Predicting Changes in the Built Environment in the era of Vehicular Automation: A Review

Abstract

We conducted a comprehensive investigation into the relationship between autonomous vehicles (AVs) and the built environment (BE). We explored how various potential changes in transportation by AVs may influence the BE using the D-variables: density, diversity, design, and destination accessibility. Our review revealed that AVs could cause drastic changes to all aspects of the BE; however, the extent of these changes strongly depends on uncertainties in technology, policy, and transportation modes. Further research into AVs–BE relationships should focus on developing comprehensive scenarios, accounting for these uncertainties and regional and social characteristics.

Keywords

autonomous vehicles density diversity design destination accessibility land use–transportation interaction urban transportation

Introduction

The introduction of autonomous vehicles (AVs) is expected to profoundly impact the built environment (BE) (Duarte and Ratti 2018). AVs can cause rapid and significant changes to the urban structure (Milakis, Kroesen and van Wee 2018; Fraedrich et al. 2019; Stead and Vaddadi 2019) as well as accessibility (Meyer et al. 2017; Milakis, Kroesen and van Wee 2018; Dianin, Ravazzoli and Hauger 2021), possibly more than any other transportation technology. They can contribute to facilitating the densification of the city center or additional urban sprawl (Milakis, Kroesen and van Wee 2018), affecting accessibility across cities and regions.

Although AVs are expected to have a general impact on travel behavior and the urban spatial structure, the specific expression of these impacts remains unclear (Hawkins and Habib 2019; Dianin, Ravazzoli and Hauger 2021). There appear to be great concerns and even greater uncertainties regarding how vehicular automation in cities may affect a core aspect of city planning: the interaction between transportation system and land use (Fraedrich et al. 2019). Studies examining the AV–BE relationships have not comprehensively explained yet how various changes in transportation caused by vehicular automation might affect the BE (e.g., Duarte and Ratti 2018; Gelauff, Ossokina and Teulings 2019; Cordera et al. 2021). Furthermore, limited research explored the impacts of vehicular automation on all the key components of the BE, such as the density and diversity of land use (Ewing and Cervero 2010). Fraedrich et al. (2019) emphasized the critical need to comprehend the potential changes to various BE elements including location, form, and design in order to prepare urban transportation policy in the era of AVs.

Discussion of AVs inevitably involves several assumptions because vehicular automation is an incomplete and unrealized technology at the present day (Soteropoulos, Berger and Ciari 2019; Harb et al. 2021; Wang and Zhang 2021). As such, there are differing views regarding the potential impacts of AVs in academic literature (e.g., Milakis et al. 2017; Fraedrich et al. 2019), the discussions of public officials (e.g., Freemark, Hudson and Zhao 2019), and the opinions of citizens (e.g., Lu et al. 2017; Ashkrof et al. 2019). Some literature focused on the optimistic scenario, expecting substantial changes to society while others showed pessimistic attitudes toward an AV-driven future. For instance, the optimistic scenario highlights potential benefits such as decreased number of cars, decreased travel time, and increased safety (Fagnant and Kockelman 2015; Freemark, Hudson and Zhao 2019). The pessimistic scenario emphasizes issues such as increased total vehicle miles traveled, social exclusion, and a decline in public transit (Papa and Ferreira 2018; Freemark, Hudson and Zhao 2019). The differing views on the potential impact of AVs are attributed to various uncertainties in technology, political and regulatory issues, and transport mode (Curtis et al. 2021; Dianin, Ravazzoli and Hauger 2021).

