of V2I to have an optimal energy control. The US Department of Energy, through the NREL study (Stephens, 2016), reported 2–6% fuel savings by adopting the V2I smart intersections.

The predictive functions typically combine models of the vehicle and its powertrain with external data such as the upcoming driving route characteristics and traffic conditions to predictively control the vehicle. Examples of such functions are
provided in Table 13.3 below, including published data on corresponding energy savings. Considering the literature for energy-saving potentials indicated in Table 13.3, it is noteworthy that:

<table><caption>**Table 13.3** Predictive automated driving (AD) functions and their energy-saving potential</caption><thead><tr><td>Function</td><td>Description</td><td>Main application area</td><td>Potential energy savings</td><td>Source</td><td>Required infrastructure or data</td></tr></thead><tbody><tr><td>Eco route planning</td><td>Identifies routes with lowest predicted energy consumption based on upcoming routes and traffic conditions, optionally including charging point selection</td><td>Routes with multiple paths and varying traffic conditions</td><td>Average 12.5% Up to 48%</td><td>(Fiori et al., 2018; Kubicka et al., 2016)</td><td>Communication with off-board digital maps including route topography, road network, and live traffic speed data provided by vehicle fleet</td></tr><tr><td>Traffic light assistant (TLA) Or Green light optimal speed advisory (GLOSA)</td><td>Predictively adjusts velocity to reach upcoming traffic lights at the start of their expected green phases</td><td>City driving with traffic lights</td><td>Average 23%–36%</td><td>(EU Horizon 2020 EVC1000, 2018)</td><td>V2I communication for upcoming signal phase and timing (SPAT) information, e.g. provided by traffic light roadside units</td></tr><tr><td>Predictive adaptive cruise control</td><td>Predictively adjust velocity to maintain appropriate headway distances to a preceding target vehicle</td><td>Motorways driving with multiple connected vehicles</td><td>Average 13%–15%</td><td>(EU Horizon 2020 EVC1000, 2018)</td><td>Onboard sensors for target object and motion detection, optionally V2V</td></tr><tr><td>Platooning</td><td>Cooperatively maintains efficient headway distances between multiple connected vehicles reducing accelerations and aerodynamic drag</td><td>Motorway driving with multiple connected vehicles</td><td>Average 4%<sup>a</sup></td><td>(Bichiou & Rakha, 2020) (Hussein & Rakha, 2020)</td><td>V2V with low latency</td></tr><tr><td>Predictive cruise control or eco-driving</td><td>Predictively adjust velocity based on upcoming hills, speed limits, and/or curves, including efficient gears election in case of multi-ratio gearboxes</td><td>Hilly motorways without traffic ahead</td><td>Average 5%<sup>b</sup></td><td>(Volvo, 2021)</td><td>Onboard navigation system maps or electronic horizon system with cloud communication for time-varying data such as dynamic speed limits</td></tr></tbody></table>
<table><caption>**Table 13.3** (continued)</caption><thead><tr><th>Function</th><th>Description</th><th>Main application area</th><th>Potential energy savings</th><th>Source</th><th>Required infrastructure or data</th></tr></thead><tbody><tr><td>Predictive thermal management</td><td>Predictively adapts thermal management of battery, e-motor, and inverter using future velocity and ambient conditions</td><td>Driving routes with cold or hot ambient conditions</td><td>Up to 11%<sup>c</sup></td><td>(Auer et al., 2015)</td><td>Communication with off-board digital maps including route topography and traffic speed data for the driven route</td></tr><tr><td>Predictive hybrid and fuel cell control</td><td>Predictively plans when to charge or discharge the battery over the route, as well as the power distribution between powertrain components (ICE, electric motor, fuel cell)</td><td>Hybrid, range extender, and fuel cell vehicles</td><td>Up to 5%</td><td>(Huss et al., 2021)</td><td>Communication with off-board digital maps including route topography and traffic speed data for the driven route</td></tr></tbody></table>

* The achievable energy savings are generally heavily dependent on the defined vehicle and use cases, resulting in wide ranges of savings typically being published for similar functions by different authors.

* The energy savings also strongly depend on the particular baseline to which they are calculated, which often consists of different types of human drivers or non-predictive control algorithms.

Nevertheless, the available published results demonstrate that a significant potential in energy savings can be achieved in particular from intelligent route optimization and velocity control.

### 13.2.6 Discussion and Implications for Environmental Impact Assessment

The nexus of data processing and exchange within the automated driving landscape raises challenges to consider while assessing CAV's sustainability. To this end, the energy consumption related to CAV's data transfer depends on wireless technologies, the cooperative communication modes, and the implemented services. Automated minibuses may support different types of internet connections

<footnote><sup>a</sup>Results for conventional (ICE-driven) car at 100 km/h cruising speed</footnote>

<footnote><sup>b</sup>Results for conventional (ICE-driven) commercial vehicles, smaller savings expected for light vehicles with electric recuperation potential</footnote>

<footnote><sup>c</sup>Results assuming full preview of route velocity available and excluding energy consumption required for component preconditioning, i.e. energy provided by the electrical grid before driving</footnote>
which result in large differences in energy consumption. The driverless wireless network can vary from 4G and 5G to DSRC, which definitely impacts the amount of exchanged data and hence the vehicle energy consumption (Masoudi et al. (2019). Cooperative V2X is expected to be the favourable communication mode with regard to energy use, according to Bo et al. (2019) and Stephens (2016). Predictive, adaptive information sharing provided through vehicle communication with infrastructure and other vehicles improved vehicle's braking performance and consequently its energy consumption. However, a highly connected vehicle also requires more processing within its infrastructure, remote, or cloud servers. This may outweigh the V2X sustainability. A fixed route or a mature on-demand service would not have comparable energy consumption as the number of involved servers and processing will not be proportionate (Greenblatt & Shaheen, 2015). As with every exchanged data within the automated minibus ecosystem, the energy efficiency can fall over to either high or low energy demand depending on the number of connections and the advancement of the deployed technologies.

Although the reported potential energy savings through predictive driving functions differ between studies, it seems evident that the savings are likely to counterbalance and even overcompensate the energy costs associated with the communication modules. It is 'common sense' in the European research community that it will be impossible to implement large-scale automated urban mobility in a safe way without infrastructure support; both in-vehicle and infrastructure communication equipment will already be there regardless of the use of predictive functionalities. Consequently, the additional cost for employing predictive functionalities is merely the additional bandwidth used by the additional data which need to be transmitted. Especially the first two functions mentioned in Table 13.3, eco route planning and traffic light assistant, apply to urban scenarios and require only a little information to be exchanged between the vehicles and the infrastructure, which makes them very promising candidates for real energy savings achieved through the implementation of automated urban mobility.

## 13.3 LCA (Life Cycle Assessment) Model

This part summarises the final results of a life cycle assessment (LCA) study on automated minibuses, which was also published as an analysis in Transportation Research Part D (Huber et al., 2022).

The LCA study investigated the environmental impacts of automated minibuses to be integrated into the public transport of cities, guided by the following research questions: (1) Which environmental impacts are associated with the operation of an automated minibus? (2) What are the main drivers of these impacts, and how can these be reduced? (3) What conclusions can be drawn from these findings for the role of automated minibuses in future public transportation systems?
### 13.3.1 Goal and Scope of the LCA Study

The common functional unit of a passenger kilometre (pkm) enables the comparison between automated minibuses and other modes of transport.

The automated minibus under investigation is 4.75 m long, 2.11 m wide, and 2.65 m high, weighs 2400 kg, and can carry 15 passengers (11 seated and 4 standing) with a maximum speed of 25 km/h.

The automated minibuses operation involved fixed routes for public transport, with trials on on-demand, door-to-door, and pooling options for passengers to request the vehicle to come to a designated pick-up location through the use of a mobile application (Navya, 2018). The automated minibuses are intended for use in a public transport system, not as a replacement for individual vehicles.

The assessment adopts a cradle-to-grave approach, encompassing the main life cycle phases recommended by Duce et al. (2013), namely, component production, vehicle assembly, use, and end-of-life treatment. The production of components has been divided into three categories: battery manufacturing, the production of automated driving components, and the production of other bus components. In order to accurately assess the environmental impact of these components throughout their life cycle, it is necessary to account for the material and energy inputs and outputs at each stage of the LCA. The following six impact categories were employed: acidification, climate change, eutrophication, ozone depletion, photochemical ozone formation, and resource depletion. A control calculation showed that the five remaining environmental impact categories presented similar results in terms of their impact.

