Market development of autonomous driving in Germany

Transportation Research Part A 132 (2020) 882–910

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Transportation Research Part A journal homepage: www.elsevier.com/locate/tra

Market development of autonomous driving in Germany Bernd Kaltenhäusera,c, , Karl Werdichb, Florian Dandlc, Klaus Bogenbergerc ⁎

T

a

Baden-Wuerttemberg Cooperative State University, Department of Technical Management, Villingen-Schwenningen, Germany Steinbeis Transfer Center Applied Methods of Project Management, Villingen-Schwenningen, Germany c University of the Federal Armed Forces Munich, Department of Traffic Engineering, Neubiberg, Germany b

ARTICLE INFO

ABSTRACT

Keywords: Autonomous vehicles Self-driving cars Self-driving taxis E-mobility Discrete system dynamics model

This paper presents a model to predict the market penetration of autonomous cars for passenger transportation, focussing on autonomous taxis without a steering wheel. For this, a discrete system dynamics model was created and evaluated where the input parameters have been taken from the literature and a survey. In this survey, user demands were investigated among 873 participants. It showed that gender, age, job situation, city size and monthly income have an impact on the trust in the technology of autonomous driving as well as on the willingness to use autonomous taxis. In contrast, no evidence of an impact was found for the respondent’s education or the numbers of adults or children in the household. The results indicate that the majority of autonomous driving vehicles will be private and will have a steering wheel within the scope of this study, which covers the time frame until 2040. These are expected to reach 12.4 million units. At the same time, autonomous taxis will enter the market. These are expected to be mainly non-controllable (without a steering wheel), reaching a maximum of 2.4 million units by 2038. This maximum mainly depends on the trust in the technology as well as on people’s willingness to give up their own car due to cost or usage factors. As an autonomous taxi supports more people on average, the total number of vehicles is expected to drop from 45.1 million to around 41.7 million by 2040. Furthermore, the percentage of people travelling primarily by taxis and public transport is expected to increase from today’s 20.0% to about 32% in 2040. Then, about 19% will use autonomous taxis at least occasionally. Lastly, the vehicle miles travelled are expected to increase by 25% with people switching from public transport to autonomous taxis.

1. Introduction At present, the automotive industry is undergoing a significant change, mainly due to the large-scale introduction of electric and hybrid drives, shared mobility, individual mobility, connected vehicles and autonomous driving. Customers are also changing their behaviour, indicated by a shrinking number of young drivers (E-mobil, 2015; Beck, 2016) and their view of a car as a means of transport rather than a status symbol (AutoScout, 2015). In return, carsharing models are used increasingly (Friedel, 2014) and providers of mobility services like ‘Uber’, ‘Lyft’, ‘Didi Chuxing’ and ‘Grab’ and ‘Ola’ (Johnson, 2017) are achieving strong growth. The biggest social changes, however, will arise from the development of autonomous driving. Initially, it will be a feature of traditional cars where the driver is still in control of the vehicle, but with the introduction of non-controllable autonomous taxis (ATs), the technology will soon have a key influence on everyday life. At the moment, the legal framework of AVs is still unclear as it

⁎

Corresponding author at: DHBW VS, Bernd Kaltenhäuser, Friedrich-Ebert-Str. 30, 78054 Villingen-Schwenningen, Germany. E-mail address: [email protected] (B. Kaltenhäuser).

https://doi.org/10.1016/j.tra.2020.01.001 Received 8 May 2019; Received in revised form 18 December 2019; Accepted 2 January 2020 0965-8564/ © 2020 Elsevier Ltd. All rights reserved.

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is a “driver’s obligation to monitor and control any kind of action taken by a vehicle system” (United Nations, 2016). For the further development of autonomous driving, the automobile industry needs legal certainty at national as well as international level. To ensure this certainty, the German government started plans for expanding the legal base to autonomous driving in April 2016 (Autobild, 2016). Indeed, autonomous driving offers several advantages: users can work while traveling (Le Vine et al., 2015), can talk to friends without being distracted from the traffic (Vallet, 2013) and save travel time (Schoettle and Sivak, 2014). In addition, children or people without a driving licence will be able to use a car without the support of an adult (Vallet, 2013). In addition, it is expected that autonomous vehicles (AVs) will have a positive influence on traffic flow stability and capacity (Talebpour and Mahmassani, 2016). Further advantages arise with people giving up their own car and switching to autonomous taxis. Besides possible cost advantages or not having to deal with finding suitable parking, autonomous taxis may feature automated refuelling and could help bring about a breakthrough in electric driving with intelligent vehicle changes on longer journeys. The diffusion rate of the technology does not only depend on the often discussed technological market maturity (Muoio, 2016) and the issue of liability (Vetter, 2016). There are rather different resistances within the population to the new technology, which are as different as the trust in the technology, the wish to drive a car actively, or financial doubts. This paper analyses these resistances using a survey with 873 participants and uses them as parameters of a discrete system dynamics model to predict the market development of autonomous driving vehicles. First, a simplified version of the model without feedback is created for the prediction. Then, additional dependencies are added to study the dynamics of the model with a sensitivity analysis. This shows the key parameters for a successful market penetration of AVs. Moreover, four additional scenarios and their output are analysed. All results distinguish between six transportation alternatives (TA), defined by their usage (private or shared) and their level of automation, as shown in Table 1. Here, transportation alternatives 1 and 2 describe the state of the art. With TAs 3 and 4, nothing changes in the basic use of the vehicle as the autopilot can be switched off at any time. With TAs 5 and 6, however, clear effects on the users arise as they cannot drive the car themselves. With TAs 2, 4 and 6, the impact on everyday life comes mainly from the fact that people no longer own a car. As the alternatives serve different user expectations, they all have to be treated within in this study. To our knowledge, the prediction model presented here is the only one that distinguishes between these six transportation alternatives. Thus, its setup and evaluation are the first contribution of this paper. Further contributions are the survey results and the results obtained by the sensitivity analysis that yield the most critical factors for a successful introduction of autonomous taxis. The paper is structured as follows: first, in Section 2, a survey of the literature is shown, followed by the calculation model in Section 3, and the survey and its results in Section 4. Then, in Sections 5 and 6, the results of the prediction model and a sensitivity analysis are presented, followed by a final conclusion in Section 7. 2. Literature review In the following, a brief overview of the literature on forecasting the number of autonomous vehicles as well as on surveys for assessing public opinions and concerns regarding autonomous driving is given. 2.1. Forecasts on AVs Little research has been performed on forecasts regarding the market penetration of AVs. Most of it has been done by consulting firms, mainly studying the case of the USA. The studies by Litman (2014, 2018) and Bierstedt et al. (2014) estimate the percentage of private level 4 vehicles to be at least 25% in 2035 or 30% in 2040, respectively. Longer term forecasts predict (nearly) 100% after 2050 (IHS Automotive, 2014), 2055 (Shanker et al., 2013) and 2060 (Rowe, 2015). An intermediate value is given by Bansal and Kockelman (2017), who predict a market penetration of 24.8–87.2% in 2045, depending on level 4 technology prices. These studies examined mainly transportation alternative 3 and the respective numbers will be used for a determination of the parameters in our model. A very complex model to simulate the market penetration of AVs has been introduced by Nieuwenhuijsen et al. (2018). As the sensitivity of their model is relatively high, the percentage of level 4 vehicles in 2040 varies between 1.2% and 29% (with even higher percentages of additional level 5 vehicles). Their model is very suitable to study effects in the market penetration process, but as the output variables react strongly to a variation of the input parameters, it is hardly possible to favour one of their scenarios. Thus, a specific prediction of the market penetration is not given there, while this is a major goal of our model (see also discussion in Section Table 1 Definition of the six transportation alternatives used in this study.

