Skip to main content

An Open Access Journal

The opportunity of shared autonomous vehicles to improve spatial equity in accessibility and socio-economic developments in European urban areas

Abstract

Background

This paper provides insight into the opportunity offered by shared autonomous vehicles (SAVs) to improve urban populations’ spatial equity in accessibility. It provides a concrete implementation model for SAVs set to improve equity in accessibility and highlights the need of regulation in order for SAVs to help overcome identified spatial mismatches.

Methodology

Through the formulation of linear regression models, the relationship between land-use and transportation accessibility (by car and public transport) and socio-economic well-being indicators is tested on district-level in four European cities: Paris, Berlin, London and Vienna. Accessibility data is used to analyse access to points of interest within given timespans by both car and public transport. To measure equity in socio-economic well-being, three district-level proxies are introduced: yearly income, unemployment rate and educational attainment.

Results

In the cities of Paris, London and Vienna, as well as partially in Berlin, positive effects of educational attainment on accessibility are evidenced. Further, positive effects on accessibility by yearly income are found in Paris and London. Additionally, negative effects of an increased unemployment rate on accessibility are observed in Paris and Vienna. Through the comparison between accessibility by car and public transportation in the districts of the four cities, the potential for SAVs is evidenced. Lastly, on the basis of the findings a ‘SAV identification matrix’ is created, visualizing the underserved districts in each of the four cities and the need of equity enhancing policy for the introduction of SAVs is emphasized.

1 Introduction

The European perspective is optimistic and pushes policy to facilitate the research, testing and introduction of autonomous vehicles (AVs) [24]. But what role are AVs to play? Are they mostly set out to be present in long-haul trips, to improve freight or can they have a role in urban areas as well? And, if so, what role are they supposed to play?

No matter if people live in urban, suburban or rural areas, the means of transportation available to them and the commute time to their workplace, local schools or hospitals can be essential for a person’s socio-economic wellbeing. The latter is a multi-dimensional concept as defined by the European Commission and the Human Development Index covering indicators on amongst others education, employment, income and health ([29], pp. 8, 42 [54];). In general, urban areas show a higher density of locations of interest or opportunities including workplaces, shops, schools as well as health centres and hospitals [46]. This paper focuses on four functional urban areas (FUA), as defined by the EU and the OECD and their political cores [11, 21]. While it is generally recognised that FUAs are associated with greater economic growth and development than rural areas, it is not clear whether all inhabitants share the same opportunities offered by the denser infrastructure of FUAs.

Nowadays most countries “transport policies generally aim to improve accessibility and reduce the negative impacts of motorised transport” ([27], p. 474). Thus, accessibility has become a central concept in spatial and transportation planning ([15], p. 3). Geurs & van Wee, [16] «define accessibility as the extent to which land-use and transport systems enable (groups of) individuals to reach activities or destinations by means of a (combination of) transport mode(s)» (p. 128). The authors also identify four components of accessibility in the literature: (1) land-use, (2) transportation, (3) temporal and (4) individual [16]. Every component of accessibility can be distributed inequitably, and in turn, transportation and spatial planning policy affect equity in accessibility and cause socio-economic developments.

Here, it is noteworthy to say that there are various understandings of the concept of equity, different types of equity, various indicators and criteria to measure it and to categorize people into classes [25, 49, 57]. For example Ramjerdi [43] evaluated an equity objective for road pricing schemes in Norway. His research highlights the difficulty to assess equity on single measures yielding contradictory results and shows the sensitivity of these measures to the geographical level of analysis.

Further, there are multiple analysis on accessibility measures in European transport appraisals: Geurs, Boon, & Wee [14] developed a theoretical framework to describe the relationship between determinants of social impacts of transport, compare UK and Dutch UK transport appraisal guidelines and come to the conclusion that social impacts of transport appraisals are still far from being as complete as economic and ecological assessments. Lucas [26] and Halden [18] argue that due to the great flexibility in UK policy to assess accessibility, most local authorities struggle to find the right range and choice of calculation and that their mapping tools downplay the complexity and barriers causing social exclusion (cit. in [15]). Consequently, Pitarch-Garrido [37] for example, suggests the concept of spatial equity as indicator for social sustainability in transportation policy and uses time-distance to measure socio-spatial equity in Valencia.

More globally, Portnov et al. [38] were able to showcase that accessibility plays an important role in development when comparing accessibility in Swiss municipalities over the second half of the twenty-first century. Along those lines the ITF [21], was able to show “a correlation between income and accessibility by public transport” in the Paris FUA on a 500 m2 grid-level analysis (p. 64), highlighting inequity in accessibility along social and spatial lines.

Of recent, research has begun to analyse the effects of shared mobility as well as autonomous vehicles in regards to their effects on accessibility and equity: Clark & Curl [5], as well as Boldrini et al. [3] analysed travel data of car- and bike sharing-data in Glasgow and in other 10 European cities, respectively. While they outline the potential of shared transportation in overcoming barriers of access such as upfront cost or maintenance, they showcase that only a small percentage of the population is making use of these services. These are mostly highly educated, middle to high income individuals who use shared mobility as a substitute for other means of transport. Pritchard, et al. [39] came to similar results in their assessments of bike-sharing in Sao Paolo, Brazil and its potential to alleviate spatiotemporal inequality in job accessibility, as measured by Gini coefficients. These findings highlight that car-hailing users increasingly substitute public transportation trips, wherein the current most well-off users put “convenience over cost” ([6, 12], p. 5) and therefore, bear the question on whether the introduction of Shared Autonomous Vehicles (SAVs) will reinforce similar trends.

Level 4 and 5 AVs are already being tested on public roads in several countries including the US, Singapore and many European countries and bring many benefits to accessibility [47]: AVs promise to improve road safety, by reducing crashes and by optimising traffic at large, improving reliability, which in turn reduces congestion and travel time [55, 56]. Their deployment has the potential to reduce pollution rates in urban areas as they are primarily electric vehicles and if the infrastructure is well connected, AVs offer an opportunity to decrease energy consumption [8]. Lastly, AVs are expected to strongly lower the cost of travel. On the one hand the cost per kilometre is reduced as the cost of a driver and most of the operating cost of vehicles are eliminated, especially if implemented in shared schemes [4]. On the other hand, AVs offer the opportunity to use the time of travel productively and have a 24-h service. Therefore, Pendleton et al. [35] argue that AVs, as part of shared mobility, make access to mobility more affordable and could strongly benefit neighbourhoods with lower accessibility.

However, there is a range of risks concerning shared mobility and AVs: Due to the lower cost of travel, they bear the risk of accelerating the urban sprawl [12]. In addition, the spatial extension of SAVs will be limited, given the market imperative that lies at the core of these on-demand systems [5]. Also, SAVs bear the risk of technical unemployment in the mobility and transportation industry, industries that mostly offer low-skill jobs and are mainly held by marginalised communities, will be displaced by high-skilled tech jobs [36, 53, 59]. Lastly, SAVs also bring barriers of access best described by the STEPSFootnote 1 model created by Shaheen et al. [45] and adapted to include the usage of AVs by Fleming [12]. Overall, these findings raise the question:

1.1 Can shared autonomous vehicles improve equity in accessibility of European urban populations?

This paper proposes a three-step model for the introduction of SAVs which helps to address equity in accessibility and showcases concrete case-studies on accessibility in the European cities of Berlin, Paris, London and Vienna. Through the analysis of accessibility to shops within 30 min by car and public transportation and relating it to district-level socio-economic well-being indicators, it builds and tests the findings by the ITF [21], as well as Portnov et al. [38] and Pitarch-Garrido [37] and spatial mismatches are identified. Furthermore, it offers a tool to visualize city districts in which SAVs can aid to overcome identified spatial mismatches and contributes to the discussion of possible socio-economic developments caused by the introduction of SAVs and how these should be addressed.

2 Methodology and data

This analysis builds on four main underlying assumptions: Firstly, accessibility for low-income populations is improved through more affordable car travel [23]. Secondly, SAVs are expected to make car-travel more affordable [4]. Thirdly, SAVs offer an opportunity to improve equity in accessibility. And lastly, SAV travel needs to be regulated in order to improve equity in accessibility and bear socio-economic benefits [7, 12]. According to this logic, SAVs have the greatest opportunity to improve city populations’ socio-economic well-being if deployed in underserved, low-income areas. In order to identify such areas, the authors propose a three-step model (see Fig. 1).

Fig. 1
figure 1

Three-step process to identify SAV districts

Step 1. entails showing that a relationship between accessibility and socio-economic well-being exists, by applying the four OLS regression models explained in Section 2.1. These relationships lay the ground for data-driven policy decisions. Step 2 ensures that accessibility is improved in city districts that will benefit the most from the deployment of SAVs: If the number of shops accessible within 30 min is greater in a district by car travel than by public transportation, AVs and SAVs offer room for improving the inhabitants’ accessibility. Lastly, Step 3. adds the significant socio-economic well-being indicator(s) to the equation, in order for SAVs to yield economic, societal and environmental benefit to the cities’ populations. Steps 2 and 3 are summarized by the ‘SAV identification matrix’ explained in Section 2.2, whilst the third section provides an overview of the data used.