### 13.3.2 Life Cycle Inventory and Data Collection

The life cycle inventory encompasses all environmental impacts associated with the system under investigation, including the inflow and outflow of materials and energy. A preliminary generic automated minibus model has been constructed using existing literature data (Gawron et al., 2018; Hawkins et al., 2013; Majeau-Bettez et al., 2011). This model was then refined by incorporating primary data obtained from the automated minibus manufacturer and public transport operators involved in the AVENUE project.

The primary data for this study were obtained from the demonstrator sites of the AVENUE project, where an automated minibus manufacturer provided information on vehicle components (such as weight, functions, and nominal power) and transport operators provided data on the use of these vehicles in public transportation. The data collection took place between 2019 and early 2021 in an iterative manner.

The sources of information regarding component production, including batteries and automated driving components, are Majeau-Bettez et al. (2011), Hawkins et al. (2013), Gawron et al. (2018), and Moreno Ruiz et al. (2020). The manufacturer supplied all the components and their respective weights for the vehicles in this study. The total weight of all the automated minibus components used is over 99% of the total automated minibus weight, which meets the requirements of standard
LCA. The assembly of these components was based on data from Majeau-Bettez et al. (2011), Hawkins et al. (2013), and Gawron et al. (2018) which represents industry-scale assembly of electric passenger cars. Additional secondary data was sourced from well-known LCA databases, chiefly ecoinvent 3.7 (Moreno Ruiz et al., 2020). A comprehensive overview of all components and materials required for vehicle assembly, along with their life cycle inventories (LCI) and details on primary and secondary sources, is provided in the study's supplementary material.

Table 13.4 presents data on the use phase of automated minibus service on fixed-route buses in five trial sites from September 2018 to January 2021. The data reveals differences in the average speed, expected annual mileage per shuttle, average

<table><caption>**Table 13.4** Automated minibus use phase data</caption><thead><tr><th>Site</th><th>Lyon, Groupama (France)</th><th>Contern (Luxembourg)</th><th>Luxembourg, Pfaffenthal (Luxembourg)</th><th>Copenhagen Nordhavn (Denmark)</th><th>Oslo, Ormoya (Norway)</th></tr></thead><tbody><tr><td>Data collection period</td><td>November 2019–January 2021</td><td>September 2018–January 2021</td><td>September 2018–January 2021</td><td>September 2020–January 2021</td><td>December 2019–January 2021</td></tr><tr><td>Route length [km]</td><td>1.3</td><td>2.2</td><td>1.2</td><td>1.3</td><td>1.6</td></tr><tr><td>Average driving speed [km/h]</td><td>10</td><td>15</td><td>17</td><td>8</td><td>10</td></tr><tr><td>Total mileage during data collection [km]</td><td>12,492</td><td>1900</td><td>9000</td><td>2000</td><td>23,000</td></tr><tr><td>Annual mileage during data collection [km]</td><td>9994</td><td>786</td><td>3724</td><td>4800</td><td>19,714</td></tr><tr><td>Total passengers</td><td>5545</td><td>650</td><td>25,060</td><td>1300</td><td>6600</td></tr><tr><td>Average passenger trip length [km]</td><td>1.3</td><td>2.2</td><td>1.0</td><td>0.4</td><td>0.8</td></tr><tr><td>Average vehicle occupancy</td><td>0.6</td><td>0.8</td><td>2.8</td><td>0.3</td><td>0.2</td></tr><tr><td>Average energy demand [Wh/km]</td><td>480</td><td>780</td><td>510</td><td>590</td><td>590</td></tr><tr><td>Minimum energy demand [Wh/km]</td><td>No data</td><td>480</td><td>350</td><td>300</td><td>300</td></tr></tbody></table>
vehicle occupancy, and average and minimum energy demand. It should be noted
that the extremely low average occupancy is due to the experimental nature of the
trial sites, where vehicles are also used for functional and technical testing purposes.

Umberto® software was utilised to create a product life cycle model and analyse
the results. The comprehensive automated minibus LCA model comprises 198 pro-
cesses and 42 subnets across 4 hierarchical levels. A portion of the overall model is
depicted in Fig. 13.2.

13.3.3 Scenario Setting

Using primary data gathered from trial sites, we have generated a near-future use
case and worst- and best-case values for scenario analysis. These values can also be
combined to form an ideal use case. The relevant parameters for these scenarios
include the automated minibuses expected lifetime, annual mileage, average pas-
senger occupancy, energy demand, energy source, and the used battery LCA data.
The parameter settings are described in detail and summarised in Table 13.5.

According to the manufacturer and transport operators involved in this study, the
battery lifetime can be used as a proxy for the overall automated minibus lifetime.
If one charging process occurs per day and the automated minibus operates for
5 days per week, the estimated battery lifetime is 7.7 years. This has been rounded
up to 7 years to account for probable losses and reduced efficiency as the battery
ages (Hadjipaschalis et al., 2009; Oliveira et al., 2015). The high cost of batteries for
electric vehicles has led to the belief that the lifespan of an automated minibus is the
same as the lifespan of its battery, although it is acknowledged that the rapid
advancement of automated minibus technology may result in obsolescence and
decommissioning before the end of the battery's lifespan. Conversely, some studies
on batteries for electric vehicles suggest longer lifetimes, such as 10 years (Deng
et al., 2017). The average lifetime of 7 years is obtained through scenario analysis,
which ranges from 3 to 10 years in some cases.

The near-future use case is based on an annual mileage of 20,000 km, while the
scenario analysis considers a range of mileages from 5000 km (which is roughly the
average of other trial sites in Table 13.5) to 36,500 km, assuming a daily operating
distance of 100 km.

The expected average occupancy for the near-future use case is five passengers, which is above the current trial data but aligns with transport operators' economic feasibility expectations. The scenario analysis considers a worst-case value of one passenger on average and a best-case value of ten passengers on average.

The energy demand for the near-future use case is 554 Wh/km, which is the dis-
tance-weighted average of the trial site's typical energy consumption. This is within
the manufacturer's specifications of 520 Wh/km, which was measured with one per-
son on board, traveling at an average speed of 6.6 km/h, and an outside temperature of
30 degrees Celsius, while the vehicle's interior was cooled down to 16 °C. The energy
demand for the autonomous mobility system encompasses all automated components,
<figure><img src="image_142.png" /><figcaption>**Fig. 13.2** Automated minibus life cycle model main model with subnets for battery production (a), production of all further components (b), and production of automated components (c). Sankey diagrams depict global warming potential in g CO2eq per pkm for the automated minibus near-future use case. In the Petri-net-based material flow network approach underlying the Umberto LCA software, blue squares represent processes or subnetworks, and circles represent input points (green), output points (red), and connection points (yellow). All values have been rounded to one digit</figcaption></figure>
<table><caption>**Table 13.5** Parameter setting for near-future use case and scenario analysis</caption><thead><tr><th>Parameters</th><th>Near-future use case</th><th>Worst case</th><th>Best case</th></tr></thead><tbody><tr><td>Expected lifetime [years]</td><td>7</td><td>3</td><td>10</td></tr><tr><td>Annual mileage [km]</td><td>20,000</td><td>5000</td><td>36,500</td></tr><tr><td>Average passenger occupancy</td><td>5</td><td>1</td><td>10</td></tr><tr><td>Energy demand [Wh/km]</td><td>554</td><td>780</td><td>332</td></tr><tr><td>Energy source</td><td>Electricity mix Europe</td><td>Fossil energy mix (using Poland as approximation)</td><td>100% renewable energy mix (using Norway as approximation)</td></tr></tbody></table>

passenger interaction components, and the electric driving components. Given that various factors such as speed, temperature, weight, and others can impact automated minibus energy consumption, it is crucial to conduct scenario analysis. The distance-weighted average of the minimum energy demand at the trial site (332 Wh/km) represents the best-case scenario, while the highest average energy demand at a trial site (780 Wh/km) represents the worst-case scenario for scenario analysis.

For the near-future use case, we have assumed a European electricity mix with 418 grams of CO2 equivalent per kilowatt-hour (kWh) (ecoinvent 3.7 databases with low voltage electricity market datasets for Europe, Poland, and Norway). In contrast, the worst-case scenario for scenario analysis is a mostly fossil electricity mix with 1037 grams of CO2 equivalent per kWh, while the best-case scenario is an almost entirely renewable electricity mix with 23 grams of CO2 equivalent per kWh. Battery production has been modelled using detailed data from the literature.

## 13.4 Results

This section will provide an analysis of the environmental impacts of additive manufacturing, as well as the results of scenario simulations and assessments of the automated components. Additionally, a comparison of automated minibuses with other modes of transportation will be presented to provide a more comprehensive understanding of the findings.