Private vehicles Shared vehicles

Not automated

Driving autonomously, but with steering wheel

Without steering wheel

TA 1 TA 2

TA 3 TA 4

TA 5 TA 6

883

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3.2). 2.2. Surveys Moreover, several surveys were accessing people’s opinions on (additional) AV pricing: 20% are willing to pay more than $7000 (109 countries, Kyriakidis et al., 2015), 25% were willing to pay $2000, $1710 and $2350 in the USA, UK and Australia, respectively (Schoettle and Sivak, 2014), 20% were willing to pay $3000 in the USA (J.D. Power, 2012) and people were willing to pay on average $7253 in Austin, Texas (Bansal et al., 2016) or $6903 in Australia (Ellis et al., 2016). Furthermore, the acceptance of AVs depending on gender, age and other factors was studied. Abraham et al. (2017) studied consumer satisfaction with the technology that is already in their vehicle. In addition, they examined how consumers are learning about in-vehicle technologies and how they would prefer to learn and which alternatives to an own car would be used. For all alternatives except trains (carsharing, ridesharing, manual or electric bike and buses), the willingness dropped steadily from people aged 16–24 years to people 75+, while the willingness to use subways and trains was almost independent of people's age. Furthermore, their results showed that younger people are more willing to accept fully autonomous vehicles while older people tended to accept only a driving assistance to reduce collisions. Using a factor analysis, Haboucha et al. (2017) found that the joy of driving, environmental concerns and Pro-AV attitudes play a significant role in estimating individual choice decisions, while technology interest and public transit attitude do not contribute. Hulse et al. (2018) asked participants on their perceptions, particularly with regards to safety and acceptance of autonomous vehicles. Here, gender, age and risk-taking had varied relationships with the perceived risk of different vehicle types and general attitudes towards autonomous cars. Participants were significantly more likely to have a positive attitude towards AVs if they were male, younger or if they perceived AVs as less risky, from the perspective of a passenger or pedestrian. In contrast, the driver status was not a significant predictor of general attitudes towards AVs. In a survey with 5000 people in 109 countries, Kyriakidis et al. (2015) found that men would be willing to pay more for automation than women. Men also seemed to be less worried about fully automated driving vehicles than women. Moreover, they found that people who currently use Adaptive Cruise Control (ACC) would be willing to pay more for automated vehicles. ACC users were also found to be more comfortable about driving without a steering wheel, and more comfortable about data transmitting. By asking participants on recently made trips, Krueger et al. (2016) found that young individuals and individuals with multimodal travel patterns may be more likely to adopt shared AVs. In another survey, Lee et al. (2017) found that age was negatively associated with perceptions, attitudes and behavioural intentions toward the acceptance and use of self-driving cars. Furthermore, factors that prevent autonomous driving have been studied in the literature. These include initial costs, licensing and testing standards, privacy, impacts and interactions with other components of the transportation system and implementation details (Fagnant and Kockelman, 2015). Moreover, liability (Fagnant and Kockelman, 2015, Underwood, 2014; Seapine Software, 2014) and safety (Fagnant and Kockelman, 2015, Schoettle and Sivak, 2014; Vallet, 2013; Seapine Software, 2014) are regarded as resistances. The usage of autonomous vehicles by non-driving, elderly and people with travel-restrictive medical conditions has been studied by Harper et al. (2016) and Schmöller et al. (2015) found that peak hours, weather conditions and the distance to the vehicle are the factors determining the usage of carsharing. A comparison with the results obtained from our survey is shown in Section 4. 3. Methodology 3.1. The calculation model A system dynamics model was developed. As all input variables are given as discrete variables by year (e.g. population numbers), the model uses discrete time steps (for the principles of discrete SD models see Ossimitz and Mrotzek, 2008; Brans et al., 1998). Furthermore, this features the usage of time-depended equations which adds additional complexity while it delivers sufficient accuracy. The key features of this model are shown in Fig. 1. Looped system dynamics models are very suitable to study effects within a system but due to their sensibility towards the input parameters, they are usually not able to make clear predictions. To combine a study of the system with the ability to make a prediction, a simplified version of the model without feedback was created first. This version avoids feedback by assessing the used parameters in a survey time-depended so that they do not have to be simulated in the calculation. This model predicts the market penetration of transportation alternatives 3, 4, 5 and 6 and also other variables like vehicle miles travelled (VMT). This model is shown in the following sections first. Afterwards, additional dependencies (feedback-loops) are added to the model and adjusted, such that the output of the model remains unchanged for the base scenario. Then, the dynamics is studied using a sensitivity analysis and based on its results, four additional scenarios and their output are analysed (see Sections 3.21 and 5). 3.2. Resistances against autonomous driving The system dynamics model is based on the idea that several hurdles have to be cleared before autonomous driving will enter the market. Of these, the technological feasibility (see Section 3.4) is often quoted in the literature, as well as the legal situation and the issue of liability (see introduction), which must still be clarified. Further resistances studied are the trust in the technology of 884

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Fig. 1. The decisive dependencies of the system dynamics model. For the evaluation of the base scenario, a linearized version excluding the red arrows was run first. In return, absolute values were determined in the survey. AVs denote autonomous vehicles, ATs autonomous taxis. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

autonomous driving (see Sections 3.19 and 4.2), the immediate availability of the vehicle (see Section 3.6), the protection of privacy (see Section 4.3), the cost of autonomous driving (see Section 4.4), the pleasure of driving (see also Section 4.4), the car as a status symbol (see also Section 4.4), the transportation of furniture (see also Section 4.4), the current stock of vehicles (see Section 3.10), the range of the vehicle (see Section 3.20), the protection of personal data (see Section 3.20), investment costs (see also Section 3.20) and antitrust concerns towards a possible monopolist (see also Section 3.20). These resistances and further parameters for the calculation (willingness to use ATs and willingness to give up the own car) are derived from the literature and a survey which is shown in Section 4. 3.3. Notation used in the model The notation used in the model is summarized in Table 2. 3.4. The introduction scenario for private and shared vehicles equipped with level 2–5 technology with steering wheel (transportation alternatives 3 and 4) Today, all vehicle manufacturers and also new potential market participants such as Google and Apple are working intensively on technical solutions for autonomous vehicles. Most of them have already announced when their technology will be available on the market. This is relatively uniformly around the year 2020: Tesla and Baidu in 2018, Apple between 2019 and 2021, Nissan (Schaal, 2013), Google, Toyota and Volvo in 2020 (cf. Muoio, 2016; Volvo, 2016), and BMW in 2021 (Condliffe, 2016). These technologies may differ, especially in the specific, predefined cases, e.g. if the respective technology is able to park a car, drives autonomous on the highway or in urban areas, etc. According to the announced dates, the year 2020 is chosen for the initial introduction of transportation alternatives 3 and 4 in this study: ts = 2020 . Technology diffusion curves are usually s-shaped (see e.g. Litman, 2014, 2018), where the first part follows mainly a linear curve. As only this time frame is studied here, the linear part of the function is sufficient. Previous studies made predictions based on a comparison with diffusion rates of other technologies. These report a value of 30% in 2040 (Litman, 2014, 2018) which yields a growth rate of 1.56%/year for the fleet and a value of 25% in 2035 (Bierstedt et al., 2017) that yields 1.43%/year. Thus, an average growth rate of 1.5%/year for the fleet is assumed for this study. This value is supported by Bansal and Kockelman (2017) who used modelling to predict a market penetration of 24.8–87.2% in 2045, which yields growth rates of 0.99–3.49%/year. t3 denotes the percentage of private AVs with a steering wheel (TA 3): 3 t

=

3 g

3 g

=0

3 g

= 0.015

× (t

2019) for t

(1)

2019

for t

(2)

2020.

(3) 885

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Table 2 Notation used within this publication (sorted in the order of appearance). Variable/parameter

Description

Section

t ts

The year observed. Used as index variable in all other variables The starting year of the introduction of AVs Percentage of private vehicles equipped with automated driving technology with steering wheel (TA 3)

3.4 3.4 3.4

Annual growth factor for TA 3

3.4

Delay between the introduction of AVs with and without steering wheel Percentage of private vehicles equipped with autonomous driving technology without steering wheel (TA 5)

3.5 3.5

Relative annual growth factor for TA 5

3.5

The duration of the introduction scenario for TA 6 Shared vehicles without steering wheel (TA 6) available The minimum age for using autonomous taxis in Germany Number of people older than the minimum age for using autonomous taxis

3.6 3.6 3.7 3.7

Number of people older than 18 years (required age to drive a conventional car)

3.7

Number of people older than the minimum age for using autonomous taxis with access to these vehicles (TA 6)

3.8

Number of people older than 18 years with access to autonomous taxis (TA 6)

3.8

Percentage of people willing to give up their own car Number of new registrations of private vehicles

3.9 3.9

Number of private vehicles equipped with automated driving technology with steering wheel (TA 3)

3.13

Number of shared vehicles Number of shared vehicles equipped with automated driving technology with steering wheel (TA 4)

3.13 3.13

Number of private vehicles being replaced by one shared vehicle Number of shared vehicles equipped with autonomous driving technology without steering wheel (TA 6)

3.13 3.13

Total number of autonomous driving vehicles Total number of vehicles including carsharing Proportion of autonomous vehicles Number of shared rides being necessary instead of one private ride Percentage of empty rides for allocation of vehicles Additional vehicle miles traveled due to empty rides Use-weighted proportion of autonomous vehicles

3.13 3.14 3.15 3.16 3.16 3.16 3.16

Percentage of people trusting in the technology of autonomous driving

3.19

3 t 3 g

Td 5 t 5 g

T at agemin

Nt10

Nt18 nt10 nt18 t

ft pt µt st

Number of private vehicles Percentage of autonomous taxi users due to cost factors Number of at least occasional users of autonomous taxis Percentage of at least occasional users of autonomous taxis Percentage of people primarily using public transport Number of private vehicles with autonomous driving technology without steering wheel (TA 5)

t t

pt5

pt3

qt qt4 rt

qt6 dt ht t

b

z w t

Vehicle miles travelled compared to 2015 Number of new carsharing registrations Number of new registrations Technological trust rate of people who are m years old in the year t

t

kt gt

m t

t

3.10 3.11 3.11 3.11 3.12 3.13

3.17 3.18 3.18 3.19

3.5. The introduction scenario for private autonomous vehicles without a steering wheel (TA 5) In addition, vehicles without a steering wheel will enter the market for private and taxi usage. As with any other new technology, the manufacturers will have to provide evidence for the legal licensing by a statistically significant operational performance. As this might be achieved simultaneously by autonomous cars with a steering wheel in everyday use, a time frame of four years is scheduled for this (Td=4) . This means that non-controllable vehicles for private and taxi use are available on the market as of 2024. As a large part of the population will be used to drive a car themselves and those who become friends with it will mainly switch to ATs, the market penetration of private non-controllable vehicles will be much slower than for controllable vehicles, if it takes place at all. Hence, a very conservatively estimated growth rate is assumed that equals 1% of the growth rate of private controllable cars. As t5 denotes the percentage of private non-controllable vehicles (TA 5): 5 t

=

5 g

5 g

=0

5 g

= 0.01

×

3 t 4

(4)

for t for t

2023

(5)

2024.

