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Article

Assessing the Impacts of Autonomous Vehicles on Urban Sprawl

by
Leon Booth
1,
Charles Karl
2,
Victoria Farrar
1 and
Simone Pettigrew
1,*
1
The George Institute for Global Health, University of New South Wales, Sydney, NSW 2000, Australia
2
Department of Transport Futures, National Transport Research Organisation (NTRO), Port Melbourne, Melbourne, VIC 3207, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(13), 5551; https://doi.org/10.3390/su16135551
Submission received: 30 April 2024 / Revised: 21 June 2024 / Accepted: 27 June 2024 / Published: 28 June 2024
(This article belongs to the Section Sustainable Transportation)
Browse Figures
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Abstract

:
Background: Urban sprawl adversely effects the sustainability of urban environments by promoting private vehicle use, decreasing the viability of active/public transport, and increasing the cost of public service provision. Autonomous vehicles could change the desirability of different residential locations due to resulting changes to urban design and decreased value of travel time. Methods: Adult Australians (n = 1078) completed an online survey that included a description of a future where autonomous vehicles are widely available. The respondents reported anticipated changes in residential location in this autonomous future. Frequency analyses were conducted, and three logistic generalised linear models were run to identify factors associated with staying in the same area or moving to higher- or lower-density locations. Results: Autonomous vehicles are likely to have mixed effects on people’s desired residential locations. Most respondents (84%) elected not to move location, 11% intended to move to lower-density locations, and 6% to higher density locations. Reasons for moving included a desire for more space, the ease of travelling in urban areas, and the reduced value of travel time. Conclusion: The introduction of autonomous vehicles will need to be managed to avoid fostering increased urban sprawl and the associated negative consequences. Strategies that increase the liveability of higher density urban environments are likely to discourage urban sprawl in a future characterised by autonomous transport options.