### 13.4.1 Life Cycle Impacts of Automated Minibuses

Table 13.6 displays the environmental impact per passenger kilometre for the selected impact categories, disaggregated into the life cycle phase components of production (separated into battery, automated, and all other bus components), vehicle assembly, use, and end of life.
<table><caption>**Table 13.6** Environmental impacts for 1 pkm of automated minibuses in a near-future use case and in comparison to an ideal use case; climate change (global warming potential 100 years) measured in g CO₂eq (carbon dioxide equivalents); acidification in mol H + eq (proton equivalents); eutrophication in kg Peq (phosphorous equivalents), ozone depletion in kg CFC-11 eq (trichlorofluoromethane equivalents), photochemical ozone formation (POCP) in NMVOCeq (non-methane volatile organic compound equivalents), resource depletion in kg Sbeq (antimony equivalents)</caption><thead><tr><th></th><th>Climate change<br>(g CO<sub>2</sub>eq)</th><th>Acidification<br>(mol H + eq)</th><th>Freshwater eutrophication<br>(kg Peq)</th><th>Ozone layer depletion<br>(kg CFC-11 eq)</th><th>Photochemical<br>ozone creation<br>(kg NMVOCeq)</th><th>Resource depletion<br>(kg Sbeq)</th></tr></thead><tbody><tr><td>Automated<br>driving<br>components</td><td>1.25</td><td>2%</td><td>9.11E-06</td><td>2%</td><td>1.08E-06</td><td>1%</td><td>7.30E-11</td><td>0%</td><td>4.96E-06</td><td>2%</td><td>2.32E-07</td><td>2%</td></tr><tr><td>Battery<br>production</td><td>11.00</td><td>14%</td><td>6.99E-05</td><td>15%</td><td>9.59E-06</td><td>9%</td><td>4.39E-08</td><td>92%</td><td>3.43E-05</td><td>14%</td><td>1.44E-06</td><td>11%</td></tr><tr><td>Further bus<br>components</td><td>18.00</td><td>23%</td><td>1.30E-04</td><td>28%</td><td>4.68E-05</td><td>45%</td><td>1.27E-09</td><td>3%</td><td>9.32E-05</td><td>39%</td><td>1.06E-05</td><td>83%</td></tr><tr><td>Component<br>production</td><td>30.30</td><td>39%</td><td>2.09E-04</td><td>45%</td><td>5.75E-05</td><td>55%</td><td>4.53E-08</td><td>95%</td><td>1.32E-04</td><td>55%</td><td>1.23E-05</td><td>97%</td></tr><tr><td>Final assembly</td><td>0.19</td><td>0%</td><td>8.51E-07</td><td>0%</td><td>8.03E-08</td><td>0%</td><td>9.22E-12</td><td>0%</td><td>4.56E-07</td><td>0%</td><td>4.65E-10</td><td>0%</td></tr><tr><td>Use (driving)</td><td>45.90</td><td>59%</td><td>2.51E-04</td><td>54%</td><td>4.59E-05</td><td>44%</td><td>2.34E-09</td><td>5%</td><td>1.05E-04</td><td>44%</td><td>4.19E-07</td><td>3%</td></tr><tr><td>End of life</td><td>1.17</td><td>2%</td><td>3.77E-06</td><td>1%</td><td>3.69E-07</td><td>0%</td><td>2.47E-11</td><td>0%</td><td>2.53E-06</td><td>1%</td><td>2.80E-09</td><td>0%</td></tr><tr><td>Total near-future<br>use case</td><td>77.50</td><td>100%</td><td>4.65E-04</td><td>100%</td><td>1.04E-04</td><td>100%</td><td>4.77E-08</td><td>100%</td><td>2.41E-04</td><td>100%</td><td>1.27E-05</td><td>100%</td></tr><tr><td>Total ideal use<br>case</td><td>6.92</td><td>9%</td><td>4.48E-05</td><td>10%</td><td>1.18E-05</td><td>11%</td><td>8.84E-09</td><td>19%</td><td>2.84E-05</td><td>12%</td><td>2.49E-06</td><td>20%</td></tr></tbody></table>
In the near-future use case, each pkm has a global warming potential of 78 g CO₂eq (as shown in Fig. 13.2 with a Sankey visualisation of the global warming potential within the automated minibus life cycle model). The majority of this impact, at 59%, stems from the use phase, while component production accounts for 39%. The primary climate impacts resulting from the use phase are primarily caused by the combustion of fossil fuels to power the operation of the automated minibus. For this reason, the use phase also accounts for 54% of the overall acidification potential. In all other environmental impact categories, the production of components either dominates moderately (eutrophication potential and photochemical ozone depletion potential) or by a wide margin (ozone layer depletion potential and resource depletion potential). The assembly and end-of-life phases of the product have no significant impact in any of the chosen environmental impact categories, which is why their modelling is based on average literature data.

The aforementioned Table 13.5 outlines the optimal parameter settings for the best possible scenario. By utilising these settings, a practical example with high automated minibus lifetime mileage, high passenger occupancy, low energy demand, and renewable energy supply can be modelled and evaluated. The total environmental impacts of this ideal scenario are presented in Table 13.6, revealing substantial reductions in environmental impact across all categories, with 80–91% reduction in resource depletion and climate change impact.

## 13.4.2 Scenario Analysis

The scenario analyses have been executed for all the parameters listed in Table 13.5 and for every environmental impact category under consideration. The findings of the scenario analyses, depicted in Fig. 13.3, demonstrate the paramount importance of non-technical parameters in determining the overall environmental impact of

<figure><img src="image_143.png" /><figcaption>**Fig. 13.3** Scenario analyses for resource depletion potential and global warming potential (near-future use case = 0%)</figcaption></figure>
automated minibuses in terms of resource depletion and global warming potential. The depletion of resources and the impact of climate change are significantly affected by low passenger occupancies and low annual mileage, whereas the energy demand of the vehicle has only a limited impact. Additionally, the energy source used to charge the automated minibus battery is the most important factor in reducing the climate impact. Operating the automated minibus within a fully renewable energy system can reduce the global warming potential by 58%.

The environmental impact category of climate change demonstrates the disparate effects of different parameters on the production and use phases (see Fig. 13.4). While the vehicle's lifetime and mileage only affect the production phase, the energy demand and energy mix influence the use phase. Occupancy affects both the production and use phases equally. The relationship between the use phase and the production phase, as shown in Fig. 13.4, exhibits strong fluctuations. In extreme cases, such as very high mileage and a high fossil energy mix, the use phase dominates. Conversely, when renewable energies are used, the importance of the use phase decreases from a climate perspective, and the importance of production for the overall result increases.

### 13.4.3 Impact of Automated Components

The focus of the study lies in the components necessary for automated driving. Table 13.6 indicates that the production of these components has a minimal impact on environmental performance, accounting for less than 2% in all impact categories. Additionally, the energy consumption of these components is of importance, despite the shift towards renewable electricity sources. The use phase's significance is reduced in the long run, but energy demand and energy mix remain critical factors in overall performance in the present. Table 13.7 provides a list of reference technologies and their corresponding nominal power figures for all the components used in automated manual driving (for a

<figure><img src="image_144.png" /><figcaption>**Fig. 13.4** Scenario analysis (global warming potential) comparing automated minibus production and use phase (near-future use case = 100%)</figcaption></figure>
<table><caption>**Table 13.7** Nominal power of automated driving components installed in one automated minibus (Light detection and ranging sensors (LiDARS), GNSS, GPS)</caption><thead><tr><td>Automated driving component</td><td>Number of components</td><td>Nominal power (W)</td></tr></thead><tbody><tr><td>180° monolayer LiDARS</td><td>6</td><td>48.0</td></tr><tr><td>360° multi-layer LiDARS</td><td>2</td><td>24.0</td></tr><tr><td>Computer</td><td>2</td><td>160.0</td></tr><tr><td>Module GNSS</td><td>1</td><td>5.6</td></tr><tr><td>Inertial unit</td><td>1</td><td>0.2</td></tr><tr><td>World shuttle router</td><td>1</td><td>25.5</td></tr><tr><td>Front/rear cameras</td><td>4</td><td>4.0</td></tr><tr><td>Wheel encoder</td><td>4</td><td>0.6</td></tr><tr><td>3G and Ethernet router</td><td>2</td><td>12.0</td></tr><tr><td>15" touchscreen</td><td>1</td><td>15.0</td></tr><tr><td>Steering encoder</td><td>2</td><td>1.2</td></tr><tr><td>Radio module GNSS</td><td>1</td><td>0.2</td></tr><tr><td>4G antenna</td><td>1</td><td>5.0</td></tr><tr><td>GPS antenna</td><td>2</td><td>3.2</td></tr><tr><td>Total</td><td>304.5</td><td></td></tr></tbody></table>

more detailed table that includes manufacturers, models, and Internet sources, please refer to the supplementary material). In summary, the components used in automated manual driving require approximately 300 W of power in total.