(6)

886

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3.6. The introduction scenario for shared vehicles without a steering wheel (TA 6) If users give up their own car completely, the alternative must be available any time. With the TA 3 and 5, users still own a car themselves. Hence, the availability shows no barrier for this. With autonomous taxis (TA 6), however, a ‘critical mass’ of vehicles must be reached. This could also be achieved with controllable carsharing vehicles (TAs 2 and 4), but it is more likely that these large investments will be made by companies like Waymo (Google) or Apple, who are planning to use non-controllable ATs. Thus, the following base scenario is assumed: the introduction phase will be s-shaped (see Section 3.4 and Gordon et al., 2018), represented by a cosine function. As the length of the introduction is hard to estimate, T = 15 years is chosen as a first step as other technologies like the airbag (Hüttenrauch and Baum, 2007: 55) or the ESP (Reifenpresse, 2010) needed 15 years for the market penetration at a national level. This estimation might be rough, but the financial (Section 3.20) and the sensitivity analysis (Section 6) will show that a shorter introduction is very unlikely due to cost reasons, while a longer introduction time doesn’t show a significant influence (if the delay isn’t too long). Thus, this could be a feasible scenario. With at denoting the relative availability of ATs:

at = 0 at =

for t cos((t

at = 1

ts

(7)

2023 TD + 1)· /T ) + 1 2

for t

for 2024

t

2038

(8) (9)

2039

3.7. The age of occasional users of autonomous taxis It is assumed that autonomous taxis will be used by children aged 10 and over, for example on the way to school. This assumes a relatively young age, but especially at the age of 10, students usually take a bus to school. Thus, all numbers are calculated either for people older than 10 years (for the usage of cars), denoted by the index ‘10’ (agemin = 10 ), or people older than 18 years (for the ownership of cars), denoted by the index ‘18’. Thus, Nt10 and Nt18 denote the number of people older than 10 and 18 years in Germany with population numbers from Destatis, 2015. 3.8. Population with access to autonomous taxis The number of people older than 10 and 18 years with access to autonomous taxis can be calculated from the number of people (see Section 3.7) and the availability of vehicles (Section 3.6):

nt10 = Nt10 × at

(10)

nt18 = Nt18 × at

(11)

3.9. New registrations of private vehicles It is expected that the number of new registrations of private vehicles would be constant at the level of 2015 (3.21 million per year, Statista, 2016a) within the scope of this forecast, if autonomous driving were not introduced. However, after 2024 the new registrations are annually reduced because of people switching to autonomous taxis. These people need to trust in autonomous driving ( t , see Sections 3.19 and 4.2), are willing to give up their own car ( t , see Section 4.4) and must have access to autonomous taxis (at , see Section 3.6). Hence:

ft = 3.21·106

ft = f2023 × (1

for t t

×

t

(12)

2023

× at )

for t

(13)

2024

The evaluation is shown and discussed in Section 5.7. 3.10. The total number of private vehicles Another aspect to be considered is the current stock of cars. It is rather unlikely that someone will sell a newly bought car right away to change to ATs while it is more likely that old scrapped vehicles will not be replaced by new ones. Currently, there is a stable stock of 45.1 million vehicles in Germany (KBA, 2016). Approximately 3.21 million new ones are registered each year (Statista, 2016a), which means that the same amount is scrapped or sold abroad. The division shows that a car is used for about 14 years on average. Therefore, the model includes scrapping the amount of cars that have been registered 14 years before. Thus, the number of private vehicles pt is assumed to remain constant at the level of 2015 until 2024. Then, with the introduction of ATs, the number of new registrations declines (see Section 3.9) while the number of annually scrapped vehicles is given by the vehicles registered 14 years before. Using the number of new vehicles in 2023 to be scrapped annually until 2038 and the cars respectively registered 14 years before from 2039 onwards, the number of private vehicles is given by: 887

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pt = 45.1·106

for t

(14)

2023

pt = pt

1

+ ft

f2023

for 2024

pt = pt

1

+ ft

ft

for t

14

t

(15)

2038

(16)

2039

Here, the effect that second vehicles in households might become unattractive with the introduction of autonomous taxis is neglected. The time development of the number of private vehicles is shown and discussed in Section 5.4. 3.11. Users of autonomous taxis As described in Section 3.8, ATs will be used by grown-ups and also children older than 10 years. Their number nt10 already includes access to autonomous taxis. But these people will not use them unless they trust in the technology ( t , Sections 3.19 and 4.2) and see a cost or usage advantage compared to their own vehicle ( µt , Section 4.4). Hence, the number of AT users st is:

st = nt10 ×

t

(17)

× µt

This can also be expressed as a proportion of people: t

st Nt10

=

(18)

However, this formula doesn’t distinguish between people mainly using private cars and people mainly using public transport. The time development of these numbers is shown and discussed in Section 5.1. 3.12. People without an own vehicle In 2017, 80.0% of the population had both a driving license and access to a car (Ecke et al., 2018). In return, t = 20.0% of all people travel primarily by taxis and public transport. This ratio is expected to remain constant until 2023, as it was constant in recent years (Weiß et al., 2016; BAG, 2019). From 2024 on, it will likely increase due to people switching to ATs. The growth is given by ft )/ pt and people who did not rely on public transport before (1 t 1) , who would have to but choose not to renew their car (f2023 (ft 14 ft )/pt (Sections 3.9 and 3.10) and have access to ATs (at , Section 3.8): t

= 20.0%

for t

t

=

t 1

+ (1

t 1)

×

t

=

t 1

+ (1

t 1)

×

(19)

2023

(f2023

ft )

pt (ft

ft )

14

pt

× at

for 2024

× at

for t

t

2038

2039

(20) (21)

The time development of people without an own car is shown and discussed in Section 5.2. 3.13. Number of autonomous vehicles The number of private autonomous cars with ( pt3 ) and without a steering wheel ( pt5 ) is simply given by the percentage of these vehicles ( t3 and t5 , Sections 3.4 and 3.5), multiplied by the total number of private vehicles ( pt , Section 3.10).

pt3 =

3 t

× pt

(22)

pt5 =

5 t

× pt

(23)

Before the introduction of ATs in 2024, the number of shared vehicles qt is given by the 15,400 vehicles available in 2015 (Statista, 2017) plus a 4.5% growth rate (assuming the growth rate from 2015 to 2016 remains constant). From 2024 on, it is given by the sum of autonomous shared vehicles with (qt4 ) and without (qt6 ) a steering wheel (both introduced later in this section):

qt = 15.400 × 1.045t qt =

qt4

+

qt6

2015

for 2015 for t

t

(24)

2023

(25)

2024

The evaluation of shared vehicles is shown and discussed in Section 5.4. From 2020 to 2023, all new shared cars will still be controllable autonomous ones, including the ones replaced after 2 years on average (Friedel, 2014). From 2024 on, the growth rate of shared vehicles is assumed to reduce to 2%, which is a linearized approximation of the decrease of the end of an s-curve. As before, this corresponds to people who used public transport before. At the same time, the people who trust autonomous driving will shift to ATs due to cost and availability reasons (see Section 3.6) where they will be available at the repsective timepoint. From 2039 on, no growth is expected anymore and the people using controllable shared cars are the people who do not trust AVs: 888

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qt4 = 0 for t 4 q2020

(26)

2019

(27)

= d2019 × 0.045 + d2018

4 q2021 = d2021

(28)

4 qt4 = q2021 × 1.02t

2021

4 qt4 = q2023 × 1.02t

2023

qt4 = qt4 1 ×

1 1

t

for 2022 t

× 1

×

t 1

t

2039

nt10 nt101

(29)

2023 for 2024

t

for t

t

2037

(30)

2038

(31)

that qt4

It should be noted is derived from d until 2021, while from 2022 onwards, d will be derived (see below). Currently, shared vehicles are used on average 7.7 times a day (Car2go, 2017). With the introduction of non-controllable taxis in 2024, the allocation will increase slightly and one shared vehicle will replace eight private ones (averaging 10 (Boesch et al., 2016) and 5.6–7.7 (Liu et al., 2017)). Thus:

rt = 7.7

rt = 8

for 2015

for t

from qt4

t

(32)

2023

(33)

2024

The growth of ATs is given by people switching from controllable shared cars to ATs and, additionally, by people switching from private vehicles to ATs (st , Section 3.11). From 2039 on, the same effects continue, with people switching from controllable to noncontrollable cars due to the increasing trust rate:

qt6 = 0 qt6 = qt4 ×

for t t /(2040

(1

qt6 = qt6 1 + qt4 1

(34)

2023 t) t

2040

t

)

qt4 +

+

st rt

st rt

for 2024

t

2039 (35)

st 1 rt

for t

2040

(36)

Finally, from 2022 onwards, the total number of autonomous driving vehicles dt is derived by adding up private and shared autonomous vehicles:

dt = pt3 + pt5 + qt4 + qt6

for t

(37)

2022

The time development of autonomous driving vehicles is shown and discussed in Section 5.3. 3.14. Total number of vehicles With the numbers of private ( pt , Section 3.10) and shared (qt , Section 3.13) vehicles, the total number of vehicles ht can be calculated: (38)

ht = pt + qt It is shown and discussed in Section 5.4. 3.15. Proportion of autonomous vehicles The proportion of AVs t

t

is simply given by its number (dt , Section 3.13) divided by all vehicles (ht , Section 3.14):

d = t ht

(39)

However, this value neglects if the autonomous driving function is used in controllable cars or if people drive themselves. The time evolution of this value is shown in Section 5.5. 3.16. Use-weighted proportion of autonomous vehicles To calculate the proportion of vehicle miles travelled by AVs, the numbers of private and shared autonomous vehicles ( pt3 and pt5 , and qt4 and qt6 , see Section 3.13) have to be divided by all private and shared vehicles ( pt and qt , see also Section 3.13). However, three effects have to be considered for shared taxis. 1st: every autonomous taxi replaces eight private vehicles on average (see Section 3.13) and thus travels rt = 8 (or rt = 4) times 889