To address the gap in AV–BE research, this review aims to examine the potential changes in transportation caused by AVs and explore the possible effects of these changes on key elements of the BE. We conduct a comprehensive review of both qualitative and quantitative studies of AV–BE relationships, focusing on how the potential transportation changes in the era of AVs (e.g., car-sharing culture, change in road capacity, change in travel costs and utility) might affect the D-variables – e.g., density, diversity, design, and destination accessibility (Ewing and Cervero 2010). We consider the various uncertainties in technology, policy and regulation, and transport modes (e.g., the level of technological advancement, the public's perception of AVs, and the relative ratios of AVs to conventional cars). We cross-review literature that deals with different assumptions on the exogenous factors affecting the direction and scale of change. Our review has been organized as follows: we describe the research framework, a discussion on the potential changes to transportation in the context of AVs, a discussion on the possible effects of these potential transportation changes on BE, and the research agenda that needs to be investigated by future AV–BE research.

Framework

A Review of Previous Studies Regarding the Effect of AVs on the BE

The relationship between transportation and the BE has been a long-standing issue in urban and transportation planning. Various theories and models have been developed to measure and analyze this relationship, such as the urban economics model, the gravity model, and the four-step model (Acheampong and Silva 2015; Kii et al. 2016). In particular, the land use–transportation interaction (LUTI) model has been actively used to examine the interactions between transportation and the BE from an integrated perspective (e.g., Wegener and Fuerst 2004; Cordera et al. 2021; Soteropoulos, Berger and Ciari 2019). The LUTI model explains that land use and transportation are interdependent and their interaction is theoretically underpinned by the so-called land use-transportation feedback cycle (Soteropoulos, Berger and Ciari 2019). Despite a large body of research on the aforementioned relationships, there is still uncertainty regarding the true nature, size, and strength of any causal relationships between travel behavior and the BE (Heinen et al. 2018).

Some studies have utilized the LUTI model to investigate the potential impacts of AVs on the BE (e.g., Duarte and Ratti 2018; Fraedrich et al. 2019; Gelauff, Ossokina and Teulings 2019; Cordera et al. 2021). However, most of these studies rarely considered how specific changes in transportation such as variations in traffic volume, traffic flow, and travel costs might affect the BE. Furthermore, only a few studies examined different aspects of the BE when investigating the impacts of AVs, such as the density and mix of land use, the layout of urban development, and the accessibility to specific locations (e.g., Cervero and Kockelman 1997; Duarte and Ratti 2018; Stead and Vaddadi 2019; Dianin, Ravazzoli and Hauger 2021). These previous studies made specific assumptions based on their respective rationales (Wang and Zhang 2021), rather than considering various uncertainties that might affect the AV–BE relationship.

There is a clear need to address the gaps in AV–BE research by considering: the potential effects of the various transportation changes on the key aspects of the BE and uncertainties that might affect the AV–BE relationships such as technology, regulations, and transport and land use policies (Yap, Correia and van Arem 2016; Nazari, Noruzoliaee and Mohammadian 2018; González-González, Nogués and Stead 2019).

Research Framework

To examine the AV–BE relationship, our study first identified several key changes in transportation that may result from the commercialization of AVs. AV technology is expected to rapidly change certain aspects of transportation compared to previous advances in transportation technology. Previous studies have indicated that AVs would lead to modal shifts (Heilig et al. 2017), improvements in public transportation (Bösch et al. 2018), changes in the total number of vehicles (Alessandrini et al. 2015), higher driving densities (Talebpour and Mahmassani 2016), the emergence of shared AVs (Fagnant and Kockelman 2014), road capacity enhancements (Levin and Boyles 2016), a reorganization of the transportation signal system (Lu et al. 2020), travel cost reductions (Correia et al. 2019), changes in the level of mobilityof various groups (Harper et al. 2016), an increase in the number of unoccupied cars (Zhang, Guhathakurta and Khalil 2018), increased demand for travel (Meyer et al. 2017), and increased demand for automated parking (Correia and van Arem 2016). The aforementioned effects can be classified into three categories: travel choices, traffic volume and flow, and travel costs (Harb et al. 2021; Milakis et al. 2017). The above studies have identified potential impacts in each of these categories, including a culture of shared mobility as well as changes to road capacity, travel costs, and utility.