According to Gawron et al. (2018) and Baxter et al. (2018) the power requirements for a medium-sized, automated vehicle are estimated to be 240 W and 200 W, respectively. The higher value of the present study might be explained by a more comprehensive list of components compared to Gawron et al. (2018) and Baxter et al. (2018), which primarily focus on primary hardware technology, such as sensors, radars, cameras, light detection and ranging sensors (LiDARS), computers, and location detection.

Table 13.7 presents the average speed at the various trial sites. The distance-weighted average speed of all sites is 11.4 km/h. If all automated components are running at their full nominal power, the energy consumption for a distance of 11.4 km is 304.5 Wh, which translates to 26.7 Wh/km. The average energy demand of the trial sites is 554 Wh/km. Therefore, only 4.8% of the total energy demand is due to the use of components required for autonomous driving.

### 13.4.4 Contextualisation

With a high degree of confidence, the environmental impact per pkm of near-future and ideal future applications of automated minibuses is significantly lower than the current trial cases. To compare its performance with other modes of transportation, the automated minibuses' climate change impact per km is contrasted against literature values of other vehicles (Table 13.8). For all vehicles, the impact is calculated
for off-peak, average, and peak operation. The average number of passengers per vehicle used in the calculation is 1.58, based on a study by Chester and Horvath (2009). Figure 13.5 shows the climate change impacts of all transportation modes, including the automated minibuses near-future and ideal use case.

<table><caption>**Table 13.8** Climate impacts, lifetime mileages, and passenger occupancies for various individual and public transportation vehicles (based on [1] Puig-Samper Naranjo et al., 2021; [2] Gawron et al., 2018; [3] Kemp et al., 2020; [4] Nordelöf et al., 2019; [5] this paper): abbreviations: *Ind.* individual, *ICEV* internal combustion engine vehicle, *HEV* hybrid electric vehicle, *BEV* battery electric vehicle, *BECAV* battery electric connected and automated vehicle, *ICECAV* internal combustion engine connected and automated vehicle, *SUV* sports utility vehicle, *BEB* battery electric bus, *PHEB* plug-in hybrid electric bus, *HEB* hybrid electric bus, *AM NF* automated minibus near-future use case, *AM ideal* automated minibus ideal use case</caption><thead><tr><th rowspan="2">Unit</th><th>Peak operation</th><th>Off-peak operation</th><th>Average operation</th><th>Peak occupancy</th><th>Off-peak occupancy</th><th>Average occupancy</th><th>Lifetime mileage</th></tr><tr><th>g CO<sub>2</sub>eq/pkm</th><th>g CO<sub>2</sub>eq/pkm</th><th>g CO<sub>2</sub>eq/pkm</th><th>No. of passengers</th><th>No. of passengers</th><th>No. of passengers</th><th>km</th></tr></thead><tbody><tr><td>Ind. ICEV petrol [1]</td><td>52</td><td>261</td><td>131</td><td>5</td><td>1</td><td>1.58</td><td>150,000</td></tr><tr><td>Ind. ICEV diesel [1]</td><td>48</td><td>241</td><td>121</td><td>5</td><td>1</td><td>1.58</td><td>150,000</td></tr><tr><td>Ind. HEV, EU electricity [1]</td><td>44</td><td>222</td><td>111</td><td>5</td><td>1</td><td>1.58</td><td>150,000</td></tr><tr><td>Ind. BEV, EU electricity [1]</td><td>27</td><td>135</td><td>68</td><td>5</td><td>1</td><td>1.58</td><td>150,000</td></tr><tr><td>Ind. small BECAV, US electricity [2]</td><td>44</td><td>221</td><td>140</td><td>5</td><td>1</td><td>1.58</td><td>257,494</td></tr><tr><td>Ind. medium BECAV, US electricity [2]</td><td>45</td><td>223</td><td>141</td><td>5</td><td>1</td><td>1.58</td><td>257,494</td></tr><tr><td>Ind. large BECAV, US electricity [2]</td><td>50</td><td>250</td><td>158</td><td>5</td><td>1</td><td>1.58</td><td>257,494</td></tr><tr><td>Ind. small ICECAV [2]</td><td>74</td><td>372</td><td>235</td><td>5</td><td>1</td><td>1.58</td><td>257,494</td></tr></tbody></table>
<table><caption>**Table 13.8** (continued)</caption><thead><tr><th rowspan="2">Unit</th><th>Peak operation</th><th>Off-peak operation</th><th>Average operation</th><th>Peak occupancy</th><th>Off-peak occupancy</th><th>Average occupancy</th><th>Lifetime mileage</th></tr><tr><th>g CO<sub>2</sub>eq/pkm</th><th>g CO<sub>2</sub>eq/pkm</th><th>g CO<sub>2</sub>eq/pkm</th><th>No. of passengers</th><th>No. of passengers</th><th>No. of passengers</th><th>km</th></tr></thead><tbody><tr><td>Ind. medium ICECAV [2]</td><td>75</td><td>375</td><td>237</td><td>5</td><td>1</td><td>1.58</td><td>257,494</td></tr><tr><td>Ind. large ICECAV [2]</td><td>86</td><td>431</td><td>273</td><td>5</td><td>1</td><td>1.58</td><td>257,494</td></tr><tr><td>Ind. BECAV SUV, US electricity [3]</td><td>27</td><td>134</td><td>85</td><td>5</td><td>1</td><td>1.58</td><td>321,868</td></tr><tr><td>Ind. ICECAV van [3]</td><td>60</td><td>301</td><td>85</td><td>5</td><td>1</td><td>1.58</td><td>321,868</td></tr><tr><td>Public BEB, EU electricity [4]</td><td>7</td><td>154</td><td>48</td><td>105</td><td>5</td><td>16</td><td>780,000</td></tr><tr><td>Public PHEB, diesel [4]</td><td>13</td><td>202</td><td>63</td><td>80</td><td>5</td><td>16</td><td>780,000</td></tr><tr><td>Public HEB, diesel [4]</td><td>10</td><td>211</td><td>66</td><td>102</td><td>5</td><td>16</td><td>780,000</td></tr><tr><td>Public diesel bus [4]</td><td>16</td><td>304</td><td>95</td><td>95</td><td>5</td><td>16</td><td>780,000</td></tr><tr><td>Public-AM NF [5]</td><td>39</td><td>387</td><td>77</td><td>10</td><td>1</td><td>5</td><td>140,000</td></tr><tr><td>Public-AM ideal [5]</td><td>7</td><td>69</td><td>14</td><td>10</td><td>1</td><td>5</td><td>365,000</td></tr></tbody></table>

Compared to other forms of public transportation, the near-future use case of automated minibuses has higher climate impacts per pkm, with the exception of the comparison to the average operation of diesel buses. However, it should be noted that all other forms of public transportation use larger buses with higher passenger numbers for peak, average, and off-peak operation. The ideal use case of automated minibuses performs better than any other form of transportation, demonstrating the significant potential for EMA to improve the environment further and optimise it. It should be viewed with caution, as no ideal use case was calculated for the other forms of transportation, and a renewable energy mix would also have a positive effect on all other battery-electric and hybrid vehicles.
<figure><img src="image_145.png" /><figcaption>**Fig. 13.5** Climate impact of different transportation modes in g CO₂eq per pkm (own compilation, based on [1] Puig-Samper Naranjo et al., 2021; [2] Gawron et al., 2018; [3] Kemp et al., 2020; [4] Nordelöf et al., 2019; [5] this study, all abbreviations are detailed in Table 13.8)</figcaption></figure>

### 13.4.5 Discussion and Limitations

The present LCA identifies the key factors that influence the environmental impact of automated minibuses. Similar to electric vehicles in general (as noted in Helmers et al., 2017), the energy consumption and electricity mix used for charging batteries have a significant impact on the vehicle's climate performance. Moreover, systemic factors, such as the utilisation of the vehicle in terms of annual mileage and passenger occupancy, also play a crucial role. Therefore, whether automated minibuses can be considered environmentally friendly depends on various factors. While an infrequently used automated minibus with few passengers is highly unlikely to have a favourable environmental impact, a heavily used automated minibus that is also fully utilised in terms of its passengers can outperform other transportation methods in terms of environmental benefits. Based on measurement data from operating automated minibuses, a near-future use case has been defined that is considered to be very achievable in the coming years. This use case already shows a very good environmental performance under the aforementioned framework conditions.