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more miles. 2nd: 1/3 of all people would use an autonomous taxi only by themselves, while 2/3 would share it with others (see Section 4.3). It is assumed that these willing will initially share each third trip as it will not always be possible to combine trips by different users and after 2030, with a higher number of ATs, two of three trips. Thus, altogether: b = 5/6 for 2020 t 2030 and b = 11/15 for t 2031. 3rd: every shared taxi needs empty rides of 7.8–14.2% (Liu et al., 2017 for allocation. Here, the value of = 7.8% calculated for the case of 0.5 US$ per mile (1.6 km) fits fairly well to the cost of about 30 eurocents per kilometer (0.625 miles) for a very cheap new car (see Section 4.4). Thus, a factor of

z=1+

= 1.085

1

(40)

is chosen for the additional miles travelled due to empty rides. Considering these three effects gives the proportion VMT by autonomous vehicles tw : w t

=

w t

=

pt3 + pt5 + rt × qt

for 2020

pt + rt × qt × b × z pt3 + pt5 + rt × qt × b × z pt + rt × qt × b × z

t

for t

2023

(41)

2024

(42)

The time evolution of this value is shown in Section 5.5. However, this formula neglects the additional time necessary to pick up fellow travellers on shared rides (Fagnant and Kockelman, 2014) as only few rides are shared and people would share rides only if the trip wouldn’t take much longer (see Section 4.3). 3.17. Vehicle miles travelled Sharing rides leads to less traffic on the roads, which is assumed to start in 2024 with the large-scale introduction of ATs. Using the formulas of this effect as well as the empty rides (see Section 3.16) and dividing through the number of cars in 2015 (see Section 3.14), the VMT compared to the year 2015 t are obtained by: t

=

t

=

pt + rt × qt p2015 + r2015 × q2015 pt + rt × qt × b × z p2015 + r2015 × q2015

for 2016

for t

t

2023

(43)

2024

(44)

The time evolution of this value is shown in Section 5.6. However, this formula neglects a differentiation between urban and trunk roads, where the former will be mainly used in the first years of public autonomous vehicles, while the latter are decisive for the majority of miles travelled. 3.18. Total number of new registrations Lastly, the number of new carsharing registrations kt is given by the number of new vehicles in the respective year plus the vehicles to be replaced. So far, carsharing vehicles are replaced after 1–3 years (Friedel, 2014). If sold after 2–3 years, they are usually scrapped. As it will be very difficult to resell a shared non-controllable taxi, it is assumed that half of the vehicles are replaced after 2 years, the other half after 3 years:

kt = qt

qt

1

+

kt 2 k + t 3 2 2

(45)

The number of all new registrations gt is given by new registrations of private ( ft , Section 3.9) plus shared (kt , see above) vehicles: (46)

gt = ft + kt The time evolution of this value is shown in Section 5.7. 3.19. Survey data

From the survey, three important values are obtained for the model: the trust in the technology of autonomous driving, the share of people who would at least occasionally use ATs due to cost reasons and the share of people who are willing to give up their own car and switch to ATs due to cost reasons. 3.19.1. Trust in the technology The trust rates until 2024 could be directly accessed via the survey. For the year 2024, it is 2024 = 83.5% (see Section 4.2). Furthermore, the following assumptions are made: those who are 10 years old or younger in 2024 have a trust rate of 100% as they 890

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will grow up with the technology, while the 18-year-old drivers show the existing rate of 83.5%. The trust rate of the remaining age groups in between decreases linearly. With time, the trust rate of all age groups is assumed to increase steadily. Hereby it is expected that the trust rate of the people who are at least 18 years old in 2024 will increase to 2044 = 90% within 20 years as well (and not 100% as some people will likely never change their opinion). Similarly, the trust rate of people aged 11–17 in 2024 increases also within 20 years. Their final trust rate decreases linearly by age, i.e. 98.75% (11 years old in 2024), 97.5% (12 years old in 2024), 91.25% (17 years old in 2024). The values for the years in between are given by (for example): 14 2026

=

12 2024

+ (2026

2024) ×

32 2044

12 2024

20

= 95.9% + 2 ×

97.5%

95.9% 20

(47)

Here, denotes the trust rate of people who are m years old in the year t. For the calculation of the total annual trust rate t beyond 2024, the age-dependent trust rate is first multiplied by the number of people at the respective age, summarized, and divided by all people above 18 years. m t

t

=

i 100 Ni × t i = 18 t 100 Ni i = 18 t

for t

2025

(48)

With younger people continuously entering the market, the average trust rate rises in the whole population. 3.19.2. Autonomous taxi users due to cost factors As shown in Section 4.4, the percentage of AT users due to cost factors is:

µt = 25.7%

for t

2027

(49)

µt = 30.1%

for t

2028

(50)

Here, the year 2028 corresponds to the introduction of ATs in more rural areas (see Section 4.4). 3.19.3. People willing to give up their own car Similarly, the percentage of people willing to give up their own car due to cost reasons (see Section 4.4) is: t

= 20.8%

for t

2027

(51)

t

= 16.3%

for t

2028

(52)

3.20. Further considerations The following aspects of the introduction of autonomous taxis were examined, but it turned out that they had no effect on the base model. 3.20.1. Investment costs for the manufacturers and suppliers of autonomous taxis To estimate the costs of the introduction of ATs (TA 6), their usage, introduction time and production costs are considered. The investment cost for each AT is taken up once since it has amortized (smartpit, 2012) before its renewal after 2–3 years (Friedel, 2014). The investment costs can be calculated from the number of initial purchases and the price of 8000 euros per vehicle (10,000 euros minus 20% discount, Schlesiger, 2014). For ATs, an additional cost for the new technology has to be added, here a conservative amount of 4000 euros is chosen (see literature review). Multiplying 2.4 million ATs with 12,000 euros yields 29 billion euros. However, not only Germany, but all countries where the introduction takes place at the same time have to be considered here. Extrapolating from Germany’s 81.5 million inhabitants (Destatis, 2015) to Europe, the USA and Canada with a total of 1.1 billion inhabitants (Statista, 2016b), this yields an investment sum of 390 billion euros. This shows that the investments are basically feasible for corporations such as Alphabet (Google) and Apple with a common cash balance of $464 billion (Egan, 2017). Particularly in Asia, there are additional companies such as Baidu, which have additional financial resources to tap into the local markets. Using the 13 million ATs obtained in the maximum scenario (see Section 5.2), the total amount would increase to $2000 billion. This would definitely limit the introduction of ATs. 3.20.2. Renting out an own car In a previous study it was shown that 28.8% of interviewees would rent out their car while not using it themselves (AutoScout, 2015). This effect is neglected here, since, on the one hand, a majority of these persons might think differently as soon as the vehicle is polluted or damaged by a user. On the other hand, it is not clear what proportion of these persons would switch to autonomous taxis themselves and thus no longer have their own car available. 3.20.3. Range of the vehicle According to a study by AutoScout (2015), more than 70% of the population expect a range of 500 km for their vehicle, which is 891

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today not yet reached by electric cars. Furthermore, the comprehensive availability of electric charging stations is not fulfilled. These barriers are often equated with autonomous driving, although there is no compelling reason as autonomous vehicles do not necessarily have to be equipped with an electric drive. Moreover, autonomous cars for urban traffic could be equipped with an electric drive (as today already realised by car2go) and for other journeys with a combustion engine (as today realised by rental car companies). Beyond this, a shared AT could meet another taxi during a long distance trip and the users could switch from an empty to a charged electric vehicle. Hence, the electric drive is not a barrier to the launch of autonomous cars. 3.20.4. Protection of personal data If a shared car is booked, at least some personal data is handed over to the provider. For some people, this might be a reason for not using shared vehicles. As 90% of all car drivers were willing to hand over personal data (at least if this leads to a reduced travel time, McKinsey, 2016), and as providers not using personal data could enter the market, the protection of personal data is not regarded as a barrier to the introduction of autonomous driving. 3.20.5. Free competition and antitrust law There are already several cooperative ventures willing to conquer the future market of autonomous driving together. These include, for example, the cooperations between BMW, Intel and Mobileye (Schmidt-Lackner, 2016), between Fiat-Chrysler and Google (Fluhr, 2016) or between GM, Lyft and Cruise (Handelsblatt, 2016). As ATs need a certain availability to enter the market, it is possible that only a single provider of ATs will exist in the long term (KPMG, 2010; Ruhkamp, 2014). If several co-operations reach this critical mass, an oligopoly and thus a quasi-free competition can be expected. Further mergers of the major suppliers are likely to be prevented by the competition authorities. For this study, this means that there will likely be no monopoly that would delay the introduction of autonomous driving, for example by high costs. 3.21. The looped model To perform a sensitivity analysis, the looped system dynamics model shown in Fig. 1 was used. 3.21.1. Additional dependencies To include the looped dependencies, the following equations are added to the linearized model described in the previous sections. Here, parameters in squared brackets refer to values of the base scenario. These parameters can be understood as the proportionality factors determined in the survey. Formulating the dependencies that way ensures that the looped model produces the same results as the unlooped model for the base scenario. 3 t :

t:

= t3· = t·

Tt : =Tt ·

t 1

[

t

1]

·

[

t 1]

(53)

t 1

[

3 q6 t 1 · t6 1 3 t 1 ] [qt 1 ]