2.1 OLS regression models for accessibility and socio-economic well-being

Based on the literature above, the authors of this paper test whether better accessibility by private car and public transport is present where yearly income is higher (H1), unemployment rates are lower (H2) and where educational attainment levels are higher (H3) in European cities.

Therefore, the linear relationship between accessibility rates and the defined socio-economic well-being variables are established as follows:

$$ {\mathrm{Y}}_d^{C, PT}={\upbeta}_0+{\beta}_1^{C, PT}\ {\mathrm{INC}}_d+{\upmu}_d $$
(1)
$$ {\mathrm{Y}}_d^{C, PT}={\upbeta}_0+{\beta}_2^{C, PT}\ {\mathrm{UEM}}_d+{\upmu}_d $$
(2)
$$ {\mathrm{Y}}_d^{C, PT}={\upbeta}_0+{\beta}_3^{C, PT}\ {\mathrm{EDU}}_{\mathrm{d}}+{\upmu}_d $$
(3)

The regression is formulated for \( {\mathrm{Y}}_d^{C, PT} \), the accessibility in a specific city district (d) by the two modes of transportation: car (C) and public transportation (PT). More precisely, \( {\mathrm{Y}}_d^{C, PT} \) is the average number of shops accessible in any given d within 30 min by the two selected means of transportation. This point of interest and time are selected as they are very representative of overall accessibility (see Section 2.3 and Appendix 3). The district-level independent variables are the following: in Model (I), INCd is the indicator for mean or median yearly incomeFootnote 2 in Euros (€) or British Pounds (£) in any given d; in Model (II) UEMd is the unemployment rate in any given d, as a percentage of the labour forceFootnote 3; and in Model (III) EDUd is the calculated weighted average educational attainment index on a scale from one to three in every d. The educational attainment index EDUd is based on the percentage of the population with low (ISCED 2011 levelsFootnote 4 01–2), medium (ISCED 2011 levels 3–4) and high (ISCED 2011 levels 5–8) educational attainment. The authors attributed the values 1 to low; 2 to medium and 3 to high educational attainment. The weighted average was calculated based on the percentages of the population attributed to these three categories for every d. Finally, μd represents the error of the OLS regression models for any d.

Building on the literature above, the authors expect a positive linear effect of yearly income (INCd), and a negative linear effect of increasing unemployment rates (UEMd) on accessibility by public transportation (\( {\mathrm{Y}}_d^{PT} \)). Since highly educated adults make use of shared mobility in Europe and because these services are mostly provided in core city areas, the authors expect a positive linear correlation between average educational attainment (EDUd) and accessibility. The comparison to accessibility by car (\( {\mathrm{Y}}_d^C \)) gives insights on the success of the European focus on PT in spatial and transportation planning. Furthermore, it offers the opportunity to learn about d where C and potential transportation by SAVs could bring greater benefits (see Section 2.2 for more details). Therefore, bivariate Models (I), (II) and (III) are tested for accessibility by car (\( {\mathrm{Y}}_d^C \)), as well as public transportation (\( {\mathrm{Y}}_d^{PT} \)).

However, all three independent variables are also known to be interrelated: Firstly, yearly income and unemployment are connected by definition, as the latter is a description of a state in which a person in working age is without work [20]. Naturally, an increasing unemployment rate, will negatively affect yearly income in a specific area. Secondly, income and income inequality are connected to educational attainment and inequality thereof, since a certain wealth and income is necessary to complete higher educational levels and higher educational attainment is associated with better paid jobs ([44, 50], pp. 389–390).

Given these interrelations between the independent variables, it is likely that there are cumulative effects on accessibility. To test this relationship and discuss potential multicollinearity, multiple linear regression Model (IV) is added to the analysis:

$$ {\mathrm{Y}}_d^{C, PT}={\upbeta}_0+{\beta}_1^{C, PT}\ {\mathrm{INC}}_d+{\beta}_2^{C, PT}\ {\mathrm{UEM}}_d+{\beta}_3^{C, PT}\ {\mathrm{EDU}}_d+{\upmu}_d $$
(4)

Together these four Models, enable the authors (1) to test whether there are spatial mismatches in terms of accessibility and socio-economic well-being in European cities and (2) how accessibility by car (\( {\mathrm{Y}}_d^C \)) and public transportation (\( {\mathrm{Y}}_d^{PT} \)) compare. The four Models are applied to district-level datasets of Paris, Berlin, London and Vienna.

2.2 SAV district identification matrix

Figure 2 exemplifies how SAV districts can be identified graphically. The y-axis showcases the difference (∆) between the number of shops accessible within 30 min by public transportation (PT) and the number accessible within the same timeframe by car travel (C). The green arrow indicates that ∆ between \( {\mathrm{Y}}_d^C \) and \( {\mathrm{Y}}_d^{PT} \) equals zero. If the number is positive, for a particular district (d), PT performs better, and d finds itself in the upper half of the matrix. If C performs better in a d it finds itself in the bottom half of the matrix. This is the case for example district dx (see red dot). In this paper ∆ is normalised (only positive numbers) if one means of transport performs better than the other in every d of a city.

Fig. 2
figure 2

SAV district identification matrix

The x-axis showcases the previously identified significant mean or median socio-economic well-being indicator for each d, as well as the city mean or median (blue arrow). The mean is applied to the UEMd, as well as the index for EDUd, while the median is used for INCd. In the case of district dx in Fig. 2, its socio-economic well-being indicator value lies below the city average or mean. In result, example district dx is identified as a SAV district.

2.3 Accessibility and socio-economic well-being data

The accessibility dataFootnote 5 for the two dependent variables \( {\mathrm{Y}}_d^C \) and \( {\mathrm{Y}}_d^{PT} \) is obtained from the ITF and is based on TomTom navigation calculations ([21], p. 22). The socio-economic well-being data, for the three independent variables yearly income (INCd), unemployment rate (UEMd) and educational attainment (EDUd) is publicly accessible and stems from corresponding city statistics offices. See Appendix 1 for a description table of the data and Appendix 3 for overview plots for each city.

2.3.1 Accessibility data

The FUA grid map data is obtained in a combined Shapefile for all cities. It is first imported, separated and combined with the corresponding FUA accessibility datasets in QGIS.Footnote 6The estimated number of points of interest accessible within a specific timeframe were previously established by the ITF [21] on the basis of TomTom calculations for road travel and schedule data for public transportation. For road travel the estimations also include two city-specific coefficients to include congestion, depending on the capacity of the roads and commuting zones for every grid field.

Given the focus of this paper on SAVs, the datasets are limited to car travel (C) and public transportation (PT), and the number of shops accessible within 30 min. The number of accessible shops per districts within 30 min shows some of the greatest differences between districts in the four cities and is representative also of accessibility by PT to hospitals and schools (see Appendix 3).

To compare the selected accessibility data to district-level indicators, publicly accessible district maps of the four cities in Shapefile-format are added in QGIS and the union vector geoprocessing tool is applied. Data preparation and selection are performed in R.Footnote 7 The data cleaning procedure reduces the number of observations (500m2 grid fields) from n = 50′159 to n = 20′424 for the Paris FUA dataset; from n = 73′428 to n = 20′723 for the Berlin FUA dataset; from n = 28′579 to n = 17′570 for the London dataset and; from n = 38′381 to n = 2′012 for the Vienna dataset.Footnote 8

On the basis of these cleaned FUA accessibility datasets, the mean number of stores accessible by C or PT from every city-district are calculated, apt to be merged with the socio-economic well-being data set explained below.

2.3.2 Socio-economic well-being data

The socio-economic well-being indicators are compiled in a search effort at the various statistics offices, their publicly available databases and publications (see Appendix 2 for city-district socio-economic well-being data).

For the city of Paris, the Institut national de la statistique et des études économiques (Insee) provides the following district-level indicators for the year 2016: median yearly disposable household income, the unemployment rate for 15–64-year olds, and percentages of the population 15 and above who have completed five different schooling levelsFootnote 9 [19].

The Berlin socio-economic well-being data is available at the Amt für Statistik Berlin-Brandenburg. Yearly mean income data stems from the 2014 handbook, while the unemployment rate and the percentage of the population who has attained a high, medium or low level of education according to ISCED 2014, are based micro-census database established in 2017 [1, 2].

For London the data is provided by the Office for National Statistics (ONS). The yearly median income in 2016–7 by borough is presented before taxes and deductions [33]. The unemployment rate is calculated for the population of 16 years of age and above for the year 2017 and qualifications of the working population ages 16–64 in 2018 are subdivided into six categoriesFootnote 10 [28, 34].

Lastly, the datapoints for the socio-economic well-being variables for Vienna are: median yearly income after taxes and deductions for the year 2014, the unemployed population per district in 2013, as well as percentages of district populations’ highest attained degreesFootnote 11 [48].

3 Results

In the first subsection, the OLS regressions are performed and the correlations in each city explained. The second subsection displays the districts (d) for every city, which are most promising for the deployment of SAVs.

3.1 Relating accessibility and socio-economic well-being in European cities

Tables 1 and 2 showcase the regression results and coefficient estimates of the four linear OLS regression models explained in Section 2.1. Table 2 displays the obtained results on the dependent variable accessibility by public transportation (PT), \( {\mathrm{Y}}_d^{PT} \), while Table 2 shows the relationship of the omitted variables on accessibility by car travel (C), \( {\mathrm{Y}}_d^C \). See Appendix 4 for detailed regression tables for each city.