1. Introduction

Over recent decades, urban sprawl has been observed in many cities around the world [1]. This involves the boundaries of cities expanding to occupy previously rural areas [2,3], with the newly urbanised areas typically characterised by fragmented, low-density suburban housing [2]. The demand for this style of housing has increased due to growing populations, rising income levels, preferences for larger dwellings, restrictions on higher-density developments, and reduced costs associated with private car ownership [1]. Urban sprawl complicates town planning due to the increased distance between new residences and existing public transport options, amenities, and places of employment [3], and it results in a higher per capita cost of providing public services such as roads, water, emergency services, education, waste collection, sewerage, and parks [4,5].
Lower-density neighbourhoods are less sustainable than higher-density neighbourhoods [6]. For example, larger suburban homes require more energy for heating and cooling compared to smaller urban homes/apartments [7]. In addition, urban sprawl is associated with poorer health outcomes [8]. The distal location of outer areas reduces the viability of active and public transport, increasing the reliance on private cars [9,10]. This contributes to lower levels of physical activity [9,10], greater greenhouse gas emissions. and poorer air quality [1,6]. Longer commute times associated with travelling from urban fringes also reduces time spent with family, engaging in physical activity, cooking, and sleeping [11]. Overall, these factors are thought to explain the identified link between greater levels of urban sprawl and increased rates of obesity [8,12,13,14]. Due to numerous issues associated with urban sprawl, there are increasing calls for the ‘re-densification’ of urban areas [1,8].
An upcoming change that could affect transport systems and urban sprawl is the introduction of autonomous vehicles (AVs). AVs are currently being trialled around the world and, in some locations, are already operating as commercial autonomous taxi services [15,16]. It is predicted that by 2050, the majority of passenger vehicles will be autonomous [17]. The introduction of AVs is expected to result in numerous benefits such as fewer deaths and injuries on roads due to these vehicles being safer than human-driven equivalents, greater efficiencies in transportation networks, and increased mobility for people who are unable to operate conventional cars such as elderly people and those with disabilities [18,19,20]. However, there are also potential disbenefits, including job losses in certain industries, increased sedentism resulting from more convenient and affordable transport options, and more congestion on roads due to vehicles travelling empty.
There are several changes to urban environments that could result from AVs. In the first instance, if AVs are predominantly shared instead of being privately owned, they could reduce the demand for car parking spaces and curb-side parking, enabling this land to be repurposed for other uses such as green spaces and high-density residential complexes [21,22,23]. Second, AVs will be able to operate in narrower lanes compared to conventional cars, reducing the space required for roads and allowing for the construction of more active transport infrastructure by widening footpaths and installing dedicated cycling paths [24]. Third, as AVs will afford substantial safety improvements compared to human-driven vehicles, they could make urban areas more appealing for active transport options such as walking and cycling due to the decreased risk of harm while using these more vulnerable modes of transport [21]. Fourth, urban areas might become more attractive as residential locations if AV transport options enable easy and rapid transport in these areas compared to the current options [21,25]. Fifth, it is expected that AVs will enable people to engage in recreational and work-related activities while travelling because they will no longer need to operate the vehicle, thereby reducing the burden of travel time [26,27,28,29,30]. Finally, if AVs increase the speed and capacity of road networks, they might allow people to commute further without incurring an increase in time spent commuting [26,27,28]. However, while there are many potential positive changes to urban environments that could result from AVs, they could also lead to much greater congestion if a predominantly private ownership model emerges [21,26]. In addition, there is likely to be greater urban sprawl if people are willing to travel further distances because of the ability to engage in alternative activities while commuting [26,31,32]
Computational modelling techniques have been frequently used to examine whether AVs are likely to affect urban sprawl [25,26,31,33,34]. These studies typically test different AV scenarios with varying assumptions such as how much AVs will cost, how fast they will travel, whether implementation will favour private or shared AVs, whether AVs will result in more efficient road networks, and varying types of parking requirements for AVs [26,33]. Most modelling studies to date predict that AVs will result in increased urban sprawl because they will (i) be cheaper per distance travelled than conventional cars, (ii) be faster than conventional cars due to higher permitted operating speeds and improvements in road network efficiency, and (iii) decrease the value of travel time due to people being able to engage in other activities while travelling because they do not need to drive [25,31,34,35,36,37]. Scenarios examining shared AV implementation generally predict less drastic increases in urban sprawl compared to private ownership scenarios [26,31]. Furthermore, some modelling studies predict AVs could supress urban sprawl if car parks become unnecessary and land is repurposed to build more high-density housing and desirable amenities in central locations [31,35].
To a lesser extent, surveys have also been used to assess whether people are likely to change their residential location once AVs are widely available [33]. The results of these studies vary from 9% to 42% of people saying they would move further away [32,38,39,40]. There could be geographic and/or cultural factors that influence the propensity to move in an AV future, with the more extreme results in terms of the proportion of people being willing to move further away observed in studies with Chinese participants [38]. Due to AVs not yet being widely available, survey studies typically include a brief explanation that outlines how AVs might be different to conventional vehicles (i.e., cheaper to run, faster, and allow other things to be carried out when using them) [38,39,40].
Survey research has identified demographic differences in terms of anticipated location changes when AVs are available. In particular, younger people and those with lower incomes seem to be more inclined to move in general (to lower- and high-density locations) [32,41]. In one US study, those who preferred non-car methods of transport, such as public and active transport, indicated a preference for moving to higher-density areas in an AV future. By comparison, participants were more likely to want to move to less densely populated areas when they were attracted to the idea of being able to undertake leisure or work activities in AVs, which was attributed to the reduced value of travel time making longer trips less onerous, increasing the attractiveness of living in more remote locations [32].
The present study builds on previous survey research by investigating expected changes in preferred residential location once AVs are in common use by enabling participants to provide more informed responses by providing them with a detailed, evidence-based description of a future featuring AVs. The study aims were to understand anticipated shifts in residential locations, identify underlying reasons for the anticipated changes, and assess whether specific demographic groups may be more or less likely to change where they live in a future characterised by the widespread availability of AVs. The findings of this research can provide valuable insights into how transport automation may affect urban sprawl and the pre-emptive strategies that may be required to ensure optimal outcomes for individuals and cities.