[

t 1]

(54) (55)

t 1

= t·

[z t 1 ] [bt 1 ] · z t 1 bt 1

(56)

µt : =µt ·

[z t 1 ] [bt 1 ] · z t 1 bt 1

(57)

t:

z t : =z t ·

bt : =bt ·

[qt6 1 ] qt6 1

(58)

[qt6 1 ] qt6 1

(59)

As the time scales are varied in the sensitivity analysis, the effective years have to be varied with them. This is exemplary shown here for Eqs. (7)–(9) (see Section 3.6):

at = 0 at =

at = 1

for t cos((t

ts

(60)

ts + TD TD + 1)· /T ) + 1 2

for ts + TD

t

t s + TD + T

(61) (62)

for t > ts + TD + T

Additionally, limitations like 100% for the maximum trust rate and further restrictions were put into the model, namely that the trust rate wouldn’t decrease: if t < t 1, then t : = t 1. On the one hand this seems realistic, on the other it prevents cases where e.g. 892

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accidents lead to a decreasing trust in the technology which in turn leads to less ATs. This case was studied separately as an additional scenario (see Section 6.6). 3.21.2. Input parameters and output variables To evaluate the model, the sensitivity analysis was executed for 19 error-prone input parameters and 11 output variables. The input parameters obtained from the survey are the trust rate t (see Section 4.2), the percentage of autonomous taxi users due to cost factors µt (see Section 4.4) and the percentage of people willing to give up their own car t (see Section 4.4). Further input parameters are the number of private vehicles being replaced by one shared vehicle rt (see Section 3.13), the percentage of empty rides for allocation of vehicles ( , see Section 3.16), the number of shared rides being necessary instead of one private ride (b , see Section 3.16), the minimum age for using ATs (agemin , see Section 3.7), the introduction time of ATs (T , see Section 3.6), the introduction year of AVs (ts , see Section 3.4), the delay between AVs and ATs (Td , see Section 3.5), the growth rate of controllable private AVs ( g3 , see Section 3.4) and the growth rate of non-controllable private AVs ( g5 , see Section 3.4). To determine the errors of the survey results, the 99% confidence intervals were calculated for Wewel (2014)

K1

= [Pn

K;

Pn +

K]

with

K

= z1

/2

Pn (1 N

Pn ) , 1

t,

µt and

t.

These are given by

(63)

where corresponds to the probability 1 of finding the parameter inside the confidence interval, K denotes the sampling error, Pn the respective percentages and z1 /2 the quantile of the standardized normal distribution (2.576 for = 0.01). The errors obtained for the five values of t are 26.4% ± 3.9%, 42.4% ± 4.3%, 58.3% ± 4.3%, 74.3% ± 3.8% and 83.5% ± 3.2%, where the sampling errors are shown absolute. Additionally, the final value of 2044 = 90% is examined, where the error is assumed to be relatively high as the value is quite arbitrary. Using the theoretical maximum of 100%, the error is assumed to be 100–90% = 10%. Likewise, the errors of the two values of µt are 25.7% ± 3.8% and 30.1% ± 4.0%, and for t they are 20.8% ± 3.5% and 16.3% ± 3.2% (all absolute). As the number of private vehicles being replaced by one shared vehicle rt (see Section 3.13) is an actual measured value for 2015 t 2023, it is assumed to be relatively precise and not considered in the sensitivity analysis. For t 2024 , the minimum value given by Liu et al. (2017) is 5.6 and thus the error is estimated as 8–5.6 = 2.4. Hence: rt = 8 ± 2.4 . For the percentage of empty rides for vehicle allocation ( , see Section 3.16), the maximum value of 14.2% given by Liu et al. (2017) yields an error for of 14.2–7.8% = 6.4%. Thus: = 7.8% ± 6.4% (absolute error). The number of shared rides being necessary instead of one private ride (b, see Section 3.16) is estimated via the ‘worst case’, where the same number of rides is necessary in the beginning. Thus, the error is 1 5/6 = 1/6 and b = 5/6 ± 1/6 . The minimum age for using ATs in the base scenario is set to 10. The minimum age could also be 6 years, when children start using school buses, but also 14 years due to governmental regulations. Thus, the error is set to 14 − 10 = 10 − 6 = 4. Hence: agemin = 10 ± 4 . The introduction scenario was chosen to take 15 years. However, if this was going to happen quickly, e. g. because one market actor would try to take over the market with a pioneering strategy, the time frame could reduce to 10 years. Thus, the error is set to 15–10 = 5: T = 15 ± 5. The start for introducing AVs in the large scale was set to 2020. However, sometimes companies make announcements, but don’t deliver afterwards. Thus, this date could be shifted backwards, but not forward. Two more years would be realistic. Thus: ts = 2020±20 . A similar variable is the delay between the introduction of controllable and non-controllable AVs. This delay was chosen to be four years, but two more years shifted forwards or backwards be realistic. Thus: Td = 4 ± 2 . The growth rate for private AVs with steering wheel and the relative growth rate for private AVs without steering wheel have been determined in Sections 3.4 and 3.5, respectively. For the error, the minimum and maximum growth rates of 0.99 and 3.49%/year, reported by Bansal and Kockelman (2017) are used. Thus: g3 = 1.5±1.99 0.51 %/year. As the value for ATs has been quite arbitrary, a large relative error of 80% will be used: g5 = 1% ± 0.8 % (absolute error). The input variables are summarized in Table 3. The output variables are the number of at least occasional users of autonomous taxis (st ), the number of private vehicles ( pt ), the proportion of people primarily using public transport ( t ), the number of shared vehicles (qt ), the number of ATs (qt6 , TA 6), the number of controllable private AVs ( pt3 , TA 3), the total number of vehicles including carsharing (ht ), the total number of autonomous vehicles (dt ), the proportion of autonomous vehicles ( t ), the use-weighted proportion of autonomous vehicles ( tw ) and the VMT compared to 2015 ( t ).

4. The survey 4.1. Overview Some model parameters could be taken from the literature, while especially people’s opinions on autonomous driving were determined in anonymised online survey. The questionnaire was distributed using social media (e.g. Xing and Facebook) as well as the university’s internal website (DHBW, 2018). The survey was fully completed by 873 participants (N = 873). First, ANOVAs have been performed to study the influence of gender, age, employment situation, education, adults in household, children in household, monthly income and city size on the trust in AV technology (η2024, see Section 4.2), the principle will to share a 893

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Table 3 Summary of the 19 input variables and their errors used for the sensitivity analysis. Variable

Value

Error (absolute)

Trust in autonomous driving

26.4%

± 3.9%

Trust in autonomous driving Trust in autonomous driving Trust in autonomous driving Trust in autonomous driving Maximum trust in AVs

42.4% 58.3% 74.3% 83.5% 90%

± ± ± ± ±

25.7%

± 3.8%

Will to use ATs occasionally

30.1%

± 4.0%

20.8% 16.3% 8 7.8%

± ± ± ±

b agemin T ts

Will to give up the own vehicle Will to give up the own vehicle ATs replacing private cars Percentage of empty rides Inverse sharing factor Minimum age for using ATs Introduction time of ATs Start introduction of AVs

5/6 10 15 2020

± 1/6 ±4 ±5

Td

Delay between AVs and ATs

4

Growth rate controllable private AVs

±62

1.5

±1.99 0.51

Growth rate non-controllable private AVs

1

± 0.8

t 2019 t = 2020

t = 2021

t = 2022 t = 2024 t,max

µt

2027

µt

2028

Will to use ATs occasionally

t 2027 t 2028

rt

3 g 5 g

4.3% 4.3% 3.8% 3.2% 10%

3.5% 3.2% 2.4 6.4%

±20

ride, the principle will to use ATs (μ2028, see Section 4.4) and the will to give up the own car (ω2028, see Section 4.4). All significant results (p < 0,05) are summarized in Table 4. In our study, gender showed an impact on all four studied variables. Men are showing a higher trust in AV technology, while women are more willing to share rides, more willing to use ATs instead of an own vehicle and also more willing to give up the own car. This conforms with the results of Haboucha et al. (2017), Hulse et al. (2018), and Schoettle and Sivak (2014) who found a gender dependence in their studies. In contrast, Kyriakidis et al. (2015) found this impact only on some variables and Krueger et al. (2016) couldn’t verify an impact at all. The respondent’s age (year of birth) also showed an impact on three variables in our study. People born between 1965 and 1990 show a higher trust in the technology than younger and older people. Furthermore, older people are in general less willing to share a ride and less willing to use ATs. The impact on the will to give up the own car was not significant. In previous studies, Abraham et al. (2017), Haboucha et al. (2017), Hulse et al. (2018), Lee et al. (2017), and Schoettle and Sivak (2014) also found an impact of respondent’s age on attitudes towards AV technology, while Kyriakidis et al. (2015) and Krueger et al. (2016) found an impact on some variables only. Furthermore, we studied the difference between urban and rural areas. The impact on the trust level was significant, however, but Table 4 ANOVAS studying the influence of gender, year of birth, city size, job situation and monthly income on the trust in AV technology, the principle will to share a ride, the will to use ATs and the will to give up the own car. Significant dependencies (p < 0,05) have been marked *. Response Variables