Table 1 Summary of city regression tables for four models on PT.
Table 2 Summary of city regression tables for four models on C

The predictors vary in format amongst each other within and between the cities. However, in sight of the result Tables 1 and 2, it becomes obvious that the estimates \( {\beta}_1^{C, PT} \) for yearly income (INCd) are by far the smallest (between 100 and 101), while estimates \( {\beta}_2^{C, PT} \) for the unemployment rate (UEMd) range between 102 and 105 and estimates \( {\beta}_3^{C, PT} \) for educational attainment (EDUd) range between 104 and 105. In case of a significant correlation, a small change in EDUd (values between 1 and 3) will increase \( {\mathrm{Y}}_d^{C, PT} \) greatly, while a similar change in INCd will only have a limited effect. The two tables also describe minimum, maximum and median \( {\mathrm{Y}}_d^{C, PT} \), in order to gain a relative understanding between the cities. In the following the results of the bivariate and the multiple linear regression models are outlined separately.

3.1.1 Simple linear regression model

Model (I) only shows a significant effect between \( {\mathrm{Y}}_{\mathrm{d}}^{\mathrm{PT}} \) and INCd in London with estimate \( {\beta}_1^{PT} \) of 12.44 at p < .001 (see Table 2). This relationship is positive and strong (R2 of .62 for 33 boroughs d). Nevertheless, the simple linear regression Model (I) performed on \( {\mathrm{Y}}_d^C \), displays positive, significant relationships with INCd (see Table 2) both for London (\( {\beta}_1^C \) = 3.01, p < .001) and Paris (\( {\beta}_1^C \) = 3.89, p < .01). INCd predicts \( {\mathrm{Y}}_d^C \) better in London (R2 = .56) than in Paris (R2 = 0.32). In summary, H1 (see Section 2.1) is confirmed for London’s political centre and is shown for \( {\mathrm{Y}}_d^C \) for the city of Paris.

In comparison, Model (II) showed negative effect (p < .05) between UEMd and \( {\mathrm{Y}}_{\mathrm{d}}^{\mathrm{PT}} \) in Vienna (\( {\beta}_2^{PT} \) = − 3′241) and between UEMd and \( {\mathrm{Y}}_{\mathrm{d}}^C \) in Paris (\( {\beta}_2^C \) = − 11′488). In both cases, the negative relationship confirms H2, although the validity of Model (II) is lower than in Model (I) with R2 = .27 in Vienna and R2 = .25 in Paris. No relationship between UEMd and \( {Y}_{\mathrm{d}}^{C, PT} \) is identified for Berlin and London.

Across the cities, EDUd shows positive significant relationships with \( {\mathrm{Y}}_d^{C, PT} \) in all cities but Berlin, strongly supporting H3. In Paris, the values of \( {\beta}_3^{PT} \) are 210′897 (p < .05) and \( {\beta}_3^C \) is of 142′660 (p < .05) with R2 of .28 and .2 respectively. In London as well, EDUd is a better predictor for \( {\mathrm{Y}}_d^{PT} \) (R2 = .44) than \( {\mathrm{Y}}_d^C \) (R2 = .32) with corresponding estimates \( {\beta}_3^{PT} \) of 356′024 (p < .001) and \( {\beta}_3^C \) equalling 81′139 (p < .001). Lastly, in Vienna \( {\beta}_3^{PT} \) is of 45′523 (p < .05, R2 = .38) and \( {\beta}_3^C \) amounts to 34′469 (p < .05, R2 = .2).

When taking a within city perspective on these three univariate models it becomes clear that the only determinant showing a relationship with both accessibility by C and PT, \( {\mathrm{Y}}_d^{C, PT} \), in Paris, as well as London and Vienna, is EDUd. However, in Paris changes in INCd explain a greater change of \( {\mathrm{Y}}_d^C \), than EDUd. Similarly, in London, both INCd and EDUd show significant effects with \( {\mathrm{Y}}_d^{C, PT} \), but INCd explains a greater share of the change of \( {\mathrm{Y}}_d^{C, PT} \) than EDUd. Finally, none of the univariate models show any relationship between \( {\mathrm{Y}}_d^{C, PT} \) and the three determinants in Berlin.

3.1.2 Multiple linear regression models

For Paris, the additive Model (IV) does not identify any significant relationship neither for PT nor for C. Meanwhile, the Berlin dataset shows significant correlations between both dependent variables and EDUd, as well as UEMd in the additive Model (IV). While the positive relationship between \( {\mathrm{Y}}_d^{C, PT} \) and EDUd (\( {\beta}_3^{PT} \) = 170′604 and \( {\beta}_3^C \) = 175′007 at p < .01) exhibited by Model (IV) confirms H3, the relationship between \( {\mathrm{Y}}_d^{C, PT} \) and UEMd (\( {\beta}_2^{PT} \) = 7′299, p < .05 and \( {\beta}_2^C \) = 7′310, p < .01) is also positive, defeating H2. In London, the multiple linear regression model showcases that the effect of EDUd on \( {\mathrm{Y}}_d^{C, PT} \) is absorbed by INCd, confirming H1 with estimates \( {\beta}_1^{PT} \) = 13.98 (p < .01) and \( {\beta}_1^C \) = 4.60 (p < .001). Nonetheless, like in Paris, the single linear regression models perform better than Model (IV). Finally, in Vienna, the significant effects of EDUd and UEMd on \( {\mathrm{Y}}_d^{PT} \) is not found in Model (IV), but the significant coefficient measured in the single linear regression model between \( {\mathrm{Y}}_d^C \) and EDUd becomes stronger (\( {\beta}_3^C \) = 67,652, p < .01).

3.2 Identifying SAV city-districts in European cities

As explained in Section 2, the selection of the relevant socio-economic well-being indicator is first explained for every city, before the performance of car travel (C) and public transportation (PT) are compared and the ‘SAV identification matrix’ is applied (see Fig. 3).

Fig. 3
figure 3

SAV district identification matrixes per city

3.2.1 Paris

In Paris, EDUd has an effect on both \( {\mathrm{Y}}_d^{C,} \) and \( {\mathrm{Y}}_d^{PT} \). However, INCd explains a greater share of the change in \( {\mathrm{Y}}_d^C \), and as SAVs offer a great opportunity for low-income households to gain improved \( {\mathrm{Y}}_d^C \), both INCd and EDUd are included in the identification process. Furthermore, Paris offers greater accessibility by car in every district (d), thus creating a vast opportunity for the deployment of SAVs.

Figure 3 shows the two identification matrixes for Paris’ political core. Applying INCd in the identification matrix highlights how strongly accessibility \( {\mathrm{Y}}_d^{C, PT} \) could be improved in districts d12, d13, d14, d17, d18, d19 and d20, increasing \( {\mathrm{Y}}_d^{C, PT} \) by more than 100′000 shops within 30 min. Also, d10 and d11 would potentially improve their \( {\mathrm{Y}}_d^{C, PT} \) by 50′000 to 100′000 shops. Similarly, EDUd spotlights the same d, except of d14 which performs better in terms of socio-economic well-being and d17 which performs clearly below average here.

3.2.2 Berlin

In Berlin, the selection of a significant socio-economic well-being variable is more difficult, as the three single linear regression models do not show any relationship between \( {\mathrm{Y}}_d^{C, PT} \) and the socio-economic well-being indicators. However, since EDUd is positive and significant in the multiple linear regression models, confirming H3 for both dependent variables, it is selected. d5, d9, d10 and d12 have the lowest accessibility rates both by PT and C. The latter promises larger improvements in Berlin with possible improvements within 30 min between 10′000 and 20′000. However, in order to improve Berlins urban populations’ socio-economic well-being, policymakers should focus on districts d1, d5, d8, d10 and d12, where the educational index scores below average (see Fig. 3).

3.2.3 London

As displayed in Section 3.1 there is a significant effect of both INCd and EDUd on \( {\mathrm{Y}}_d^{C, PT} \) in London, confirming H1 and H3. Therefore, both these socio-economic well-being indicators are included in the second part of analysis.

Since London is the only city where \( {\mathrm{Y}}_d^C \) does not outperform \( {\mathrm{Y}}_d^{PT} \) in every district, the identification matrixes in Fig. 3 show four fields, as described in Section 2.2. The benefits of improving \( {\mathrm{Y}}_d^{C, PT} \) by C are limited to a maximum of 20′000 more shops within 30 min. On the contrary, the difference ∆ between \( {\mathrm{Y}}_d^{PT} \) and \( {\mathrm{Y}}_d^C \) can reach up to more than 330′000 shops (see d1). When applying either socio-economic well-being variable (INCd or EDUd), the identified SAV districts in the bottom left corner are: d4, d8, d11, d16, d17, d26 and d29. The Sutton borough (d29) performs slightly below average in terms of INCd, while its EDUd equals the city average. The borough of Havering (d16) has the highest potential of improvement.

3.2.4 Vienna

H2 and therefore, an effect of EDUd on \( {\mathrm{Y}}_d^{C, PT} \) is confirmed in Section 3.1. Therefore, this predictor is selected for the identification of SAV districts in Vienna.