2. Materials and Methods

This paper is part of larger project examining the potential public health consequences of AVs (blinded for review). Recruitment was conducted by the ISO-accredited web panel provider Pureprofile, resulting in a sample of 1078 Australian adults. Quotas were used to recruit a roughly representative sample across age, gender, and residential location characteristics. A demographic breakdown of the sample is detailed in Table 1. Members of Pureprofile’s web panel were invited by email to participate in an online survey and were informed upon accessing the survey link that the research aimed to explore the impact of AVs on lifestyle behaviours. This study was approved by a university human research ethics committee and all participants provided informed consent.

2.1. Survey Items

Respondents provide their demographic information (age, gender, and postcode (to derive their socioeconomic status according to the Australian Bureau of Statistics (2018)). The category of residential location was identified by asking “Where do you currently live?” (response options: an urban area, a suburban area, and a regional/remote area). The use of several transport options was examined by asking respondents “In a typical week, which of the following modes of transport do you use to get around? Select all that apply” (response options: personal vehicle (car, truck, motorcycle, etc.), ride-hailing services (e.g., Uber, taxis), public transport (e.g., bus, train, light rail, ferry), bicycle/skateboard/scooter, and walking). If respondents used an examined transport option, they also reported the average number of hours they used that option per week. As per previous research [19,46], respondents’ attitudes to AVs were assessed by asking “How do you feel about fully autonomous vehicles being widely used in the future?” (response options: 1 ‘Very negative’ to 5 ‘Very positive’).
Respondents were then presented with a vignette describing an autonomous future. The vignette was developed by synthesising insights gathered from interviews with 52 experts spanning various fields such as public health, transportation, urban planning, and telecommunications (blinded for review). The vignette referred to various aspects of vehicle automation identified in the expert interviews, including the availability of private autonomous vehicles, shared autonomous vehicles, and autonomous public transport options. Improvements to active transport infrastructure and the availability of autonomous delivery services were also described. The vignette is presented in Figure 1.
Following exposure to the vignette, respondents were asked “If you were living in this world, would you change where you live compared to now?” (response options: no, I would live in a similar area; yes, I would move to an urban area; yes, I would move to a suburban area; and yes, I would move to a regional/remote area). Those who anticipated they would move to another area were then asked, “Can you please describe why you might change where you live?” (response options: would like more space, can relax during travel time, easier to travel further, can be productive during travel, easier to travel in urban areas, more footpaths, more cycleways, nicer to live in urban areas, and other (open response)). Relevant survey items used in this paper are available in the Supplementary Materials.

2.2. Analyses

For respondents who indicated they would change where they live, their current residential location was compared to their anticipated residential location to determine whether they envisaged moving to a higher- or lower-density location. Descriptive analyses for selected reasons for moving location were also conducted. Three binary logistic generalised linear models were run to identify factors associated with choosing to (i) stay in the same location, (ii) move to a higher-density location, and (iii) move to a lower-density location. For each model, the dependent variable was selecting the respective location versus not choosing that option (binary outcome). The independent variables were age, gender, current residential location (urban, suburban, and regional/rural), socioeconomic status, current time spent travelling using the examined transport options, and attitude to AVs. Age, socioeconomic status, current time spent travelling using the examined transport options, and attitude to AVs were all treated as continuous variables. Gender and current residential location were treated as nominal (dummy-coded) variables. For all models, respondents selecting non-binary gender or who preferred not to answer the gender question were not included in the analyses due to insufficient numbers to make meaningful comparisons. For the model examining predictors of moving to a higher-density location, those who currently lived in an urban location were excluded from the model as they could not move to a higher-density location. Likewise, those who currently lived in a regional/remote area were excluded from the model examining predictors of moving to a regional/remote area in an autonomous future.