Technology trust

Willing to share

Willing to use ATs

Willing to give up car

Gender Male Female

* 86,4% 79,7%

* 86,6% 93,4%

* 29,7% 32,7%

* 18,2% 20,1%

Year of birth 1990–2000 1981–1989 1965–1980 1955–1964 1946–1954 ≤1945

* 80,8% 85,7% 90,2% 78,9% 80,0% 75,9%

* 93,1% 88,9% 86,9% 84,3% 83,9% 82,5%

* 31,2% 38,4% 32,8% 20,2% 25,0% 23,2%

18,0% 30,9% 17,3% 13,7% 25,0% 27,0%

City size

*

*

0–20 t 20–50 t 50–100 t > 100 t

82,8% 87,9% 75,8% 85,4%

93,0% 93,9% 81,0% 84,0%

* 30,5% 41,4% 30,3% 26,7%

17,2% 18,8% 16,4% 24,7%

Technology trust

Willing to share

Willing to use ATs

Willing to give up car

Job situation Unemployed Student Part-time Full-time Retired

* 81,8% 80,6% 83,7% 89,7% 76,0%

94,7% 92,5% 92,8% 88,5% 95,7%

* 34,8% 31,3% 66,7% 32,8% 6,3%

24,7% 13,8% 43,2% 15,5% 23,8%

Monthly income 0–1000 € 1000–2000 2000–3000 3000–4000 4000–5000 ≥5,000 €

*

*

*

*

77,8% 79,0% 78,8% 92,7% 93,3% 89,4%

94,1% 92,6% 82,7% 84,6% 80,0% 65,1%

30,5% 35,5% 36,0% 42,7% 40,0% 26,0%

17,4% 17,3% 16,5% 26,3% 36,7% 11,9%

894

€ € € €

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Table 5 Weighing of gender, year of birth and city size. Gender

Share in study Share in population

Year of birth

City size

Male

Female

1990–2000

1981–1989

1965–1980

1955–1964

1946–1954

≤1945

0–20 t

20–50 t

50 t – 100 t

> 100 t

56,7% 49,3%

43,3% 50,7%

26,2% 13,9%

18,8% 13,4%

22,8% 25,4%

17,4% 18,0%

10,1% 11,9%

4,7% 17,4%

57,4% 40,3%

12,7% 18,6%

7,3% 9,2%

22,6% 32,0%

didn’t give a clear indication. In contrast, people in more urban areas are less likely to share rides but they are more willing to give up their own car. We couldn’t find other studies that examined these correlations before. Studying the employment situation (unemployed, student, part-time, full-time, retired) showed that retired people show slightly less trust in the technology. In addition, they are less likely to use ATs, while people working part-time are more likely to use them. Here, Haboucha et al. (2017) as well as Schoettle and Sivak (2014) could find an impact of the employment situation. Then, the impact of the respondent’s monthly income was studied. Here, only 65% answered that optional question. They indicated that wealthier people have a higher trust in the technology, but especially the best earning people are less willing to share rides, less willing to use ATs and less willing to give up their own car. In contrast, Krueger et al. (2016) didn’t find an impact here. No evidence of an impact was found for the respondent’s education (not shown in Table 4). In contrast, Haboucha et al. (2017) as well as Schoettle and Sivak (2014) found an impact here. Furthermore, no impact could be found for the number of adults in household (Haboucha et al. (2017) found one), and also none for the number of children in household (Haboucha et al. (2017) found one while Krueger et al. (2016) didn’t). To consider the impacts of gender, age and city size on the parameters, the answers of the respective groups have been weighted according to the ratio between their occurrence in the study and the population, respectively (Bethlehem et al., 2011: 209 ff.). These percentages are summarized in Table 5. The other significant impact variables job situation and monthly income were not used here as they were correlated to gender and age. 4.2. Trust in the technology of autonomous driving Our survey yielded that already today 26.4% of the population would trust autonomous driving if it is classified as safe by the authorities. Another 34.1% would rely on the technology after they had tried it and felt safe. A further 13.8% would use it after it has proved itself for at least two years. It is assumed that autonomous vehicles will be tested by all interested people from 2020 onwards and thus the technology can prove its safety to these people. Hence, the trust rate of 26.4% in 2019 will increase linearly within three years to 74.3% in 2022. In 2025, after five years of proving, a further 9.2% can be added (see Fig. 2). As t denotes the percentage of people trusting in the technology of autonomous driving, the parameters used are: t

= 26.4%

for t

2019

(64)

t

= 42.4%

for t = 2020

(65)

Fig. 2. Survey result showing the usage of autonomous vehicles after successful safety classification by the authorities. 895

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Fig. 3. Survey result showing the willingness to share an autonomous taxi on the way to work. t

= 58.3%

for t = 2021

(66)

t

= 74.3%

for t = [2022, 2023]

(67)

t

= 83.5%

for t = 2024

(68)

The calculation of the trust rates after 2024 is shown in Section 3.19. 4.3. The protection of privacy According to a study by AutoScout (2015), 54% of the participants would not take a stranger in their car or leave it to them, in order to protect their privacy. A similar result was obtained from our study, where 33% would use an AT only by themselves or share it only with people they know. The rate is lower here as the question regarded a taxi and not an own vehicle. The survey results on sharing a taxi are shown in Fig. 3. The more people share a taxi, the more favourable it becomes in term of costs. This will be a critical factor for the success of shared taxis and is included in the calculation (see Section 4.4). 4.4. The cost of autonomous driving Autonomous taxis are used on a large scale only if their price is lower than an own vehicle, especially if people give up the own car completely. To include the cost of autonomous driving in the calculation, the costs of shared vehicles are compared to the costs of public transport (p.t.) in the following. To compare different means of transportation, two trips were chosen, one being a 12 km (7.5 miles) trip, e.g. to work, the second one a longer distance of 100 km (62 miles). To calculate the costs of these journeys for public transport, representative routes were selected and the calculated averaged costs are 2.85 euro (12 km) and 22.85 euro (100 km). Already today, shared vehicles can be rented for a just slightly higher rate, depending on traffic and booking packages (DriveNow, 2018). With ATs entering the market and their more efficient usage, prices will be reduced in the mid-term. Configuring these vehicles with an electric engine, which will likely undercut combustion engines in the near future (Els, 2016; Wüllner, 2016), would provide additional cost advantages. In addition, some users would share an AT with fellow travellers (see Section 4.3), which would further reduce costs. Therefore, the costs of ATs are assumed to be similar to the costs of public transport. These costs are lower than using an own car which costs about 30 eurocents per kilometer for a very cheap new car (ADAC, 2018). What is decisive, however, are not the exact costs of transportation, but the ‘felt costs’ which one intuitively connects with it. To examine this effect, the participants in the survey should estimate the cost of a 12 km trip to their workplace as well as a 100 km journey by motorway. Subsequently, the costs of the two trips should be estimated for public transport. This was used to determine the fraction of people who estimate the costs of public transport as lower than the costs of private transport. It was also asked whether one would prefer to do the respective trip by taxi if the costs were the same. If people stated that they would use ATs even if they were more expensive, they were assumed to use them, independent of their cost estimation. If they would use them if they were equally priced, they had to estimate the costs of public transport as lower or equal. If they stated that the costs must be more favourable, they had to estimate the costs of public transport as lower. If it was stated that one would rather use public transport or the own car, it was be assumed that they would use autonomous taxis not even used occasionally (see Table 6). For the 12 km journey, a total percentage of 25.7% would at least occasionally use autonomous taxis. This value was used for the 896

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Table 6 Methodology to determine if a person would at least use ATs due to cost reasons. The eventual users of ATs are tagged in green. These are the people who would use taxis always or at least occasionally, depending to the individual cost estimation.

first six years of the calculation where bigger cities are accessed. From the 7th year on, a value of 18.1% was used, which is calculated by averaging the values of both trips (12 km and 100 km), since the rural regions are also accessed then. The value determined here is slightly lower, as public transport travel costs were by trend estimated as higher for long-distance travel. The percentage of AT users due to cost factors is thus given by:

µt = 25.7%

for t

2027

(69)

µt = 30.1%

for t

2028

(70)

Furthermore, the proportion of people who would even give up their own vehicle and switch to autonomous taxis due to cost factors was estimated in a similar way. If people would change to ATs even if they were more expensive, they were assumed to do so, independent of their cost estimation. If they would change if it was equally priced, they had to estimate the costs of public transport as lower or equal. If they stated that the costs must be slightly or clearly more favourable, they had to estimate the costs of public transport as lower. This is shown in Table 7. Table 7 Methodology to determine the share of people who would give up their car and change completely to ATs (tagged in green). These are the people who stated that they would change under certain cost conditions, combined with their respective estimation of public transport costs.

The cost estimate for 12 km yields that 20.8% of the participants would be willing to give up their own car, while taking the mean with 100 km shows 16.3%. The first value is used analogously to the estimation above for the first 6 years, while the second one is used from the 7th year onwards. Hence, the percentage of people willing to give up their own car is: t

= 20.8%

for t

2027

(71)

t

= 16.3%

for t

2028

(72)

The advantage of this evaluation is that frequently mentioned reasons against ATs such as ‘driving fun‘ or ‘car as status symbol‘ as well as parameters like transportation networks or the job market are already contained in the question of the willingness to give up the own car and therefore do not have to be evaluated separately. A similar case is the occasional transport of furniture with an own vehicle where providers will offer suitable solutions (e.g. by means of special vehicles for specific purposes). 5. Prediction results In this section, the results that were predicted using the model and the survey described in Sections 3 and 4 are presented. 897

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Fig. 4. Calculated number and share of occasional users of non-controllable taxis in Germany.

5.1. Users of autonomous taxis The number and share of at least occasional users of ATs are derived in Section 3.11 and shown in Fig. 4. It can be seen that the number of occasional users is increasing, particularly as a result of the 15-year introduction scenario of noncontrollable taxis. Other effects like the trust in the technology do not significantly alter the shape of the curve. The maximum value (within the forecast of this study) is 19 million users, which equates to 27% of the population. This value results from the trust in the technology as well as the personal cost or usage advantage. The number of registered users will be higher as some people are registering without using the offer afterwards, as it is today.