In terms of accessibility \( {\mathrm{Y}}_d^{C, PT} \), C performs better than PT in all 15 districts where data for both means of transportation is available. The city mean educational attainment index score is of 1.97. In result, d2, d5, d11, d12, d15, d16 and d20 all find themselves below the mean and could potentially benefit from the deployment of SAVs (see Fig. 3). Especially, d11 offers a great potential, as it has by far the lowest EDUd of all districts d and it could increase its \( {\mathrm{Y}}_d^{C, PT} \) by over 30′000 shops within 30 min, almost quadrupling its current \( {\mathrm{Y}}_d^{PT} \).

4 Discussion and policy recommendations

AVs and especially shared AVs (SAVs) are expected to bring many benefits to its users, including, amongst others, improved security as well as the time and comfort gained for its users during travel. However, the expected reduced costs of car travel (C) through SAVs are likely to expand the urban sprawl, increase congestion within urban centres and cause a loss of employment opportunities in the transportation industry [4, 30, 31]. These negative externalities can be mitigated by policies aimed at ensuring the complementarity of SAVs to public transportation (PT) and their focused introduction so to improve social equity. As showcased by the ITF and confirmed in this paper, C offers better accessibility, in terms of the number of accessible shops within 30 min, in most European cities [21]. Nevertheless, this is only possible, since PT systems carry the majority of passenger traffic, relieving much of the potential C. Therefore, the three-step model developed in this paper, aims to purposefully deploy and support SAVs in city districts in which accessibility should be improved in order to increase accessibility and socio-economic well-being.

The first step of analysis showcases that there is a relationship between accessibility and socio-economic well-being in the investigated European urban areas, as this relationship is evidenced in the cities of Paris, London as well as Vienna and partially shown in the city of Berlin. Overall, educational attainment (EDUd) is the best-performing socio-economic well-being indicator for accessibility across the cities. It showcases significant positive effects on district-level accessibility to shops within 30 min by C and PT (\( {\mathrm{Y}}_d^{C, PT} \)) in the simple linear regression models for Paris, London and Vienna, as well as in the multiple linear regression models for Berlin and partially in Vienna, thereby providing strong evidence for H3 (see Section 3.1). This tendency can likely be explained by (1) the greater share of high-skill employment in urban centres ([9], pp. 27–32, 2) the greater number of higher education institutions in urban centres and (3) the stability of educational attainment over generations ([32], pp. 76–77).

However, given the reasoning above and the significant correlations between yearly income (INCd) and EDUd identified within all four cities analysed, the authors also expect a significant effect between INCd and \( {\mathrm{Y}}_d^{C, PT} \) confirming H1. The latter is only supported in Paris and London. On the one hand, this can be explained by the focus of the analysis on the political core and its districts, which creates a “small n” problem in all cities analysed, and especially in Berlin.Footnote 12 Hence, in future analysis, a neighbourhood-level analysis would be beneficial to resolve the “small n” problem and improve comparability between the observations.

On the other hand, urban planning policies (including centrally located social housing) in Berlin and Vienna, likely decelerated the urban sprawl and gentrification, rendering the effect of INCd less significant in these two cities. Meanwhile, if INCd has a significant effect on \( {\mathrm{Y}}_d^{C, PT} \), it has greater predictive ability than EDUd in relative city terms. Therefore, INCd is included in the identification process of SAV districts in Paris and London (see Section 3.2).

H2 is only confirmed for the effect of the unemployment rate (UEMd) on accessibility by C (\( {\mathrm{Y}}_d^C \)) in Paris and on accessibility by PT (\( {\mathrm{Y}}_d^{PT} \)) in Vienna. While UEMd is one of the most often measured and used economic socio-economic well-being indicators, it is less stable over time than INCd and EDUd, due to its responsiveness to cyclical downturns. In result, this predictor is not applied in any cities’ ‘SAV district identification matrix’.

In general, the simple linear regression models perform better than the multiple linear regression models in Paris, London and Vienna for \( {\mathrm{Y}}_d^{C, PT} \). While this hints at multicollinearity the Variance Inflation Factor does not show any alarming rates. In future research, it would be interesting to include data on employment and unfilled positions to gain more direct needs-based insight and see whether the models behave similarly, as described by Silva and Larsson (cit. in [22]) and to include more complex accessibility measures as proposed by Geurs & van Wee [16], Geurs et al. [14] as well as by [40]). However, we tried to the best of our knowledge to fill this gap with literature on the potential benefits and risks of SAVs. By doing so, we follow the logic typically found in equity assessment of transportation policy as described in the recent literature review by Guo et al. [17] and their three identified main components: population measurement, cost-benefit measurement as well as equity measurement.

Throughout this paper we have highlighted the importance of regulation to include equity in accessibility assessment alongside the introduction of SAVs. The ‘SAV district identification matrix’ is the very result of this. In literature, there are mainly three policy areas which can help to yield the benefits of SAVs and help improve overcome social and spatial inequity of urban inhabitants’ [12, 51]: (1) incentivise the usage of shared mobility including SAVs by overcoming technical and economic barriers in underserved low-income areas and (2) incentivise the usage of public transport, shared vehicles and other means of transport in well-served areas and (3) offer a platform to share data between service providers both public and private to coordinate and optimise service provision and accessibility.

In the realm of policies (1) and (2), this paper offers a data-driven three-step model identifying districts where the deployment and focused support of SAVs may prove helpful in overcoming the spatial barrier described in the STEPS model by Fleming [12] and Shaheen et al. [45]. In order to be successful, the policies must be adapted to the various business models for the introduction of SAVs in European cities: The desired focused and complementary deployment of SAVs to PT is achieved easiest if all means of transportation are organised by the same entity, allowing for a simplified data-generation and analysis. If SAVs are privately owned, regulation is necessary to oblige companies and the corresponding sharing systems to focus their services on the identified districts and overcome part of the market incentive at the core of their services. The latter is strongly connected to the policies necessary to defeat the economic barriers discussed in the STEPS model as subsidies should be included for low-income urban populations. This can be offered through a monthly or yearly budget to use the services in the SAV districts or through adapted pricings schemes according to a user’s registered address or depending on a users’ address of departure or destination. Overall, these policies must be adapted to spatial planning measures, as well as existing and potentially growing transportation needs in every city. Policy (3) is therefore detrimental to help monitor and evaluate policies and their impact. Economic policy measures to facilitate access to required technologies (smartphones, mobile data subscriptions and alternative payment systems) are relevant to either business models. These measures are also closely connected to policies directed at overcoming the social barriers, as the sharing economy mainly is used by younger, more affluent and well-educated adults. With EDUd being the best indicator for accessibility in European cities, it becomes evident that the urban populations with the greatest opportunity to improve their accessibility and socio-economic well-being need to be integrated in the sharing economy. Facilitating eased access to the required technologies and organising events to raise awareness is prerequisite to a successful deployment of SAVs.

5 Conclusion

In summary, this paper provides new evidence for spatial mismatches in European urban areas and offers insights on how SAVs can improve equity in accessibility and socio-economic well-being in the four European capitals analysed. Nevertheless, how SAVs will be deployed, which business models will prevail, how car-ownership will be affected and ultimately, who the largest beneficiaries of SAVs will be, remains to be seen. Certainly, policies must be introduced alongside and in preparation to the deployment of SAVs, in order to ensure their complementarity to PT, asses their effect on equity in accessibility and forego possible negative externalities. Therefore, policies must be implemented that are (1) data-driven, requiring the deployment and support of SAVs in areas with lower accessibility and socio-economic well-being, and (2) raise awareness among low-education as well as low-income populations and provide technologies and support to facilitate their access to SAVs. These policies will need to be continuously adapted, as to respond to user needs and market developments. Further research on a neighbourhood level and the application of broader accessibility measures, especially to include job-market data, are important as to secure the data-driven and well-directed approach described. The three-step model proposed in this paper can aid policymakers to maintain overview and aim at improving accessibility for European urban populations.

Availability of data and materials

The data sources are listed in Appendix 1 and are for the most part publicly available. The data that support the findings of this study are available on request from the corresponding author.

Notes

  1. The STEPS model identifies (s)patial, (t)emporal, (e)conomic, (p)hysiological and (s)ocial barriers and offers possible solutions to overcome them.

  2. Statistical offices in the multiple cities, regions and countries use different indicators to track inhabitants’ yearly incomes. This paper made use of both median and mean yearly income dependent on availability and timeliness of data (see Section 2.3 for details).

  3. Active population is calculated differently by various statistical offices, depending on included age-range, inclusion and definition of long-term unemployed, as well as inclusion of students ([20], pp. 1–2).

  4. International Standard Classification of Education (ISCED) as defined by the UNESCO Institute for Statistics in 2011 ([52], pp. 30–63). This classification is used to make statistical data collected by various statistical offices comparable between cities and calculate average educational attainment.

  5. The accessibility data used by the ITF for its city benchmarking is soon to be publicly accessible online and was received before publication and upon request for this paper.

  6. QGIS is a free opensource software for geographic information systems (GIS) [41]. This paper made use of version QGIS 3.8 Zanzibar.