The use case calculated in an optimal manner also highlights the potential environmental benefits of automated minibuses, as long as all conditions and parameters are optimal. The most environmentally intensive phases of an automated minibuses life cycle are the production of components and driving, and the components required for automated driving play a minor role, contributing less than 2% to production-related climate impacts and approximately 5% to driving-related climate impacts.
The LCA results presented here are promising, particularly because they indicate the potential for achieving higher mileages in the short term, and there is a positive attitude (goodwill) expressed by potential users towards the automated minibus (Korbee et al., 2024). This study also has some limitations that are important to consider. For example, the data used was collected from pilot sites provided by public transport operators and vehicle manufacturers. These sites have an experimental nature and were affected by COVID restrictions, which limited the amount of data collected and the number of passengers in the vehicles. As data accuracy is a recurrent concern in LCAs of emerging technologies (Arvidsson et al., 2018; Hetherington et al., 2014), future research could focus on utilising longer time-series of data collected from regular operations rather than relying on data from demonstration and trial site operations. To address the potential consequences of innovative technology data, this study suggests utilising scenario development. The purpose of these findings is not to present specific numbers for inclusion in product declarations but rather to offer valuable insights on the key factors affecting the future environmental impact of automated minibuses.

The low relevance of automated driving components is comparable to studies of automated vehicles (Gawron et al., 2018).

Although other studies forecast a much greater effect of automated components on overall energy consumption (Brown et al., 2013; Gawron et al., 2018; Gonder et al., 2016; Grisoni & Madelenat, 2021; inria, 2019; Krail, 2021; Pihkola et al., 2018; Saujot et al., 2017; Wadud et al., 2016).

The deployment of automated vehicles necessitates adaptations in both physical and digital infrastructure, as pointed out by Noussan & Tagliapietra, 2020, and the implementation of vehicle-to-everything (V2X) technologies requires additional technical infrastructure.

The following out-of-vehicle technical infrastructure is necessary for the operation of automated vehicles: roads, sensors to detect special signals, and a long-range wireless network (Liu, Tight, et al., 2019). The examined automated minibus neither transmits large amounts of data to the outside nor requires extensive additional technical infrastructure, which may change in the future. Notably, the study did not consider the long-term benefits of automated driving compared to human driving. Research suggests that connectivity and cooperative technologies will lead to better traffic anticipation, modulated driving, better manoeuvring (Fagnant & Kockelman, 2015), and better ride-matching capacity (Shaheen & Bouzaghrane, 2019), which could further reduce energy consumption. However, the use of automated vehicles may simultaneously reduce energy demand through efficient driving and increase it through additional data processing and transmission (see, e.g. Stephens et al., 2019). The current state of research does not allow for a definite conclusion, and the impact of data processing and transmission on the automated minibus under investigation is currently minor. The implementation of innovation and efficiency improvements in batteries is expected to decrease the negative impact on the environment caused by automated minibuses.

The automated minibus energy demand of 554 Wh per driven km in comparison to other electric vehicles is quite high, as reported by Puig-Samper Naranjo et al.,
2021 and Bauer et al., 2015. Since the components for automated driving do not significantly influence this consumption, other factors must be responsible for this high level of energy consumption. Specifically, the heating and cooling of the vehicles, as well as their low speed, are noteworthy. The entire interior of the vehicle is constantly cooled on warm days and warmed up on cold days, which results in high energy consumption. To further reduce the overall energy consumption and environmental impacts, higher speeds, reduced heating and cooling behaviour, and additional energy efficiency measures on the vehicle could be implemented, but these are not within the scope of this study.

### 13.4.6 Conclusion and Consequences for Future Mobility Systems

This LCA study highlights the potential of automated minibuses as part of public transport. If automated minibuses are utilised extensively in terms of mileage and regularly used by multiple passengers, they offer significant environmental benefits and perform similarly or better than other public transport vehicles. However, it becomes clear that automated minibuses play a specific role in the overall mobility system and cannot replace all other modes of transport. Currently, the performance of automated minibuses, which features low speed, low passenger capacity, door-to-door service, on-demand service, and driverless operation, is seen as complementary to public transport. For example, automated minibuses can cover the ‘first and last mile’ or take over off-peak operations of regular buses, increasing the availability, flexibility, efficiency, and reliability of local public transport, which brings significant environmental benefits, especially when replacing individual motorised transportation. However, there may be some rebound effects if automated minibuses replace walking or biking or lead to more travel due to their convenience and comfort (Grisoni & Madelenat, 2021; inria, 2019; Saujot et al., 2017).

Taking into account the environmental and sustainability science perspective, this study highlights the limitations of LCA studies that solely concentrate on individual vehicles and vehicle types. The environmental benefits of automated minibuses are influenced by the individual vehicle's performance but are largely determined by the vehicle's utilisation, occupancy, and integration within a comprehensive transportation system. As a result, comparing different automated minibus types or brands is relatively inconsequential in this context.

The findings of this research are relevant to decision-makers at various levels of policy and public transport operators. For the latter, the study offers clear understanding of the environmental benefits and drawbacks of the automated minibus implementation. For policymakers, the study underscores the importance of developing plans and frameworks for the deployment of autonomous vehicles in a timely manner to maximise environmental benefits. A public transport system that integrates automated minibuses at strategic points and is multimodal and flexible appears to be a promising approach.
## 13.5 Final Environmental Indicators for Sustainability Assessment of Pilot Sites

The assessment of the pilot sites finalises the analysis presented in the second iteration of the environmental deliverable, and it focuses on the environmental indicators. The assessment presents the data collected from the pilot sites as well as the recent updates of the methodology and results. It serves as background information and data for the final sustainability deliverable 8.12.

The objective of this section is to investigate the environmental performance of the automated minibus through mobility indicators. Sustainability indicators are a powerful tool to simplify, quantify, analyse, and communicate complex information (Innamaa & Salla, 2018; KEI, 2005; Singh et al., 2009). In addition, urban sustainability indicators are fundamental to support target setting and performance reviews and enable communication among policymakers, experts, and the general public (Shen et al., 2011; Verbruggen & Kuik, 1991).

The environmental indicators and respective units of assessment are presented in Table 13.9.

In addition to the environmental indicators, the indicators for the sustainability assessment comprehend the social, economic, governance, and system performance of the automated minibus (Nemoto et al., 2021).

Each indicator requires a specific methodology (refer to APPENDIX A), and the value of the indicators is represented on a scale of 1–5, with 5 being considered the best score. For each indicator we:

1. Defined a parameter.

2. Defined a scale, with minimum and maximum values considering the environmental impacts of main urban modes of transport, e.g. walking, cycling, small and big cars, and bus (freight transport and air transport were not comprehended, for example).

3. Calculated the indicator value for the automated minibus according to the demonstrator site.

<table><caption>**Table 13.9** The environmental indicators and units of assessment</caption><thead><tr><td>Environmental indicators</td><td>Unit of assessment</td></tr></thead><tbody><tr><td>Energy efficiency</td><td>Automated minibus energy consumed for passenger per km (kWh/pkm)</td></tr><tr><td>Use of renewable energy</td><td>Automated minibus use phase, energy source, and percentage of renewable energy sources (%)</td></tr><tr><td>Noise pollution</td><td>Automated minibus traffic noise (dB)</td></tr><tr><td>Air pollution</td><td>Automated minibus emissions of air pollutants, PM levels (ug/m3), NOx, CO emissions</td></tr><tr><td>Climate change</td><td>Automated minibus GHG emissions: CO<sub>2</sub>eq/pkm</td></tr></tbody></table>
The results are presented on a spider chart, providing a disaggregated overview of the indicators. This allows for identifying the weaknesses and strengths of each indicator (WBCSD, 2015), also for a comparison between the pilot sites.

The limitations of the assessment concern the innovativeness of the automated minibus. The technology is still in a test and development phase. Hence, the main limitations concern the fact that the pilot projects are restricted to a local/neighbourhood area, and the automated minibuses drive in mixed traffic area at a low average speed (10–18 km/h). The automated minibuses drive on a fixed route (with the exception of ‘Belle Idée’ test site, where on-demand service has been tested), and the safety driver on board the automated minibus is required in case human intervention is required, as well as to report the performance of the automated minibuses in general.

These limitations reduce the performance and usability of the automated minibuses. In addition, the demonstrator sites have been facing constraints due to the COVID-19 pandemic. As a result, there have been interruptions in the pilot tests, and some transport companies have limited the maximum number of passengers to four during certain periods. This factor has a negative impact on the automated minibuses performance assessed by the environmental indicators.