3. Results

Most (84%) respondents expected that they would continue to stay in a similar area to where they currently lived. Around twice as many anticipated moving to lower-density (11%) compared to higher-density areas (6%) (see Figure 2). Respondents who reported intending to move to a lower-density area most frequently indicated it was to have more space (38%), followed by ease of travelling further (27%), being able to relax during travel time (25%), and being productive during travel (11%). Those anticipating moving to higher-density areas most often indicated it was because it is easier to travel in urban areas (44%), followed by easier to travel further (31%), more footpaths (30%), nicer to live in urban areas (25%), more cycleways (20%), can be productive during travel (16%), and can relax during travel time (8%) (see Table 2). Virtually no respondents selected the ‘other’ open-ended response option.
The generalised linear models all explained a significant amount of variance in the examined dependent variables, with the results of the likelihood ratio chi-square omnibus tests for each model being significant (decision to stay in the same area (ꭓ2 (10) = 47.69, p < 0.001), move to a lower density area (ꭓ2 (10) = 19.74, p = 0.032), and move to a higher-density area (ꭓ2 (10) = 43.23, p < 0.001)). The models revealed that older respondents, those living in regional/remote locations, and those in higher socioeconomic status areas were least likely to move location in an autonomous future. In contrast, younger respondents and those living in lower socioeconomic status areas were more likely to anticipate moving to a higher-density location. Further, those living in a regional/remote area were more likely than those living in a suburban area to see themselves moving to a higher-density location. Intentions to move to a lower-density area were more common among younger respondents and those living in an urban (vs. suburban) location. Attitude to AVs was not associated with any of the examined outcomes. See Table 3 for a summary of the results.

4. Discussion

Consistent with the results of previous stated preference surveys [39,40], most respondents in the present study anticipated living in a similar area to their current location once AVs are widely available across both personal and public transport alternatives. However, a sizeable minority (16%) indicated a desire to change residential location, most of whom intended moving to lower-density areas (11% of the total sample). This is highly consistent with previous studies conducted in the US that found 11–12% of respondents intended moving away from urban areas [39,40] but different to another US study that found nearly one-third of respondents would consider moving further away once AVs are available [38]. The latter outcome could be attributable to the use in that study of a differing description of an autonomous future that focused on the value of travel time and speed benefits afforded by AVs.
Age and socioeconomic status were found to be associated with different location preferences. Older respondents were more likely than younger respondents to intend to stay in a similar location in an autonomous future, a finding that was consistent with similar survey research examining urban sprawl and AVs [32,38,41]. This difference could be partially due to differing rates of home ownership and moving patterns among younger and older cohorts. In Australia and the U.S., home ownership is substantially higher among older people, whereas younger people are more likely to rent [47,48]. The identified benefits of renting compared to owning a home include flexibility to move to another home and the ability to trial living in a location before buying [49,50]. In addition, Australian data show that older home owners move less frequently than younger people and renters [51], likely due to their investment in their current home and the substantial costs associated with selling and buying.
A tendency for homeowners to remain in the same location might also explain why respondents from higher socioeconomic areas were less likely to move in the scenario. The Australian Bureau of Statistics’ [42] calculation for socioeconomic status that was used in the present study assigns higher scores to areas with greater rates of home ownership and lower scores to areas with higher unemployment, an overcrowding of housing, and poor internet access. The latter characteristics are likely to make an area less desirable [52], which could account for respondents from lower socioeconomic areas being more likely to anticipate relocating. Previous research has also found people with lower incomes are more likely to envisage moving in a future with AVs [32].
The reasons cited for moving to higher- and lower-density areas suggest possible policy responses to discourage urban sprawl in an autonomous future. A desire for more space was the most commonly selected reason for moving to a lower-density location. In Australia, the average floor space of freestanding homes is substantially larger than apartments [53], and a lack of larger apartments suitable for family-sized households is an identified issue in Australian urban planning [54]. To encourage higher-density living in an autonomous future, policy makers could mandate that a minimum proportion of apartments in new developments is suitable for larger households.
In the present study, reasons for moving to high-density locations commonly centred around the increased ease of travel in urban areas. The future scenario presented in the vignette was characterised by affordable and convenient ride-hail and public transport options, comprehensive active transport infrastructure, and rapid autonomous delivery services, which might have increased the perceived liveability of urban areas. Modelling studies have predicted that better mobility in urban areas would increase the desirability of higher-density locations in an autonomous future [25]. Present-day cities that are characterised by transportation environments with comprehensive public transport infrastructure have higher population densities and less car use [55,56]. By comparison, cities that have developed their infrastructure to increase the reach and/or capacity of road networks typically result in more sprawl and car use [55,56]. Policy makers should ensure that the transportation opportunities offered by autonomous transport technologies are leveraged to enhance the affordability and availability of transport options that are less car-centric to lessen the demand for private vehicles and discourage urban sprawl while improving the liveability of urban areas [37].