  7. Statistics are done using R version 3.6.1 [42], the dplyr [58], the data.table [10] and the car [13] packages.

  8. The ITF accessibility dataset for the Vienna FUA entails a very limited amount of datapoints for public transportation PT. Therefore, a separate dataset with n = 12′708 for C is created in order to have a stronger basis for the calculations of average \( {\mathrm{Y}}_d^C \).

  9. The completed levels of schooling are attributed as follows: high educational attainment for ‘diplôme de l’enseignement supérieur’; medium educational attainment for ‘CAP ou d’un BEP’ as well as ‘baccalauréat (général, technologique, professionnel)’; and low educational attainment for ‘d’aucun diplôme ou au plus d’un BEPC, brevet des collèges ou DNB

  10. Percentage of working population with no educational level as well as population with National Vocational Qualification (NVQ) level 1 are summed under low educational attainment; Medium educational attainment comprises the percentage of the population with NVQ2 only plus percentage with Trade Apprenticeships; and high educational attainment covers the percentages of the population with NVQ3 and NVQ4.

  11. The six degrees were attributed as follows: ‘Pflichtschule’ and ‘Lehre’ were attributed to low; ‘BMS’ and ‘AHS’ to medium; and ‘BHS’, ‘Hochschule’, and ‘Kolleg’ to high educational attainment.

  12. This issue is addressed by the predicted models with 95% confidence intervals created to showcase the stability of the OLS regressions applied (see Appendix 5)

Abbreviations

C :

Car travel

d :

District, borough of a political city core

EDUd,:

Educational attainment index per district

EU:

European Union

FUA:

Functional urban area

HDI:

Human Development Index

INCd :

Mean or median yearly income per district

ITF:

International Transport Forum

OECD:

Organisation for Economic Co-operation and Development

OLS:

Ordinary Least Squares

PT :

Public transportation

SAVs:

Shared autonomous vehicles

UNDP:

United Nations Development Programme

UK:

United Kingdom

US:

United States

UEMd :

Unemployment rate per district

\( {\mathrm{Y}}_d^C \) :

Number of accessible shops per districts by car travel

\( {\mathrm{Y}}_d^{PT} \) :

Number of accessible shops per districts by public transportation

References

  1. Amt für Statistik Berlin-Brandenburg (2015). Die kleine Berlin-Statistik 2015, (p. 64). Berlin: Amt für Statistik Berlin-Brandenburg Retrieved from Amt für Statistik Berlin-Brandenburg website: https://www.statistik-berlin-brandenburg.de/produkte/kleinestatistik/AP_KleineStatistik_EN_2015_BE.pdf.

    Google Scholar 

  2. Amt für Statistik Berlin-Brandenburg. (2017). AfS StatIS-BBB - table view [Database]. Retrieved October 2, 2019, from StatIS-BBB Das Statistische Informationssystem Berlin Brandenburg website: https://www.statistik-berlin-brandenburg.de/webapi/jsf/tableView/tableView.xhtml

    Google Scholar 

  3. Boldrini, C., Bruno, R., & Laarabi, M. H. (2019). Weak signals in the mobility landscape: car sharing in ten European cities. EPJ Data Science, 8(1), 7. https://doi.org/10.1140/epjds/s13688-019-0186-8.

    Article  Google Scholar 

  4. Bösch, P. M., Becker, F., Becker, H., & Axhausen, K. W. (2018). Cost-based analysis of autonomous mobility services. Transport Policy, 64, 76–91. https://doi.org/10.1016/j.tranpol.2017.09.005.

    Article  Google Scholar 

  5. Clark, J., & Curl, A. (2016). Bicycle and car share schemes as Inclusive modes of travel? A socio-spatial analysis in Glasgow, UK. Social Inclusion, 4(3), 83–99. https://doi.org/10.17645/si.v4i3.510.

    Article  Google Scholar 

  6. Clewlow, R., Mishra, G. S., & Kulieke, S. (2017). Disruptive Transportation: The Adoption, Utilization, and Impacts of Ride-Hailing in the United States (no. UCD-ITS-RR-17-7; p. 38). Institute of Transportation Studies, University of California, Davis Retrieved from Institute of Transportation Studies, University of California, Davis website: https://trid.trb.org/view/1485471.

    Google Scholar 

  7. Cohen, T., & Cavoli, C. (2019). Automated vehicles: Exploring possible consequences of government (non) intervention for congestion and accessibility. Transport Reviews, 39(1), 129–151. https://doi.org/10.1080/01441647.2018.1524401.

    Article  Google Scholar 

  8. Cui, Q., Wang, Y., Chen, K., Ni, W., Lin, I., Tao, X., & Zhang, P. (2019). Big data analytics and network Calculus enabling intelligent Management of Autonomous Vehicles in a Smart City. IEEE Internet of Things Journal, 6(2), 2021–2034. https://doi.org/10.1109/JIOT.2018.2872442.

    Article  Google Scholar 

  9. Davis, D. R., & Dingel, J. I. (2014). The Comparative Advantage of Cities (working paper no. 20602). Cambridge: National Bureau of Economic Research. https://doi.org/10.3386/w20602.

    Book  Google Scholar 

  10. Dowle, M., & Srinivasan, A. (2019). Extension of `data.Frame` (Version 1.12.4). Retrieved from https://cran.r-project.org/web/packages/data.table/data.table.pdf

    Google Scholar 

  11. Eurostat. (2018). Glossary: functional urban area. Retrieved September 16, 2019, from Statistics explained website: https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Functional_urban_area

    Google Scholar 

  12. Fleming, K. L. (2018). Social equity considerations in the new age of transportation: Electric, automated, and shared mobility. Journal of Science Policy & Governance, 13(1), 20.

    Google Scholar 

  13. Fox, J., Weisberg, S., & Price, B. (2019). Companion to applied regression (version 3.0-3). Retrieved from https://cran.r-project.org/web/packages/car/car.pdf

    Google Scholar 

  14. Geurs, K. T., Boon, W., & Wee, B. V. (2009). Social impacts of transport: Literature review and the state of the practice of transport appraisal in the Netherlands and the United Kingdom. Transport Reviews, 29(1), 69–90. https://doi.org/10.1080/01441640802130490.

    Article  Google Scholar 

  15. Geurs, K. T., Patuelli, R., & Dentinho, T. P. (2016). Accessibility, Equity and Efficiency: Challenges for Transport and Public Services. Cheltenham, UK & Northampton, MA, USA: Edward Elgar Publishing.

  16. Geurs, K. T., & van Wee, B. (2004). Accessibility evaluation of land-use and transport strategies: Review and research directions. Journal of Transport Geography, 12(2), 127–140. https://doi.org/10.1016/j.jtrangeo.2003.10.005.

    Article  Google Scholar 

  17. Guo, Y., Chen, Z., Stuart, A., Li, X., & Zhang, Y. (2020). A systematic overview of transportation equity in terms of accessibility, traffic emissions, and safety outcomes: From conventional to emerging technologies. Transportation Research Interdisciplinary Perspectives, 4, 100091. https://doi.org/10.1016/j.trip.2020.100091.

    Article  Google Scholar 

  18. Halden, D. (2011). The use and abuse of accessibility measures in UK passenger transport planning. Research in Transportation Business & Management, 2, 12–19. https://doi.org/10.1016/j.rtbm.2011.05.001

  19. Insee. (2019). Commune de Paris 1er Arrondissement (75101). Retrieved October 2, 2019, from Comparateur de territoire website: https://www.insee.fr/fr/statistiques/1405599?geo=COM-75101

    Google Scholar 

  20. International Labour Organization [ILO]. (2019). Unemployment rate. Retrieved from https://www.ilo.org/ilostat-files/Documents/description_UR_EN.pdf

    Google Scholar 

  21. ITF (2019a). Benchmarking Accessibility in Cities: Measuring the Impact of Proximity and Transport Performance (No. 68). OECD Publishing Retrieved from OECD Publishing website: https://www.itf-oecd.org/sites/default/files/docs/accessibility-proximity-transport-performance_2.pdf.

    Google Scholar 

  22. ITF (2019b). Improving Transport Planning and Investment Through the Use of Accessibility Indicators (text no. 66). Paris: OECD Publishing Retrieved from OECD Publishing website: https://www.itf-oecd.org/transport-planning-investment-accessibility-indicators.

    Google Scholar 

  23. King, D. A., Smart, M. J., & Manville, M. (2019). The poverty of the carless: Toward universal auto access. Journal of Planning Education and Research, 0739456X1882325. https://doi.org/10.1177/0739456X18823252.

  24. Koetsier, J. (2019). Self-driving Cars In 10 Years: EU Expects “Fully Automated” Cars by 2030. Retrieved August 2, 2019, from Forbes website: https://www.forbes.com/sites/johnkoetsier/2019/04/06/self-driving-cars-in-10-years-eu-expects-fully-automated-cars-by-2030/

    Google Scholar 

  25. Litman, T. (2019). Evaluating transportation equity. World Transport Policy and Practice, 8(2). Retrieved September 20, 2019, from https://www.vtpi.org/equity.pdf.