The next section presents the results for the environmental indicators for five AVENUE demonstrator sites: Pfaffenthal and Contern (Luxembourg City), Groupama Stadium (Lyon, France), Ormøya (Oslo, Norway), and Nordhavn (Copenhagen, Denmark). The transport operators provided primary data; therefore, the results and analysis rely on the data presented in Table 13.4 (Chap. 3), which also provided inputs for the LCA study.

An overview of the pilot trials is present in Table 13.10, and the results per site are illustrated in Fig. 13.6, followed by analysis and conclusions.

The indicators addressing ‘local air pollution’ and ‘local noise pollution’ do not vary from site to site because they are assessed according to the vehicle. As an electric vehicle, the automated minibus has a good score on local air pollution. It is explained by the fact that BEVs in their use phase have zero exhaust emissions, e.g.

<table><caption>**Table 13.10** Overview of the AVENUE demonstrator sites</caption><thead><tr><td>City</td><td>Pilot</td><td>Characteristics of route</td><td>Type of passenger</td></tr></thead><tbody><tr><td>Lyon</td><td>Groupama Stadium</td><td>Fixed route with stops 1.3 km Will become an on-demand, door-to-station service</td><td>Regular workers, people with reduced mobility (medical Centre nearby)</td></tr><tr><td>Copenhagen</td><td>Nordhavn</td><td>Fixed route with stops, 1,2 km, will become an on-demand, door-to-door service</td><td>Residents of the area, tourists</td></tr><tr><td>Oslo</td><td>Ormøya</td><td>Fixed route with stops, 3,6 km</td><td>Residents of the area</td></tr><tr><td>Luxembourg</td><td>Contern</td><td>Fixed route with stops, on-demand. 2.2 km</td><td>Employees working at campus Contern</td></tr><tr><td></td><td>Pfaffenthal</td><td>Fixed route with stops, on-demand 1.2 km</td><td>Workers, tourists, residents, and visitors of Luxembourg city</td></tr></tbody></table>
<figure><img src="image_146.png" /><figcaption>**Fig. 13.6** Environmental performance of the automated minibuses in the demonstrator sites. The scale ranges from 1 to 5, with 5 as the best score and 1 worst score</figcaption></figure>

NOx and PM, and they just emit PM locally from road and tyre and brake wear, like other motor vehicles (European Environment Agency, 2018). The air pollutant emissions for the electricity generation to charge BEV batteries occur in power stations and tend to impact less densely populated areas (ibid). For this reason, the local air pollution emissions are assessed here for the use phase, as they affect cities (more densely populated areas) and consequently cause greater human exposure and potential health damage.

For local noise pollution, the automated minibus as an EV do not differ significantly from ICEV in the usual traffic and from 30 km/h speed. This is due to the fact that ‘the tyre/road noise increases more with increasing speed than the propulsion noise, and therefore the tyre/road noise dominates the propulsion noise at high speeds’ (Marbjerg, 2013). Therefore, the automated minibuses as an EV play a role to avoid local noise pollution for urban traffic during the night in low-speed areas (Jochem et al., 2016). Since the automated minibuses currently run at a low speed of 11–18 km/h, their noise pollution is slightly lower than ICEVs and lower than regular buses.

In all the pilot sites, the automated minibuses scored poorly for ‘low contribution to Climate Change’. This indicator is highly affected by the low occupancy of the automated minibuses, due to the characteristics of the pilots, such as temporary and new services, the newness of the technology, as well as the interruptions of the trials due to the constraints of the COVID-19 pandemic and the reduction in mobility and in the use of public transport. Further, the climate change indicator is affected by the vehicle lifetime, total mileage, and electricity mix, as pointed by the LCA study (Chap. 3). From all the sites, Pfaffenthal (Luxembourg) presents a better performance due to the higher average of vehicle occupancy, while Ormøya (Oslo) and
Nordhavn (Copenhagen) present the lowest performance for climate change due to the very low average occupancy and low mileage in the case of Nordhavn.

Likewise, the energy efficiency indicator is directly impacted by the average occupancy of the automated minibus. Therefore Pfaffenthal (Luxembourg) presents a good score (with an average occupancy of 2 or 8 passengers), in contrast to the other sites.

The indicator of renewable energy for the use phase varies according to the share of energy from renewable sources in gross electricity consumption in each country. In this case, Nordhavn (Copenhagen) and Ormøya (Oslo) present a good score since Denmark and Norway have a share of energy from renewable sources in gross electricity consumption of 62% and 100%, respectively, in contrast to 9% in Luxembourg and 21% in France.

In relation to Chap. 2, it is worth noting that the pilot trial at Groupama Stadium (Lyon) comprehends V2I communication, meaning that three traffic light junctions operate in communication with the automated minibus. The V2X communications were not taken into account for the environmental indicators at this stage. However, on a larger scale, vehicle communications and connectivity could contribute to reducing energy impacts in mobility. Lee and Kockelman (2019), for example, pointed out that energy savings resulting from vehicle-to-infrastructure connectivity and smart intersections range from 6% to 30%, thanks to improvements in traffic interactions and better fuel-efficient driving (see more in Chap. 2).

The assessment of automated minibuses based on the environmental indicators point out that the automated minibuses face challenges to be deployed as an environmentally friendly mode of transport at the current stage.

A key factor targeting ‘low contribution to climate change’ is primarily to increase the vehicle occupancy and secondly through technology development, to increase the vehicle speed, mileage, and lifetime. Likewise, by aiming at a better energy efficiency, it is crucial to increase vehicle occupancy. Therefore, it is important that the automated minibuses are deployed in routes in order to cover real gaps in mobility, with more permanent services and good acceptance. And as mentioned previously, the average occupancy of the automated minibuses was also affected by the COVID pandemic, interruptions in the trials, and mobility restrictions.

The automated minibuses, as a BEV, can highly contribute to the reduction in local air pollution, and while targeting the reduction of local noise pollution, the automated minibuses present limited advantages in comparison to regular cars and buses, reducing noise during the night and at low-speed areas.

In the future, the automated minibus has the potential to be deployed as environmentally friendly mobility taking into account technological improvements, better social acceptance and usability, better integration into urban mobility as part of intermodal and MaaS systems, as well as shared and electric mobility.

The sustainability assessment study aims to set goals for the future deployment of the automated minibuses and therefore monitor the progress of the environmental and remaining sustainability indicators towards a more sustainable operation.
## 13.6 General Discussion and Conclusion

The different assessments in this deliverable reach interesting results on the current and future performance of automated minibuses. The energy demand analysis of the automated components in the automated minibus shows that the energy efficiency depends on the wireless technologies, the cooperative communication modes, and the implemented services. The automated technologies in the automated minibus, as deployed in the AVENUE pilots, are around 5% of the total energy used. The potential evolution of the energy demand depends on the different types of Internet connections. The driverless wireless network impacts the transmitted data and eventually the overall energy consumption or savings. Moreover, predictive and adaptive driving functions and information sharing in the automated minibus are likely to improve the acceleration and braking processes and hence contribute to overall energy savings. The energy demand also differs if the automated minibus provides an on-demand service. Overall, the energy-saving potential from predictive driving functions is highly likely to outweigh the energy consumption from data transmission energy.

Going beyond the energy analysis related to driving itself, the presented LCA study focuses on environmentally relevant energy flow and materials throughout the life cycle of the vehicle within the public transportation system. The study reiterates findings from other studies that energy consumption and the electricity mix used for charging have significant climate performance impacts. It also shows a low relevance of automated driving components in current deployment circumstances. The LCA study was used to identify factors that contribute to the environmental impact of automated minibuses. The environmental benefits of automated minibuses rely on the utilisation rate and occupancy factor and thus on their integration in the overall transportation system.

The current deployment of automated minibuses does not show significant environmental benefits, but future use cases are likely to improve substantially. The development and assessment of environmental indicators for an overall sustainability assessment corroborate the LCA study's conclusions. Occupancy, vehicle speed, mileage, and lifetime play an important role in reducing environmental impacts. Automated minibuses are particularly beneficial if they can be deployed to close former public mobility gaps, which would otherwise lead to the use of individual cars.

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

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

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**Chapter 14**

**Environmental Impact Assessment:**
**Externalities of Automated Electric**
**Vehicles for Public Transport**

**Ines Jaroudi, Adrian Boos, Tobias Viere, and Guy Fournier**

**Abstract** This chapter provides a comprehensive analysis of the economic and environmental externalities associated with the use of automated minibuses in public transport, using a mixture of scenario planning and an externality model in the context of the AVENUE project cities. By analysing six different deployment scenarios, including the substitution of automated minibuses for buses and private cars, this study sheds light on the potential shifts in external costs and benefits. This chapter carefully assesses the impact of the deployment of automated minibuses on reducing external costs, taking into account factors such as energy efficiency, connectivity, automation features passenger numbers and vehicle utilisation rates. The results show that the environmental and economic outcomes of deploying automated minibuses depend significantly on the specific deployment strategies, highlighting scenarios in which automated minibuses could either reduce or exacerbate external costs. Through a detailed assessment of these scenarios, the chapter provides a nuanced understanding of how the strategic integration of automated minibuses into urban transport systems can influence the broader goals of economic sustainability and environmental protection. The study emphasises the importance of aligning automated minibus deployment strategies with city-specific goals and the broader sustainability agenda and provides valuable insights for policymakers, urban planners and transport stakeholders.

