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

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 STEPS¹ model created by Shaheen et al. [45] and adapted to include the usage of AVs by Fleming [12]. Overall, these findings raise the question:

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).

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.

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 (EDU_d) 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 (INC_d) and EDU_d identified within all four cities analysed, the authors also expect a significant effect between INC_d 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.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 INC_d less significant in these two cities. Meanwhile, if INC_d has a significant effect on \( {\mathrm{Y}}_d^{C, PT} \), it has greater predictive ability than EDU_d in relative city terms. Therefore, INC_d 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 (UEM_d) on accessibility by C (\( {\mathrm{Y}}_d^C \)) in Paris and on accessibility by PT (\( {\mathrm{Y}}_d^{PT} \)) in Vienna. While UEM_d is one of the most often measured and used economic socio-economic well-being indicators, it is less stable over time than INC_d and EDU_d, 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 EDU_d 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.