  26. Lucas, K. (2012). A critical assessment of accessibility planning for social inclusion. In Accessibility Analysis and Transport Planning (pp. 228–242). Edward Elgar Publishing. https://doi.org/10.4337/9781781000113.00022

  27. Lucas, K., van Wee, B., & Maat, K. (2016). A method to evaluate equitable accessibility: Combining ethical theories and accessibility-based approaches. Transportation, 43(3), 473–490. https://doi.org/10.1007/s11116-015-9585-2.

    Article  Google Scholar 

  28. Mayor of London, & London Assembly. (2019). London Area Profiles. Retrieved October 6, 2019, from London Datastore website: https://data.london.gov.uk/london-area-profiles/

    Google Scholar 

  29. Medgyesi, M., Özdemir, E., & Ward, T. (2017). Regional indicators of socio-economic well-being (research note no. 9). Brussels: European Commission.

    Google Scholar 

  30. Meyer, J., Becker, H., Bösch, P. M., & Axhausen, K. W. (2017). Autonomous vehicles: The next jump in accessibilities? Research in Transportation Economics, 62, 80–91. https://doi.org/10.1016/j.retrec.2017.03.005.

    Article  Google Scholar 

  31. Milakis, D., van Arem, B., & van Wee, B. (2017). Policy and society related implications of automated driving: A review of literature and directions for future research. Journal of Intelligent Transportation Systems, 21(4), 324–348. https://doi.org/10.1080/15472450.2017.1291351.

    Article  Google Scholar 

  32. OECD (2017). Indicator A4 to what extent does parents’ education influence their children’s educationalattainment? In Education at a glance 2017: OECD Indicators. OECD Publishing. https://doi.org/10.1787/eag-2017-10-en.

    Chapter  Google Scholar 

  33. Office for National Statistics (ONS). (2019a). Earnings by Place of Residence, Borough. Retrieved October 6, 2019, from London Datastore website: https://data.london.gov.uk/dataset/earnings-place-residence-borough

    Google Scholar 

  34. Office for National Statistics (ONS). (2019b). Qualifications of Working Age Population (NVQ), Borough [Database]. Retrieved October 6, 2019, from London Datastore website: https://data.london.gov.uk/dataset/qualifications-working-age-population-nvq-borough

    Google Scholar 

  35. Pendleton, S. D., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y. H., … Rus, D. L. (2017). Perception, planning, control, and coordination for autonomous vehicles. Machines, 5(6). https://doi.org/10.3390/machines5010006.

  36. Pettigrew, S., Fritschi, L., & Norman, R. (2018). The potential implications of autonomous vehicles in and around the workplace. International Journal of Environmental Research and Public Health, 15(9). https://doi.org/10.3390/ijerph15091876.

  37. Pitarch-Garrido, M.-D. (2018). Social sustainability in metropolitan areas: Accessibility and equity in the case of the metropolitan area of Valencia (Spain). Sustainability, 10(2), 371. https://doi.org/10.3390/su10020371.

    Article  Google Scholar 

  38. Portnov, B. A., Axhausen, K. W., Tschopp, M., & Schwartz, M. (2011). Diminishing effects of location? Some evidence from Swiss municipalities, 1950–2000. Journal of Transport Geography, 19(6), 1368–1378. https://doi.org/10.1016/j.jtrangeo.2011.07.017.

    Article  Google Scholar 

  39. Pritchard, J. P., Tomasiello, D. B., Giannotti, M., & Geurs, K. (2019a). Potential impacts of bike-and-ride on job accessibility and spatial equity in São Paulo, Brazil. Transportation Research Part A: Policy and Practice, 121, 386–400. https://doi.org/10.1016/j.tra.2019.01.022.

    Article  Google Scholar 

  40. Pritchard, J. P., Tomasiello, D., Giannotti, M., & Geurs, K. (2019b). An international comparison of equity in accessibility to jobs: London, São Paulo and the Randstad. Findings, 7412. https://doi.org/10.32866/7412.

  41. QGIS Development Team. (2019). QGIS Geographic Information System. Open Source Geospatial Foundation Project (Version QGIS 3.8 Zanzibar). Retrieved from http://qgis.osgeo.org

    Google Scholar 

  42. R Core Team (2019). R: A language and environment for statistical computing (version 3.6.1 (action of the toes)). Vienna: R Foundation for Statistical Computing Retrieved from https://www.r-project.org.

    Google Scholar 

  43. Ramjerdi, F. (2006). Equity measures and their performance in transportation. Transportation Research Record, 1983(1), 67–74. https://doi.org/10.1177/0361198106198300110.

    Article  Google Scholar 

  44. Rodríguez-Pose, A., & Tselios, V. (2009). Education and income inequality in the regions of the European Union. Journal of Regional Science, 49(3), 411–437. https://doi.org/10.1111/j.1467-9787.2008.00602.x.

    Article  Google Scholar 

  45. Shaheen, S., Bell, C., Cohen, A., & Balaji, Y. (2017). Travel behavior: Shared mobility and transportation equity. US Department of Transportation Retrieved from US Department of Transportation website: http://innovativemobility.org/?project=travel-behavior-shared-mobility-transportation-equity.

    Google Scholar 

  46. Smith, M. E. (2002). The earliest cities. In Urban Life—Readings in the Anthropology of the City, (4th ed., ). Prospect Heights: Waveland Press, Inc. Retrieved from https://www.academia.edu/2976000/The_Earliest_Cities_2002_.

    Google Scholar 

  47. Society of Automotive Engineers (SAE). (2019). Standards Collections. Retrieved April 9, 2019, from https://www.sae.org/standards/

    Google Scholar 

  48. Taxacher, I., & Lebhart, G. (2016). Wien—Bezirke im Fokus. Statistiken und Kennzahlen (p. 99). Wien: Magistrat der Stadt Wien, MA 23 Retrieved from Magistrat der Stadt Wien, MA 23 website: https://www.wien.gv.at/statistik/pdf/bezirke-im-fokus-1-23.pdf.

    Google Scholar 

  49. Thomopoulos, N., Grant-Muller, S., & Tight, M. R. (2009). Incorporating equity considerations in transport infrastructure evaluation: Current practice and a proposed methodology. Evaluation and Program Planning, 32(4), 351–359. https://doi.org/10.1016/j.evalprogplan.2009.06.013.

    Article  Google Scholar 

  50. Todaro, M. P., & Smith, S. C. (2015). Economic development, (12th ed., ). Boston: Pearson.

    Google Scholar 

  51. Tomer, A., Kneebone, E., Puentes, R., & Berube, A. (2011). Missed Opportunity: Transit and Jobs in Metropolitan America. Retrieved from https://www.brookings.edu/research/missed-opportunity-transit-and-jobs-in-metropolitan-america/

    Google Scholar 

  52. UNESCO Institute for Statistics (2012). International standard classification of education: ISCED 2011. Montreal: UNESCO Institute for Statistics Retrieved from http://www.uis.unesco.org/Education/Documents/isced-2011-en.pdf.

    Google Scholar 

  53. Union of Concerned Scientists. (2018). Self-Driving Cars Explained. Retrieved September 30, 2019, from Self-driving cars website: https://www.ucsusa.org/clean-vehicles/how-self-driving-cars-work

    Google Scholar 

  54. United Nations Developement Programme (UNDP). (2020). Human Development Index (HDI). Retrieved January 31, 2021, from Human Development Reports website: http://hdr.undp.org/en/content/human-development-index-hdi

    Google Scholar 

  55. U.S. Department of Transportation. (2016). USDOT Automated Vehicles Activities. Retrieved April 9, 2019, from US Department of Transportation website: https://www.transportation.gov/AV

    Google Scholar 

  56. Voege, T., & Zhivov, N. (2018). Cooperative Mobility Systems and Automated Driving (p. 35). OECD/ITF Retrieved from OECD/ITF website: https://www.itf-oecd.org/cooperative-mobility-systems-automated-driving.

    Google Scholar 

  57. van Wee, B., & Geurs, K. (2011). Discussing equity and social exclusion in accessibility evaluations. European Journal of Transport and Infrastructure Research, 11(4). https://doi.org/10.18757/ejtir.2011.11.4.2940.

  58. Wickham, H., François, R., Henry, L., & Müller, K. (2019). Dplyr: A grammar of data manipulation (Version 0.8.3). Retrieved from https://cran.r-project.org/web/packages/dplyr/index.html

    Google Scholar 

  59. Winick, E. (2017). Self-driving cars endanger nearly four million jobs but could create a $7 trillion industry. Retrieved September 30, 2019, from MIT technology review website: https://www.technologyreview.com/f/609747/self-driving-cars-endanger-nearly-four-million-jobs-but-could-create-a-7-trillion-industry/

    Google Scholar 

Download references

Acknowledgements

We would like to express our very great appreciation for Dr. Dimitrios Papaioannou and the International Transport Forum for providing us the accessibility dataset prior to its open-access release and valuable and constructive suggestions and aid during the planning and development of this research work.

Funding

Not applicable

Author information

Authors and Affiliations

Authors

Contributions

NE carried out the literature and data research, performed statistical analysis and wrote the manuscript. MAR conceived the research project and participated in its design. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Norman Eppenberger.