I. Jaroudi (✉)

Fraunhofer ISI, Karlsruhe, Germany

Pforzheim University, Pforzheim, Germany

CentraleSupélec, LGI, Gif-sur-Yvette, France

e-mail: ines.jaroudi@isi.fraunhofer.de

A. Boos · T. Viere

Pforzheim University, Pforzheim, Germany

G. Fournier

Pforzheim University, Pforzheim, Germany

CentraleSupélec, LGI, Gif-sur-Yvette, France
# 14.1 Introduction

The following assessment addresses six scenarios for potential deployment in AVENUE cities and the resulting external costs. It focuses on the potential increase or decrease in externalities as an indicator for the scenarios in which mobility should evolve. The summary of categories, impacts and methods using the work of van Essen et al. (2019), Jochem et al. (2016), Héran and Ravalet (2008), Fagnant and Kockelman (2018), and Shalkamy et al. (2015) is shown in Table 14.1.

In this chapter, the scenarios planning methodology (intuitive logic approach, key factors and driving forces) as well as the six selected scenarios (replace all buses, replace all cars, expand the network, targeted expansion of the network, robotaxis, AM in MaaS) are explained and the externality categories are introduced. First, the externalities and scenarios model is applied in detail to the case study of Geneva, where the mobility behaviour of the city, the results in terms of savings or losses per scenario and their implications are presented. Then, the results are discussed, such as the limitations of the analysis, comparison between the scenarios, potential policy recommendations, and rebound effects. Afterwards, it is applied to the other three cities of AVENUE (Luxembourg, Lyon and Copenhagen). In a final stage, a comparative analysis is conducted between the cities.

<table><caption>**Table 14.1** Recapitulation of the categories, effects and methods of externalities</caption><thead><tr><td>Categories</td><td>Impacts</td><td>Methods</td></tr></thead><tbody><tr><td>Tank to wheel (ttw) air pollution (PM2.5, PM10, NOx, SO2 and NMVOC)</td><td>Health effects, crop losses, building damage and biodiversity loss</td><td>Damage cost estimation</td></tr><tr><td>Tank to wheel (ttw) climate change (CO2, N2O and CH4)</td><td>Sea-level rise, biodiversity loss, water management difficulties, extreme weather conditions and crop failures</td><td>Avoidance cost approach</td></tr><tr><td>Well-to-tank (wtt) aggregated emissions</td><td>Similar to TTW</td><td>Similar to TTW</td></tr><tr><td>Noise</td><td>Annoyances, health effects caused by road traffic noise</td><td>Willingness-to-pay, burden of disease approach</td></tr><tr><td>Accidents</td><td>Material damage, production losses and administrative, medical and human costs</td><td>Damage cost approach</td></tr><tr><td>Congestion</td><td>Not meeting passengers' mobility demand due to the temporary scarcity of infrastructure (traffic flow reaches its capacity)</td><td>Delay cost and deadweight loss approaches</td></tr><tr><td>Parking space</td><td>Urban space in km2</td><td>Based on Héran and Ravalet (2008)</td></tr><tr><td>Production phase</td><td>Climate change emissions</td><td>Avoidance cost approach</td></tr></tbody></table>
## 14.2 Scenarios

### 14.2.1 Methodology

Scenario planning is a way to imagine potential paths in the future (Derbyshire & Wright, 2017). The scenarios are built using the intuitive logic approach (ILA). ILA defines driving forces (political, economic, technological, ecological, social and legal) as well as key factors that help structure the scenarios. These factors are either quantitative and predictable, such as demographics, while others are qualitative and less predictable, such as user acceptance and policies (Huss & Honton, 1987; Lindgren & Bandhold, 2009). The ILA strength lies in its flexibility (Zmud et al., 2015).

For this study, the driving forces help answer how and why the AM might be deployed in each scenario; they are defined, based on Townsend (2014) and Milakis et al. (2017) scenarios, as follows:

- Technology advancement (automation technology and digital services).

- Urban policy (political agenda for mobility and sustainability).

- Transportation offer (trends of use and modes available).

- The users (most likely to use the AM in the scenario).

As for the key factors, they were determined through a deliberative process within the AVENUE team. The trial sites, the interdisciplinary nature of the project, and the work of (Beukers, 2019; Korbee et al., 2021; Viere et al., 2021) were used to select the following key factors:

- Whether the AM are replacing one mode of transport or multiple modes

- Whether AM supports or competes with public transport (PT) (replace buses modal share or not)

These two factors help estimate the modal shift within each scenario, which is crucial to estimating the future transportation performance in person-km (pkm) based on mobility censuses and the consequent externality estimations.

The setting of key forces and key factors is combined with the system boundaries of each scenario. Figure 14.1 shows the classification of the scenarios based on the key factors.

The boundaries are defined by the key parameters of the vehicles, circulation specifications and AM service. Thus, the analysis is continued as a qualitative assessment that studies the direct and indirect consequences of the modal shift from each scenario using observations from AVENUE pilot studies and previous research on AV deployment. The overall structure is presented in Fig. 14.3, and it is the basis for the scenario description. The methodology helps limit the uncertainty of the scenarios by building plausible stories (Lindgren & Bandhold, 2009; Amer et al., 2013). See Fig. 14.2 for the steps of the scenario description and Fig. 14.3 for the methodology:
<figure><img src="image_149.png" /><figcaption>**Fig. 14.1** The classification of scenarios based on key factors</figcaption></figure>

<figure><img src="image_150.png" /><figcaption>**Fig. 14.2** Steps of scenario building</figcaption></figure>
Methodology framework

## System Boundaries (assumptions)

Area urban / suburban level

## Vehicle and circulation specification

Capacity factors

Speed limits in the area

Traffic situation: average congestion on roads

Vehicles lifetime

## AM service

Intermodal and seamless, first/last mile, on-demand and door-to-door

## Scenario development (circumstances): Key factors (to define the scenario in the transportation system)

It supports public transport or competes with public transport

Replaces only one mode of transport, or it replaces more than one modal share

## Scenario development (circumstances) Driving forces (to frame the scenario):

Technology development (level of automation SAE, Connectivity V2X, digital services)

Regulatory conditions (policies: supportive or restrictive or other)

Transportation offer (description of other modes and dominance of private or public stakeholders)

Users (profile of AM users)

## Modal shift

The predicted AM modal share from absorbing other modes of transport modal share (based on the survey and literature)

## The qualitative assessment: Direct consequences

### Environmental and Societal impacts

Emissions

Accidents

Congestion

Noise

### Impacts on mobility

Travel demand - Increased demand due to on-demand service,

Road capacity-levels of congestion during off/on peak hours, the value of time)

### Impacts on infrastructure

Parking

## The qualitative assessment: Long term consequences (rebound effects)

### Impacts on mobility

On the transportation system (other secondary modal shifts mobility behaviour, induced demand-due to the convenience of service)

### Impacts on infrastructure (urban development)

Roads, city plans, jobs density, demographics

<caption>**Fig. 14.3 Methodology of scenario description**</caption>
## 14.2.2 The Scenario Description

Based on the methodology described, the analysis focuses on six scenarios that concern the deployment of AM in cities. At this level, the scenarios occur in a standard EU city (to be applied to Geneva, Lyon, Luxembourg and Copenhagen) and in the next decade, which is a safe estimation of when is it most likely to have AV technology on the roads (Milakis et al., 2017).

Out of the six scenarios, five focus on different modal shifts caused by the deployment of AM, while one focuses on robotaxis. Among the overall scenarios, four are set in the city centre, while two are in smaller urban dwellings (characterised by smaller urban densities) surrounding urban areas like villages, towns and suburban areas.

The scenario description using the proposed methodology helps pinpoint the advantages and disadvantages of each deployment strategy. It also seeks to learn about potential obstacles and catalysers to reduce the environmental footprint of introducing AV in urban areas. It is reinforced by a deliberative process with the AVENUE experts (Antonialli et al., 2021), where the most plausible scenarios are drafted based on the key factors and driving forces.