Fig. 5. Calculated share of people travelling primarily by taxi and public transport. The effect of the introduction of ATs affects the curve with a time delay, which is mainly a result of the current stock of vehicles. 898

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Fig. 6. Calculated number of autonomous driving vehicles in Germany. The number of private vehicles exceeds the number of shared vehicles. On the other hand, as shared vehicles support more people on average, they will have an important impact on everyday life.

5.2. People without an own vehicle The share of people travelling primarily by taxis and public transport is derived in Section 3.12 and plotted in Fig. 5. This share increases from today’s 20.0% to a maximum of about 29% within the scope of this study. This maximum depends on the willingness to give up the own car and the trust in the technology. The long rise between 2030 and 2040 corresponds mainly to the stock of vehicles that have to be scrapped first. This also shows that the current stock of vehicles has a significant effect on the market penetration of ATs. Thus, state incentives for scrapping vehicles could help ATs to a faster market penetration.

Fig. 7. Calculated number of private controllable and non-controllable vehicles. Private AVs are expected to be mainly equipped with a steering wheel, and to reach 12.4 million units within the scope of this study.

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Fig. 8. Calculated number of shared controllable vehicles and ATs in Germany. In comparison to private vehicles, shared ones are mainly noncontrollable. They will reach their maximum (within the scope of this study) of 2.4 million units already in 2038. The number of controllable shared vehicles will decline mainly after 2026 with autonomous taxis taking over the market.

5.3. Number of autonomous vehicles The numbers of private and shared, as well as controllable and non-controllable vehicles are derived in Section 3.13. First, in Fig. 6, the number of private AVs is compared to the number of shared AVs. The starting point of shared AVs is mainly given by technical and subsequent legal availability, while the growth rate is primarily given by the introduction scenarios. Finally, the maximum value of shared AVs in 2038 mainly depends on the trust in the technology as well as on the willingness to give up the own car. For a more detailed view, the numbers of private and shared vehicles are split into controllable and non-controllable ones in Figs. 6 and 7, respectively. Fig. 7 reflects the assumption that private AVs will mainly be equipped with a steering wheel. They are expected to reach 12.4 million units within the year 2040 (the maximum within the scope of this study). Although the market penetration is assumed to be

Fig. 9. Calculated number of vehicles in Germany. The majority of vehicles remains private within the scope of this study. While the private vehicles are continuously decreasing from 45.1 to 39.3 million, the number of carsharing vehicles is increasing to 2.4 million.

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Fig. 10. Calculated proportion and use-weighted proportion (VMT) of autonomous vehicles in Germany. While the proportion of autonomous vehicles increases linearly due to the introduction scenario of private vehicles, the use-weighted proportion is increasing faster with the introduction of autonomous taxis that support more people on average.

linear, the curve is concave, as the total number of private vehicles is decreasing due to people switching to ATs. Fig. 8 shows that ATs are expected to reach their maximum of 2.4 million by 2038. The value of 2.4 million corresponds mainly to results from the survey, namely the trust in the technology as well as the personal cost or usage advantage. The number of shared controllable vehicles, which is negligible compared to the other autonomous cars, has its maximum in 2021 with a stock of ~20,000 vehicles. Subsequently, the number decreases again, as most of the previous users will switch to ATs, and only a small amount of controllable vehicles has to be kept for the people not trusting autonomous driving.

Fig. 11. Calculated relative VMT in Germany compared to 2015. First, the VMT increase due to people switching from public transport to ATs. Due to a declining population in Germany, the relative VMT are expected to drop again after 2036.

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Fig. 12. Calculated annual new registrations of private and shared vehicles in Germany.

5.4. Total number of vehicles The numbers of private and shared vehicles are derived in Sections 3.10 and 3.13, and the total number of vehicles in Section 3.14. They are all shown in Fig. 9. The calculation indicates that the total number of 45.1 million vehicles drops to around 41.7 million vehicles in 2040, while the number of shared vehicles increases to 2.4 million. It should be noted that carsharing vehicles support more people on average and thus have a higher impact on vehicle miles travelled than privately owned cars (see Section 5.5). 5.5. Proportion and use-weighted proportion of autonomous vehicles The use-weighted proportion (vehicle miles travelled) of AVs is compared to the proportion of AVs in Fig. 10. These magnitudes are derived in Sections 3.15 and 3.16. While the proportion of AVs increases almost linearly, the relative vehicle miles travelled increase steeply after 2026. This corresponds mainly to the introduction of ATs, which as they support more people on average. The slope of this curve depends on the introduction scenario and also data from the survey, namely the trust in autonomous driving and the willingness to use ATs. 5.6. Vehicle miles travelled The formula to calculate the relative vehicle miles travelled compared to 2015 was derived in Section 3.17 and is shown in Fig. 11. The calculation indicates that the vehicle miles travelled increase by 25% with people switching from public transport to ATs. After 2036, the VMT are expected to drop again, which corresponds to the declining total population in Germany. 5.7. New registrations The formulas for the calculation of new registrations of private, public and all vehicles were derived in Sections 3.9 and 3.18. Their time evolution is shown in Fig. 12. It can be seen that the first introduction of non-controllable taxis in 2024 might lead to additional new registrations. However, at the same time, new registrations of private cars are expected to decline as people switch to ATs. This behaviour continues until private new registrations have dropped from 3.21 to 2.6 million per year. On the one hand, an AT supports more people on average; on the other hand, it has to be replaced after just 2–3 years and also people who used solely public transport before start using them. As the total number of new registrations increases, the two latter effects are more dominant here. 6. Sensitivity analysis To study the effects and dependencies in the looped system dynamics model, a quasi-Monte Carlo simulation. The parameter 902

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Table 8 Variance of the 11 output variables in 2040. The last column shows the relative variance of the output parameters when all inputs are varied at the same time. The other columns show the contributions of the most important input parameters to this variance and thus how much the outputs are chaning, when only single parameters are varied. The (+) and (−) signs indicate positive or negative correlations. Contributions < 4% are not shown. Variable

t w t

ht pt t

dt t

st

pt3

qt qt6

Start intro-duction of AVs: ts

VMT compared to 2015 Use-weighted proportion of AVs

Delay between AVs and ATs: Td

Inverse sharing factor: b

(+) 4% (+) 4%

(+) 5% (+) 13%

11% 18,8%

154,7% 156,6%

Total number of vehicles Number of private vehicles Proportion of AVs Total number of AVs Percentage of people primarily using p.t. Occasional users of ATs Controllable private AVs (TA 3)

(+) 7% (−) 12%

(+) (+) (+) (+) (−)

21% 29% 24% 61% 80%

(+) (+) (+) (+) (−)

(−) 14% (+) 11%

(−) 108% (+) 94%

(−) 32% (+) 47%

Number of shared vehicles Number of ATs (TA 6)

(−) 14% (−) 14%

(−) 108% (−) 109%

(−) 31% (−) 31%

ATs replacing private cars: rt

7% 9% 29% 35% 25%

Relative variance

31,4% 43,0% 55,8% 106,1% 115,8%

(−) 12% (−) 12%

164,7% 165,8%

values were determined with a 19-dimensional Sobol sequence (Saltelli, 2008) with 1024 samples. This ensures that the possible parameter values are combined the most effective way in the 19-dimensional space. The outcomes are summarized in this section. 6.1. Input-output correlation First, all inputs were varied at the same time in the quasi-Monte Carlo simulation. With this, the relative variance of the 11 output variables was calculated (see Table 8). This shows how much the output variables are changing when the input parameters are varied at the same time. It can be seen that the relative variance is very different for the outputs. Especially the vehicle miles travelled and the use-weighted proportion of AVs are quite stable. On the other hand, the number of ATs and thus the number of all shared vehicles are subject to a significant change when varying the input parameters. Moreover, the numbers of private AVs are changing significantly, as an increasing number of ATs leads to decreasing number of private vehicles. Then, the contribution of each single factor to these variances was determined (see also Table 8). Descriptive speaking, this shows to which extent the output parameters are changing when single input parameters are varied. The (+) and (−) signs indicate the direction of variation. Not shown in the table are all trust rates t , the willingness to use ATs ( µt ), the willingness to give up the own car ( t ), the minimum age of AT users (agemin ), the growth rates for private AVs ( g3 and g5 ), the duration of the introduction of ATs (T ) and the number of empty rides for allocation, as they all didn’t contribute significantly (< 5%) to the output. These parameters are thus only important for the evolution, but not for the result in 2040. Also the initially assumed rate for a replacement of private cars by shared ones contributes only slightly to the outcome of the model. In contrast, the output is mainly determined by the delay between the introduction of AVs and ATs (Td) , the inverse sharing factor (b) and to less extent to the start of the introduction of AVs (ts ). The results can be briefly summarized: 1st: a later introduction of AVs and a longer delay between the introduction of AVs and ATs lead to more privately owned AVs and less shared ones. Additionally, an earlier introduction would lead to less traffic. Thus, it will be very important to remove any obstacles that could prevent the usage of AVs and ATs quickly; especially, their usage has to be legislated and liability must be issued. 2nd: Higher sharing rates of ATs lead to more ATs and less privately owned vehicles as the travel costs decline and thus more people switch to ATs in return. The results reflect the simple fact that higher sharing rates lead to less traffic. Thus, the providers of ATs have to introduce incentives for sharing rides from the beginning. 6.2. Alternative scenarios To determine interesting scenarios to be studied, a scatter plot showing the number of ATs (TA6) in 2040 as a function of the delay between AVs and ATs is created. It is shown in Fig. 13. From this scatter plot, four alternative scenarios are derived. As these scenarios show corner point solutions, the main goal here is not to present precise numbers, it is furthermore to present the principal cases and under which conditions they occur. For clarity, only their three most important input parameters are shown in Table 9. The first scenario is the one with the highest number of ATs (max scenario, see Section 6.3). The second one is the scenario with the lowest number of ATs (min scenario, see Section 6.4). The third scenario uses the same parameters as the min scenario, with additional cases like a dwindling trust rate being allowed (see Section 6.5). The fourth scenario is a scenario that produces the same 903