Ethics declarations

Competing interests

Neither author has conflicts of interest to disclose regarding this manuscript.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1 - Data description and sources

Data title

City

Description

Downloaded from

ITF ACCESSIBILITY DATA

Berlin, London, Madrid, Vienna

Csv-files with accessibility data by bike, car, public transport and walking to hospitals, schools, health, recreational zones, shops, food shops, restaurant and a number of population for 500 m2 grid fields of functional urban areas with increasing time (5 min–60 min) or distance (1-20 km); data calculated in 2018–9, but based on INSPIRE grid, and population data generated by the JRC of the European Commission (EC)

Received by e-mail by Dimitris Papaioannou, data analyst at the International Transport Forum (ITF) of the OECD in Paris, France

ITF MAPS OF FUNCTIONAL URBAN AREAS

Berlin, London, Paris, Vienna

Shapefile containing 500 m2 grid maps of the six corresponding urban areas. Best read with the application QGIS to visualize accessibility data

Received by e-mail by Dimitris Papaioannou, data analyst at the International Transport Forum (ITF) of the OECD in Paris, France

District map

Berlin

Mapname: “Ortsteile von Berlin”; coordination system: EPSG:25833; last updated in 2018

https://fbinter.stadt-berlin.de/fb/index.jsp

Paris

Mapname: “Arrondissments”; last updated in 2016

https://opendata.paris.fr/explore/dataset/arrondissements/information/

Vienna

Mapname: “Bezirksgrenzen Wien”; last updated in 2015

https://www.data.gv.at/katalog/dataset/stadt-wien_bezirksgrenzenwien/resource/f1540ea4-edd4-42f5-9b39-2cbba97fea36

London

Mapname: “statistical-gis-boundaries-london.zip: London_Borough_Excluding_MHW.shp”; last updated in 2014

https://data.london.gov.uk/dataset/statistical-gis-boundary-files-london

Population size

Berlin

Population size per district; Micro Census of 2017

https://www.statistik-berlin-brandenburg.de/webapi/jsf/tableView/tableView.xhtml

Paris

Population size per district; year 2016

One webpage per district: https://www.insee.fr/fr/statistiques/1405599?geo=COM-75101

Vienna

Population size per district; year 2013

https://www.wien.gv.at/statistik/bevoelkerung/tabellen/bevoelkerung-bez-zr.html

London

Population size per district; year 2018

https://data.london.gov.uk/london-area-profiles/

Yearly income

Berlin

Monthly mean income per district; year 2014.

https://www.statistik-berlin-brandenburg.de/produkte/kleinestatistik/AP_KleineStatistik_EN_2015_BE.pdf

Paris

Median yearly disposable household income in Euro; year 2016

One webpage per district: https://www.insee.fr/fr/statistiques/1405599?geo=COM-75101

Vienna

Average yearly income after taxes and deductions per employee per district, in Euro; year 2014

https://www.wien.gv.at/statistik/bezirke/index.html

London

Median yearly income of taxpayers per borough in £; years 2016 and 2017

https://data.london.gov.uk/dataset/average-income-tax-payers-borough

Activity rate

Berlin

Estimated number of employed people per district; Micro Census 2017:

https://www.statistik-berlin-brandenburg.de/webapi/jsf/tableView/tableView.xhtml

Paris

Activity rate for 15–64-year-olds per district; year 2016

One webpage per district: https://www.insee.fr/fr/statistiques/1405599?geo=COM-75101

Vienna

Active population; year 2013

https://www.wien.gv.at/statistik/bezirke/index.html

London

Employment rate per borough for population aged 16+; year 2017

https://data.london.gov.uk/london-area-profiles/

Unemployment rate

Berlin

Estimate of number of unemployed people per district; Micro Census 2017

https://www.statistik-berlin-brandenburg.de/webapi/jsf/tableView/tableView.xhtml

Paris

Unemployment rate for 15–64-year-olds per district; year 2016

One webpage per district: https://www.insee.fr/fr/statistiques/1405599?geo=COM-75101

Vienna

Unemployed population; year 2013

https://www.wien.gv.at/statistik/bezirke/index.html

London

Unemployment rate per borough for population aged 16+; year 2017

https://data.london.gov.uk/london-area-profiles/

Educational attainment

Berlin

Estimate of number of people of highest, middle and lowest educational attainment per district, calculated percentage of total population, Micro Census 2017

https://www.statistik-berlin-brandenburg.de/webapi/jsf/tableView/tableView.xhtml

Paris

Attribution of percentages of population with educational attainment defined by six different school diplomas to three groups (high-medium-low) for population 15 and above; year 2016

One webpage per district: https://www.insee.fr/fr/statistiques/2011101?geo=COM-75113#chiffre-cle-6

Vienna

Attribution of percentages of population with educational attainment defined by six different school diplomas to three groups (high-medium-low); year 2011

https://www.wien.gv.at/statistik/bezirke/index.html

London

Percentage of population aged 16–64 (1) with no qualifications and with NVQ1, (2) NVQ2 only plus with Trade Apprenticeships, and (3) with NVQ3 only and with NVQ4+; 2018 data

https://data.london.gov.uk/dataset/qualifications-working-age-population-nvq-borough

Appendix 2 - City district-level socio-economic well-being indicators

1.1 Paris socio-economic well-being data

CITY DISTRICTS

DISTRICT_NUM

POP_SIZE

UNEMP_RATE

MEDIAN_INC

LOW_EDU

MID_EDU

HIGH_EDU

EDU

Paris_City

 

2,190,327

11.4

30,298

15.5

22.9

61.6

2.5

1 Arrondissement

1

16,252

10.9

32,697

14.8

20.1

65.1

2.5

2 Arrondissement

2

20,260

10.8

30,567

13.2

17.3

69.5

2.6

3 Arrondissement

3

34,788

10.7

31,333

12.8

17.6

69.6

2.6

4 Arrondissement

4

27,487

10.6

31,007

11.3

19

69.7

2.6

5 Arrondissement

5

59,108

9.7

33,169

10.8

16.1

73.1

2.6

6 Arrondissement

6

40,916

10.1

39,063

10.8

15.9

73.3

2.6

7 Arrondissement

7

52,512

9.6

42,466

11.5

16.2

72.3

2.6

8 Arrondissement

8

36,453

9.1

40,540

12.7

18.4

68.9

2.6

9 Arrondissement

9

59,629

9.9

33,258

10.8

15.9

73.3

2.6

10 Arrondissement

10

91,932

12.1

25,618

18.2

19.5

62.3

2.4

11 Arrondissement

11

147,017

12

26,810

16.1

20.5

63.4

2.5

12 Arrondissement

12

141,494

10.7

27,110

17.9

23.1

59

2.4

13 Arrondissement

13

181,552

12.9

23,751

13.6

31.9

54.5

2.4

14 Arrondissement

14

137,105

11.8

27,288

16.9

21.2

61.9

2.5

15 Arrondissement

15

233,484

9.9

30,448

14.4

19.9

65.7

2.5

16 Arrondissement

16

165,446

10.2

38,378

14

19.4

66.6

2.5

17 Arrondissement

17

167,835

11.5

30,282

16.4

70.7

12.9

2.0

18 Arrondissement

18

195,060

13.3

21,542

22.1

23.2

54.7

2.3

19 Arrondissement

19

186,393

16.7

19,611

27.5

26.4

46.1

2.2

20 Arrondissement

20

195,604

15

21,017

23.5

26.2

50.3

2.3

1.1.1 Berlin socio-economic well-being data

CITY DISTRICTS

DISTRICT_NUM

POP_SIZE

UNEMP_RATE

MEAN_INC

LOW_EDU

MID_EDU

HIGH_EDU

EDU

Berlin_City

 

3,558,900

7.9

21,000

15.6

40.1

30.4

1.9

Mitte

1

365,300

12

19,200

20.4

32.8

33.0

1.8

Friedrichshain-Kreuzberg

2

275,200

7.4

20,100

12.8

30.7

43.0

2.0

Pankow

3

388,200

5.1

22,200

8.6

36.1

39.8

2.0

Charlottenburg-Wilmersdorf

4

313,300

6.5

21,600

13.4

38.5

35.5

2.0

Spandau

5

233,600

9.2

19,200

23.9

45.1

16.4

1.6

Steglitz-Zehlendorf

6

288,400

5.7

25,200

13.3

38.3

33.9

1.9

Tempelhof-Schoeneberg

7

339,500

7.5

23,100

16.5

40.1

30.1

1.9

Neukoeln

8

321,500

12.4

18,600

25.2

38.9

21.6

1.7

Treptow-Koepenick

9

252,600

5.5

21,900

11.5

43.7

31.2

1.9

Marzahn-Hellersdorf

10

255,400

7.8

20,400

13.1

48.0

23.7

1.8

Lichtenberg

11

274,200

6

19,200

11.5

46.7

28.2

1.9

Reinickendorf

12

251,700

10

22,200

18.3

48.7

20.4

1.8

1.1.2 London socio-economic well-being data

CITY DISTRICTS

DISTRICT_NUM

POP_SIZE

UNEMP_RATE

MEDIAN_INC

LOW_EDU

MID_EDU

HIGH_EDU

EDU

London_City

 

9,006,352

5.0

27,400

15.4

12.2

66.8

2.4

City of London

1

7681

 

57,300

  