Limitations and Future Research

The results of this study should be considered in the context of several limitations. First, predictions based on respondents’ reactions to the vignette are tied to the assumptions within the vignette, potentially leading to disparate behavioural outcomes if the actual autonomous future diverges from the depicted one. However, involving experts in the development of the vignette should have enhanced its validity. Second, respondents’ behavioural intentions may not accurately reflect their future actions due to the intentions–behaviour gap [57], meaning when the transportation benefits afforded by AVs are an observable reality, people might be more inclined to change their residential location than they currently expect. Third, while efforts were made to ensure a representative sample, utilising a web-panel provider could introduce biases (e.g., excluding people without internet access). Fourth, while the present study examined the relationship between residential location and the propensity to move in an autonomous future, more detailed geographical features of the respondents’ current and preferred residential locations were not assessed, preventing an examination of how these features might affect movement intentions. Finally, the sample’s restriction to those residing in Australia limits generalisability to other geographic and cultural contexts. Future research should be conducted with more diverse samples and different AV scenarios to further validate findings across varied populations and potential autonomous futures. Furthermore, more detailed information should be gathered on transport and geographical-related features of preferred environments to determine how these factors could influence decisions to change residential location in an AV future. This could be conducted in tandem with land use/transport interaction models to more comprehensively examine how AVs might affect urban sprawl.

5. Conclusions

The findings of this study indicate that the widespread adoption of autonomous transport technologies could impact on desired residential locations. The perceived ease of travel and space availability appeared to play important roles in respondents’ decisions regarding potential moves, highlighting the importance of transportation infrastructure and housing policies in shaping urban landscapes. The present findings thus provide important insights for policy makers aiming to curb urban sprawl and promote sustainable urban development in an autonomous future by emphasising the need for interventions that improve transportation accessibility and liveability in urban areas.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16135551/s1.

Author Contributions

Conceptualization, L.B., C.K. and S.P.; Methodology, L.B. and S.P.; Formal analysis, L.B.; Data curation, L.B., V.F. and S.P.; Writing—original draft, L.B.; Writing—review & editing, L.B., C.K., V.F. and S.P.; Project administration, V.F. and S.P.; Funding acquisition, C.K. and S.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Health and Medical Research Council (grant number 2002905) and Australian Research Council (grant number DP230102623).