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Fig. 13. Scatter plot showing the number of ATs in 2040, depending on the delay between the introduction of AVs and ATs. Here, the upper marks show scenarios where ATs come to a breakthrough while in the lower marks they are caught in a dwindling spiral. The base scenario as well as the max-, min-, and cancel-out scenario are marked in red colour. Furthermore, the impact of the delay can be clearly seen. For a critical delay of about 5.3 years, it is almost impossible to get a breakthrough of ATs. Thus, it is very important to legislate ATs as soon as possible. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Table 9 Most important parameters of the four alternative scenarios. The other parameters used were also determined in the Sobol sequence.

ts Td b

Variable

Base scenario

Max scenario

Min scenario

Cancel-out scenario

Start introduction of AVs Delay between AVs and ATs Inverse sharing factor

2020 4 0,83

2020 2,85 0,75

2022 9,3 0,87

2020 4,73 0,72

number of ATs as the base scenario. Here, it might be the case that the effects of varied parameters cancel each other out (cancel-out scenario, see Section 6.6). 6.3. The maximum scenario This scenario shows a very successful market penetration of ATs (see Fig. 14). It occurs due to more sharing of vehicles and a shorter delay between AVs and ATs. In this scenario, a quickly increasing number of ATs induces a higher trust rate and also an increasing willingness to use ATs due to reduced costs. In return, people tend to give up the own vehicle. Without the 14-year delayed scrapping of vehicles, this would happen instantly here. However, it should be noted that this scenario would need around 2000 billion euros to be financed in Europe and North America (see Section 3.20). This limitation is not included in the calculation and thus it is very unlikely that this scenario could occur so quickly. 6.4. The minimum scenario In this scenario, the market penetration of ATs is not successful, as shown in Fig. 15. This scenario occurs mainly due to a high delay between the introduction of AVs and ATs (see Fig. 13), combined with a later introduction of AVs and less sharing. In this scenario, ATs will also enter the market, but very slowly, corresponding to the low willingness to use ATs instead of an own vehicle due to high costs. Also here, the trust rate is relatively high and leads to a successful market penetration of privately owned AVs.

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Fig. 14. The main outcomes of the max scenario. Here, the number of autonomous taxis (top left) is increasing quickly, due to an increasing trust rate (middle left) and an increasing willingness to give up the own car (bottom right). This leads in return to a dwindling number of privately owned AVs (top right).

6.5. The unconstrained minimum scenario This scenario uses the same input parameters as the min scenario shown in Section 6.4. In contrast, the trust rate is unconstrained here (see Fig. 16). This leads to a dwindling spiral of trust rate, number of ATs and thus the willingness to use ATs. Compared to the constrained min scenario, the vanishing trust rate prevents ATs from entering the market at all. Just until 2024, the trust rate is sufficiently high for people buying private AVs. This scenario can for example occur if there are too many accidents caused by AVs and thus people loosing trust in the technology. 6.6. The cancel-out scenario The fourth scenario is a scenario that produces the same amount of ATs in 2040 as the base model. Here, it shall be studied if some modified parameters cancel each other out. Namely, these are the slightly reduced sharing rate and the slightly increased delay between AVs and ATs. For this, the number of private AVs is shown in Fig. 17. Here, the number of AVs differs completely from the base scenario. Thus, the effects cancel each other out in terms of ATs, but regarding the other variables, the scenarios are completely different. 905

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Fig. 15. The main outcomes of the min scenario. The number of autonomous taxis (top left), the number of privately owned AVs (top right), the percentage of people trusting AVs (middle left), the percentage of AVs (middle right), the share of at least occasional users of ATs due to cost factors (bottom left) and the percentage of people willing to give up their own car (bottom right), all calculated for the min scenario.

7. Conclusion 7.1. Conclusions from the prediction model Based on the technological, legal, economic and social resistances to autonomous driving, a model predicting the market penetration of autonomous vehicles in Germany was developed. In particular, it indicates that autonomous taxis (ATs) will successfully enter the market during an assumed 15-year long introduction scenario. Here, the analysis showed that the introduction could also take longer without a significant change in the market share, while a faster market development is unlikely due to high costs. Then, the maximum of the 20-year prediction period is achieved, which is mainly the result of the trust in the technology, the desire to own a vehicle and the individual cost estimation. This maximum is an estimated 2.4 million shared vehicles, supporting approximately 19 million people or 23% of the population. In the private sector, the majority of autonomous vehicles (AVs) is expected to be equipped with a steering wheel. Here, the market penetration is expected to start in 2020 and increase quickly so that a market share of 31.5% is reached in 2040. In contrast to this, an extremely slow market penetration of non-controllable vehicles in the private sector is assumed, as there are strong resistances to the technology in the near future, and those who are positive about the introduction will tend to switch to ATs rather than buying a non-controllable car themselves. The more efficient resource allocation with ATs results in a reduction of the vehicle fleet in 906

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Fig. 16. The number of autonomous taxis (top left), the number of privately owned AVs (top right), the percentage of people trusting AVs (middle left), the percentage of AVs (middle right), the share of at least occasional users of ATs due to cost factors (bottom left) and the percentage of people willing to give up their own car (bottom right), all calculated for the unconstrained min scenario.

Germany from today's 45.1 million vehicles to 39.3 million private and 2.4 million shared vehicles. A sensitivity analysis showed that the market share can be strongly influenced by the early introduction of AVs and ATs and that sharing rides can help ATs to a breakthrough. The study also indicated that vehicles miles travelled will increase with the introduction of ATs and thus, autonomous driving will have a significant effect on congestion. Although AVs can be routed to prevent congestion, clear conclusions cannot be drawn here yet, as the local character of urbanization and infrastructure are not within the scope of this model. 7.2. Conclusions from the survey Furthermore, the survey conducted within the range of this study yielded that men are showing a higher trust in AV technology, while women are more willing to share rides, more willing to use ATs instead of an own vehicle. Moreover, people born between 1965 and 1990 show a higher trust in the technology than younger and older people and older people are in general less willing to share a ride and less willing to use ATs. In addition, the survey yielded that people in more urban areas are less likely to share rides but they are more willing to give up their own car. Finally yet importantly, wealthier people have a higher trust in the technology and especially the richest people are less 907

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Fig. 17. The scenario which produces the same result for ATs as the base scenario. Although showing a similar number of ATs, other parameters like the number of private AVs are completely different and thus the parameters do not simply cancel each other out.

willing to share rides, use ATs and give up their own car. 7.3. Policy implications As shared taxis might lead to more traffic, it is questionable if their market penetration should be pushed by governments. But on the other hand, they feature other advantages like an increased road safety or the mobility of elder people. In addition, less traffic could be reached using a more intelligent routing of ATs. Furthermore, as less vehicles would be required in total and ATs travel most of the time, less parking areas would be required. Hence, the weighing up of the pros and cons would be up to the authorities. If they should favour to push autonomous driving, it will be very important to remove any obstacles that could prevent the usage of AVs and ATs as soon as possible. First, the technical feasibility must be achieved quickly, where mainly car manufacturers and new market participants like Waymo are challenged, but also governments should push here significantly with incentives. As the increased VMT correspond mainly to people switching from public transport to ATs, it should be an important political goal to focus on people switching from private cars to ATs, while the ones using public transport should continue using it. For this, it is necessary to reduce public transport fares so that they significantly undercut the prices of ATs. The sensitivity analysis yielded that the time delay between the introduction of AVs and ATs is the most critical parameter for the successful large-scale introduction of ATs. Especially, VMT can be reduced with a quick introduction of ATs. Thus, governments are required to legalize the usage of non-controllable ATs as soon as possible by forming a statutory framework and allowing liabilities to be issued. Additionally, higher initial ride sharing rates can help pushing the market penetration of autonomous taxis and also reduce VMT. Hence, suppliers of ATs should to introduce incentives for sharing with the introduction to the market, for example by significantly reduced travel costs. Policy makers can contribute here with giving incentives to the suppliers. Furthermore, as losing the trust in the technology of autonomous driving could lead to a dwindling spiral in the usage of ATs, governments are also required to set clear quality and safety standards to be fulfilled by car manufacturers to prevent this scenario. 7.4. Outlook Focusing on the German market is, however, only reasonable in the first step, since the 3.21 million new registrations should be set again 78 million worldwide (Steiler, 2015) and pilot projects are already underway, not just in often cited California, but also for example in Singapore and Pittsburgh, Pennsylvania (Peer, 2016; Alvarez, 2016). The next step will be a traffic simulation with the results of this study to analyse the impact of autonomous cars on traffic and roadworks. Another further research subject could be the role of the traditional car manufacturers within the new business models. Additionally, the impact of the results presented here on the automotive sector as well as service workplaces, and thus on the labour market and the economy can be analysed. Furthermore, social impacts due to different user behaviour as well as impacts on the urban landscape might be analysed.

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