100

3.0

Barking and Dagenham

2

212,773

10.4

23,300

25

20.3

46.6

2.1

Barnet

3

397,049

3.7

28,800

13.5

13.1

66.7

2.4

Bexley

4

249,999

3.3

26,100

17.6

21.7

56.2

2.3

Brent

5

336,859

7.4

24,100

16.7

21

55.7

2.3

Bromley

6

332,733

5.7

30,400

14

16.3

66.2

2.5

Camden

7

252,637

7.2

35,500

11.9

7.5

74.2

2.5

Croydon

8

391,296

7.5

25,600

14

16.6

62.9

2.4

Ealing

9

350,784

3.7

26,100

18

8.3

66.7

2.3

Enfield

10

337,697

6.1

25,400

21.2

11.2

57.3

2.2

Greenwich

11

286,322

5.7

26,000

15.7

14.5

63.5

2.4

Hackney

12

281,740

1.6

28,000

13.8

8.6

65.1

2.3

Hammersmith and Fulham

13

184,050

2.9

32,300

13.6

6.6

73.2

2.5

Haringey

14

284,288

7

25,100

17.2

9.6

64.7

2.3

Harrow

15

255,369

2.1

27,300

14.9

14.9

65.4

2.4

Havering

16

257,511

3.5

26,100

23.9

20.8

46.4

2.0

Hillingdon

17

309,926

5

26,200

21.2

12.9

56.5

2.2

Hounslow

18

278,264

7

26,100

15.7

10.2

67.5

2.4

Islington

19

238,267

4.7

32,900

8.6

8.2

76.3

2.5

Kensington and Chelsea

20

159,301

6.3

39,500

13.5

8.2

72.7

2.5

Kingston upon Thames

21

179,581

5.1

30,200

13.2

10.9

73.1

2.5

Lambeth

22

334,724

4.5

29,200

12.2

9.8

73.1

2.5

Lewisham

23

310,324

2.8

26,700

13.6

11.6

70

2.5

Merton

24

209,421

 

29,500

16.9

12.5

66.3

2.4

Newham

25

353,245

5.5

22,500

20.7

8.7

62.1

2.2

Redbridge

26

305,910

4.4

27,400

15.2

15.1

61.3

2.3

Richmond upon Thames

27

199,419

4.2

36,600

9.4

9.5

77.8

2.6

Southwark

28

322,302

4.9

29,400

13.9

7.5

72.8

2.5

Sutton

29

207,378

2.2

27,300

15.2

15.4

64.3

2.4

Tower Hamlets

30

317,203

9.1

30,500

17.7

10.9

62.3

2.3

Waltham Forest

31

283,524

3.1

24,500

18.6

11.3

61.1

2.2

Wandsworth

32

324,400

4.1

35,000

8.7

10.2

77.7

2.6

Westminster

33

254,375

4.4

36,100

7.6

6.7

78.6

2.6

1.1.3 Vienna socio-economic well-being data

CITY DISTRICTS

DISTRICT_NUM

POP_SIZE

UNEMP_RATE

MEDIAN_INC

LOW_EDU

MID_EDU

HIGH_EDU

EDU

Vienna_City

 

1,741,246

11.4

20,956

41.3

20.7

38.0

2.0

1. Innere Stadt

1

16,268

4.6

32,852

22.2

22.9

54.9

2.3

2. Leopoldstadt

2

96,866

12.1

19,518

45.7

19.6

34.7

1.9

3. Landstraße

3

85,508

10.0

22,519

37.7

20.6

41.7

2.0

4. Wieden

4

30,989

7.8

24,208

27.7

21.1

51.2

2.2

5. Margareten

5

53,071

12.0

18,801

44

19.3

36.7

1.9

6. Mariahilf

6

30,117

9.4

22,133

29.8

21.6

48.6

2.2

7. Neubau

7

30,309

7.4

23,093

25.4

21.3

53.3

2.3

8. Josefstadt

8

23,930

6.4

23,336

24

21.8

54.2

2.3

9. Alsergrund

9

39,968

7.6

22,492

26.4

21.3

52.3

2.3

10. Favoriten

10

182,595

15.3

18,239

62.5

17.8

19.7

1.6

11. Simmering

11

92,274

13.7

19,369

61.4

19.6

19

1.6

12. Meidling

12

89,616

14.0

18,743

53.7

19.2

27.1

1.7

13. Hietzing

13

50,831

7.5

27,581

28.7

22.8

48.5

2.2

14. Penzing

14

86,248

10.6

20,227

43.5

21.8

34.7

1.9

15. Rudolfsheim-F.

15

73,527

13.8

16,766

53.1

18.3

28.6

1.8

16. Ottakring

16

97,565

12.6

18,701

51.4

19

29.6

1.8

17. Hernals

17

53,489

11.3

19,665

44.5

19.8

35.7

1.9

18. Währing

18

48,162

8.1

24,150

28.9

20.6

50.5

2.2

19. Döbling

19

68,892

9.0

25,588

32.4

22.4

45.2

2.1

20. Brigittenau

20

83,977

7.3

17,657

56.5

18

25.5

1.7

21. Floridsdorf

21

146,516

6.2

20,869

55.9

20.8

23.3

1.7

22. Donaustadt

22

165,265

4.8

22,515

49.7

23.1

27.2

1.8

23. Liesing

23

95,263

4.6

23,940

45.1

22.6

32.3

1.9

Appendix 3 - Plots of accessibility and socio-economic well-being data

1.1 Paris - accessibility plots

figure a
figure b
figure c
figure d

non-core

intersect

x

x2

x3

core

intersect

x

x2

x3

shops

−0.101

1

−1.947

4.377

 

−0.144

1

−2.569

−22.092

hospitals

− 0.095

1

− 1.923

6.095

 

−0.175

1

−2.509

−33.648

schools

−0.089

1

−1.886

6.596

 

−0.167

1

−1.933

−23.333

  1. Regression functions for accessibility by PT to shops, hospitals and schools in core and non-core of Paris FUA, normalised by multiplier of x

1.1.1 Paris - socio-economic well-being plots

figure e

1.1.2 Berlin - accessibility plots

figure f
figure g
figure h
figure i

non-core

intersect

x

x2

x3

core

intersect

x

x2

x3

shops

−0.091

1

−2.339

5.759

 

−0.098

1

−2.634

−5.670

hospitals

−0.089

1

−2.432

6.36

 

−0.1

1

−2.641

−6.421

schools

−0.096

1

−2.315

5.146

 

−0.102

1

−2.417

−4.487

  1. Regression functions for accessibility by PT to shops, hospitals and schools in core and non-core of Berlin FUA, normalized by multiplier of x

1.1.3 Berlin - socio-economic well-being plots

figure j

1.1.4 London - accessibility plots

figure k
figure l
figure m
figure n

non-core

intersect

x

x2

x3

core

intersect

x

x2

x3

shops

−0.091

1

−2.511

6.111

 

−0.111

1

−1.635

−3.952

hospitals

−0.0912

1

−2.404

5.936

 

−0.119

1

−1.358

−5.063

schools

−0.093

1

−2.399

5.719

 

−0.115

1

−1.505

−4.576

  1. Regression functions for accessibility by PT to shops, hospitals and schools in core and non-core of London FUA, normalized by multiplier of x

1.1.5 London - socio-economic well-being plots

figure o

1.1.6 Vienna - accessibility plots

figure p
figure q
figure r
figure s

non-core

intersect

x

x2

x3

core

intersect

x

x2

x3

shops

−0.062

1

−2.891

9.679

 

−0.091

1

−3.483

−4.684

hospitals

−0.082

1

−2.461

6.009

 

−0.095

1

−3.175

−4.635

schools

−0.072

1

−2.733

9.022

 

−0.096

1

−3.375

−4.861

  1. Regression functions for accessibility by PT to shops, hospitals and schools in core and non-core of Vienna FUA, normalized by multiplier of x

1.1.7 Vienna - socio-economic well-being plots

figure t

Appendix 4 - OLS regression models of accessibility and socio-economic well-being per city

1.1 Paris linear OLS regression models on access to shops within 30 min

figure u

1.1.1 Berlin linear OLS regression models on access to shops within 30 min

figure v

1.1.2 London linear OLS regression models on access to shops within 30 min

figure w

1.1.3 Vienna linear OLS regression models on access to shops within 30 min

figure x

Appendix 5 - Plots of predicted accessibility by C and PT on basis of OLS regression models (I), (III) and (IV)

As the sample sizes are very small (number of districts between n = 12 and n = 33), the analysis also includes predicted models to test the validity of the four models. Here, one hundred predicted values for the two dependent variables \( {\mathrm{Y}}_d^C \) and \( {\mathrm{Y}}_d^{PT} \) are created, while changing independent variables INCd and EDUd from their minimum to their maximum respectively and keeping the remaining variables at their sample means.

1.1 Paris plots

figure y

1.2 Berlin plots

figure z

1.3 London plots

figure aa

1.4 Vienna plots

figure ab

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eppenberger, N., Richter, M.A. The opportunity of shared autonomous vehicles to improve spatial equity in accessibility and socio-economic developments in European urban areas. Eur. Transp. Res. Rev. 13, 32 (2021). https://doi.org/10.1186/s12544-021-00484-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12544-021-00484-4

Keywords