### 14.2.2.1 Scenario 1: Replace All Buses (Sc1)

This scenario occurs in the city centre. AM are implemented on a fixed schedule, yet they operate on flexible routes within a geofenced area.

According to Hansen et al. (2021), geofencing involves the utilisation of GPS or similar location-based technologies to define virtual boundaries or zones within which AM can function. These boundaries are typically determined by either the service provider or regulatory authorities. The minibuses are meant to support the public transport network, mainly rail transport, by replacing diesel buses. The AM are more flexible and less costly (Milakis et al., 2017).

Technological advancements such as achieving level 4/5 automation, improved sensory capabilities and AV platooning support the replacement (Wadud et al., 2016). The transportation system at the time of introducing the AM does not differ significantly from the status quo. The electrification process of the bus fleet is limited; motorised individual mobility is dominant.

The replacement might lead to a ripple effect that reduces car use and increases train ridership in the long term. Integrating electric powertrains in AM would lead to a decrease in both air pollution and greenhouse gas emissions compared to the typical buses. It is also wise to predict a change in the building environment, where more roads and train stations could be redesigned to assimilate the change. The city would assign more pick-up and drop-off points around the stations. Eventually, modernising the public transport and integrating AM in ITS will make the city centre more attractive, thus attracting more jobs and increasing the population (Narayanan et al., 2020).
<table><caption>**Table 14.2** Occupancy factors (van Essen et al., 2019)</caption><thead><tr><td>Vehicle</td><td>Occupancy factors</td></tr></thead><tbody><tr><td>Car</td><td>1.6</td></tr><tr><td>Bus</td><td>19.0</td></tr><tr><td>AM</td><td>6.0</td></tr><tr><td>Robotaxi</td><td>1.2</td></tr></tbody></table>

### 14.2.2.2 Scenario 2: Replace All Cars (Sc2)

This scenario is also set in a high-density urban area such as a city centre. The AM are deployed to replace all cars trips. The service of the AM is on-demand, door-to-door, and running on flexible routes. The AM would have on average six people per vehicle; see Table 14.2. We assume no restrictions on waiting time (for a waiting time of more than 4 min, there is no need for an increase in fleet size to meet an increase in travel demand as well as empty runs based on Fagnant and Kockelman (2018).

It is also set to support public transport and only replace cars by introducing policies restricting the circulation of internal combustion engine vehicles (ICEV). The scenario is supported by policies that mirror the current environmental agenda of sustainable cities, such as the zero-emission strategy or carbon tax, presenting the ultimate goal for sustainable mobility (Fournier et al., 2020; ITF, 2015; ITF, 2017; ITF, 2020).

The replacement of cars with automated vehicles such as the AM is bound to have a significant effect on accidents rates and traffic congestion, as explained by Childress et al., 2015; Alazzawi et al., 2018; Auld et al., 2017; Litman, 2021. Less AM are needed to meet the travel demand. This leads to an increase in road capacity and thus a decrease in road traffic, especially during peak hours (Wadud et al., 2016). On the one hand, the AM would be integrated better with other long-distance transportation such as trains as AM are used more by travellers. On the other hand, they might reduce the active transportation modal share because the AM provides on-demand and door-to-door short-distance trips (Milakis et al., 2017).

### 14.2.2.3 Scenario 3: Robotaxis (Sc3)

Robotaxis are described as shared automated vehicles in numerous studies (Alazzawi et al., 2018; Fagnant & Kockelman, 2018; Jones & Leibowicz, 2019; Litman, 2021). Although they might be comparable to the AM in terms of services (on-demand, door-to-door), they differ in the occupancy factors, speed, vehicle size and integration with PT (Merlin, 2017). The AM is a bigger vehicle that could carry up to 15 passengers, it rarely provides single-ride trips and it requires longer waiting times for pick-up. On the contrary, the robotaxis are destined mostly for single ridership. They are operated by private stakeholders, they could drive faster and they have reduced waiting times. They are convenient, especially if the passenger privileges their privacy (UITP, 2017).
For this scenario, the robotaxis serve the city centre as well. They do offer door-to-door and on-demand trips but no ridesharing services. They are competing with public transport, replacing more than one mode of transport (cars, buses and walking).

The regulatory conditions are described best as a “Laissez-faire” outcome. This means that there are no policies to regulate the AV market. Private stakeholders are seeking to maximise their profit which would have unpredictable consequences on sustainable mobility and people’s welfare (Niles, 2019). The regulatory conditions also translate into a deteriorating public transport offer that manifest in a high dependency on individual motorised mobility. A laissez-faire approach is likely to result in a rise in traffic volume and a potential increase in pkm. Concurrently, anticipated enhancements in network efficiency, often assumed, may not materialise without effective government intervention (Cohen & Cavoli, 2018).

The trend of AV markets driven by shareholders’ interest would create a race to optimise the services: higher speeds, less waiting times and more vehicles; therefore an increase in overall vehicle travelled km “VKM” is expected.

The long-term consequences of this scenario on the mobility system are the modal shift from active modes of transport and public transportation. Thus, this scenario is expected to cause induced demand as a rebound effect, and it could even reduce public transport ridership as it is very convenient (Litman, 2022; Niles, 2019; UITP, 2017). Furthermore, urban planning follows car-centric strategies, where the building environment is designed to accommodate private vehicles rather than the people. The spread of AV means new roadway design features such as improved lane markings, signs designed to be read electronically and wireless repeaters in tunnels to provide internet access (Childress et al., 2015).

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

In this scenario, the AM are deployed to support public transportation in low-density urban areas such as small villages and suburban areas where the PT offer is limited compared to more urbanised areas. The service provided by the AM is seamless intermodal trips. The modal shift is to replace one mode of transportation, mainly cars. The technological development is similar to previous scenarios. The policies in place that enable the expansion of the transportation network with AM are drafted to increase accessibility, reduce reliance on individual vehicles, and develop surrounding areas to cities. The modal shift is partially from cars to AM. The direct repercussions of the scenario, in terms of environmental and societal impacts, should be similar to the “replace all cars” scenario. The direct effect on mobility is that the AM is more susceptible to making empty runs and carrying fewer passengers on board. The AM are meant to attract car users. Thus they should provide services that could compete with the comfort and convenience of an individual vehicle.

For the indirect consequences, we predict that the AM would increase overall PT ridership. However, it is difficult to determine the effect on walking and biking, but
as it offers short trips, it could also replace some walking and biking. The improvement in the public transport network outside of densely populated areas would make them more attractive to inhabitants.

### 14.2.2.5 Scenario 5: Targeted Expansion of the Public Transport Network (Sc5)

The targeted expansion scenario is similar to scenario 4 “expand the network”. In this scenario, the AM are supporting the public transport network. It also partially replaces car modal share. The prominent feature in this scenario is that it also replaces a share of the buses. Specifically, the AM are deployed to replace night buses and low occupancy buses. The bus service is at capacity (or even over capacity) during peak hours but underutilised during the day (Pyddoke, 2020).

Moreover, it runs frequently empty in areas with high car ownership (Adra et al. (2004). Hence, substituting these buses with AM could reduce the environmental impact. This scenario’s direct and indirect consequences are similar to scenario 4.

### 14.2.2.6 Scenario 6: AM in MaaS (Sc6)

The AM are deployed within MaaS to better provide on-demand services that bridge the first and last mile and provide seamless and intermodal trips. They are deployed in highly dense areas such as city centres. Their introduction aims to support PT. They are positioned to influence more than one mode of transport (cars, walking and biking). The technological innovations in AV are similar to the previous scenarios. However, there are significant advancements in digital on-demand services, interoperability, ticketing, utilising mobile apps, the cloud, ride-pooling and routing algorithms (Plested, 2021).

The regulations to support this deployment strategy rely on public and private collaboration for MaaS services, platform management, open API, and data sharing.

Other regulations in place are similar to scenario 2 “replacing all cars”, where the city is seeking to implement more sustainable practices in line with the sustainable urban mobility plan (SUMP) and smart city initiatives. They adopt fuel and parking measures and push and pull regulations (in line with transport demand management (TDM)) to prevent the use of ICEV in the city centre and reduce the environmental and societal impact. The public transportation offer is efficient and reliable. However, there are gaps connecting travellers to mobility hubs (e.g. tram and metro stations). Thus, the AM seeks to capture first- and last-mile travellers that would have driven to reach a train or a tram station. The modal shift to be studied in this scenario concerns the share of journeys within an intermodal trip that connects to or from a train/tram station. The deployment of AM would reduce emissions.

We consider that for the AM to meet the travel demand and remain competitive the waiting time is less than 4 min. Hence, there is an increase in VKM due to pooling and rerouting to pick-up and drop-off passengers (ITF, 2020; Jones & Leibowicz,