Institutional Review Board Statement

Ethics approval number HC210207.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 16 05551 g001
Figure 1. Vignette of an autonomous future presented to respondents.
Figure 1. Vignette of an autonomous future presented to respondents.
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Figure 2. Anticipated changes in residential location (n = 1078).
Figure 2. Anticipated changes in residential location (n = 1078).
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Table 1. Survey sample profile.
Table 1. Survey sample profile.
CharacteristicTotal
(n = 1078)		Australia ^
n%%
Gender *
    Male5345050
    Female5415050
    Non-binary2<1<1
Age (years)
    18–343152929
    35–543573334
    55+4063837
Socioeconomic status #
    Low (deciles 1–4) 3533340
    Medium (deciles 5–7)3373130
    High (deciles 8–10)3883630
Location
    Urban1141473
    Suburban 66763
    Regional/remote2572427
* 1 respondent elected to not respond to the gender question. # Derived from postcode using Australian Bureau of Statistics [42]. ^ Age and male/female gender proportions estimated using Australian Bureau of Statistics [43], non-binary proportion using Australian Bureau of Statistics [44], socioeconomic status taken from Australian Bureau of Statistics [42], and residential location taken from Australian Institute of Health and Welfare [45]. Note that national data do not differentiate between suburban and urban areas.
Table 2. Reasons provided for moving location in an autonomous future.
Table 2. Reasons provided for moving location in an autonomous future.
Reason for Movingn%
To a lower density area (n = 114)
    Would like more space4338
    Easier to travel further3127
    Can relax during travel time 2925
    Can be productive during travel 1311
To a higher density area (n = 61)
    Easier to travel in urban areas2744
    Easier to travel further1931
    More footpaths1830
    Nicer to live in urban areas1525
    More cycleways1220
    Can be productive during travel1016
    Can be relax during travel58
Table 3. Predictors of anticipated changes in residential locations in an autonomous future.
Table 3. Predictors of anticipated changes in residential locations in an autonomous future.
FactorsIntend to Move to Higher-Density Location
OR [95% CI]
(n = 931)		Intend to Stay in Similar Location
OR [95% CI]
(n = 1075)		Intend to Move to Lower-Density Location
OR [95% CI]
(n = 819)
Age0.96 [0.94, 0.98] ***1.02 [1.01, 1.04] ***0.98 [0.97, 0.99] *
Gender (female)0.75 [0.43, 1.29]1.08 [0.77, 1.51]1.05 [0.7, 1.57]
Current residential location ^
    Urban-RefRef
    SuburbanRef1.32 [0.84, 2.07]0.44 [0.27, 0.7] ***
    Regional/remote2.08 [1.14, 3.8] *2.68 [1.45, 4.97] **-
Socioeconomic status (decile) 0.89 [0.81, 0.99] *1.07 [1, 1.14] *0.96 [0.9, 1.04]
Time spent traveling via
    Conventional car 1.02 [0.99, 1.04]0.99 [0.97, 1.01]1.01 [0.98, 1.03]
    Conventional ride-hail 1.04 [0.98, 1.11]0.98 [0.94, 1.04]0.99 [0.91, 1.07]
    Conventional public transport 1.03 [0.97, 1.08]0.99 [0.96, 1.03]1 [0.96, 1.04]
    Bicycle/scooter/skateboard0.88 [0.72, 1.08]1.04 [0.96, 1.12]1 [0.91, 1.08]
    Walking 1.00 [0.96, 1.04]1.01 [0.98, 1.04]0.99 [0.95, 1.02]
Positive attitude to AVs 1.16 [0.88, 1.52]1.00 [0.85, 1.18]0.92 [0.75, 1.13]
* < 0.05, ** < 0.01, *** < 0.001. ^ Ref = reference category; OR = odds ratio; 95% CI = 95% confidence interval. Hyphens indicate that no data were available for that group due to those respondents already living in the highest or lowest density of residential location category, making it impossible for these respondents to move a higher- or lower-density category, respectively. This also resulted in different sample sizes for each model.
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Booth, L.; Karl, C.; Farrar, V.; Pettigrew, S. Assessing the Impacts of Autonomous Vehicles on Urban Sprawl. Sustainability 2024, 16, 5551. https://doi.org/10.3390/su16135551

AMA Style

Booth L, Karl C, Farrar V, Pettigrew S. Assessing the Impacts of Autonomous Vehicles on Urban Sprawl. Sustainability. 2024; 16(13):5551. https://doi.org/10.3390/su16135551

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Booth, Leon, Charles Karl, Victoria Farrar, and Simone Pettigrew. 2024. "Assessing the Impacts of Autonomous Vehicles on Urban Sprawl" Sustainability 16, no. 13: 5551. https://doi.org/10.3390/su16135551

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