We analyzed the effects of these changes in transportation on the BE by considering the three traditional D-variables (i.e., density, diversity, and design) that have historically been used to measure changes in the BE (Cervero and Kockelman 1997) and a fourth D-variable, destination accessibility (Ewing and Cervero 2010). These D-variables have been widely used in transportation–BE studies, as they allow the examination of changes in the BE at a more detailed level (Stevens 2017). Our study investigates potential effects on BE variables including residence and activity aggregation (density), the differentiation of various urban phenomena in space (diversity), the layout of road networks (design), and changes in the accessibility of specific destinations (destination accessibility).

The research framework of our study is based on the idea that travel behavior and the BE may influence each other through attitudes (Lin, Wang and Guan 2017; Heinen et al. 2018; Mouratidis, Peters and van Wee 2021). Heinen et al. (2018) described the interactions between travel behavior and the BE as follows: (a) attitudes are an antecedent to travel behavior as well as the BE—both are caused by a shared predictor (attitudes) and are consequently correlated; (b) travel behavior influences attitudes, which consequently determines the location of residences, which in turn influences the BE; (c) the BE influences attitudes, which affects travel behavior. This circular causation could be formed by the intervention of attitudes between travel behavior and the BE (Cao, Mokhtarian and Handy 2009). While our framework is based on the LUTI model, we only focus on the impact of transportation changes on the BE rather than on the inverse relationship. This is because studies on the impacts of the BE on AVs are far more limited and are still in their early stages. A number of studies that examined the implications of the BE for transportation (e.g., the relation between urban density and travel mode choice) did not fully consider the context of AVs yet. Figure 1 describes the framework for our review of the impacts of AVs on the BE.

Figure 1.

Research concept.

Due to the wide range of uncertain factors, there is no single answer to how the BE will change in an AV-dominated era. Uncertainties affecting the AV–BE relationship can be related to technology (e.g., the timing of the launch of full-fledged AVs), policy and regulations (e.g., specific land use policies), and transportation modes (e.g., the proportion of AVs in the transportation fleet and preferences in public transportation) (Fraedrich et al. 2019; Cugurullo et al. 2021). In reality, these uncertainties highly depend on active and conscious political choices that are made in society (e.g., investing in certain types of transportation infrastructure, modifying fuel tax, amending parking standards, subsidizing transit, and focusing urban development around transit nodes). Our study has attempted to account for some of these uncertainties based on available research rather than seeking for a single explanation for the effects of AVs on the BE.

Literature Selected for Analysis

Relevant peer-reviewed papers published in English were collected from the Web of Science and Google Scholar. Since the issuance of the National Highway Traffic Safety Administration's Preliminary Statement of Policy in 2013 (NHTSA 2013), academic discussions on AVs have expanded their focus from technical aspects to broader social implications (e.g., Anderson et al. 2014; Fagnant and Kockelman 2014). To account for this trend, we collected relevant studies published between 2014 and 2021. Keywords for AVs (e.g., AVs, autonomous driving, automated vehicles, automated driving, and self-driving) and the BE (e.g., BE, urban, city, spatial, planning, form, and structure) were utilized as search terms to collect the initial set of related literature. When additional keywords related to the D-variables were found in the collected studies, the search was repeated until no new keywords appeared. Both a pre-screening and a full-text screening of the collected papers were conducted. The final set of search keywords is presented in Table 1.

Table 1.

Literature Search Keywords.

		Keywords
AVs (autonomous vehicles)		AVs (autonomous vehicles), autonomous driving, automated vehicles, automated driving, self-driving
Impact of AVs on transportation	Travel choices	SAV (shared autonomous vehicle), PAV (private autonomous vehicle), shared, sharing, choice, mode, modal, public transit, first-mile, last-mile
	Traffic volumes/ flows	Traffic, capacity, demand, road, VOT (value of time), VTTS (value of travel time saving)
	Travel cost	Cost, utility, value, consumption, saving
Built environment		Built environment, urban, city, spatial, planning, form, structure
D-variables	Density	Density, sprawl, compact
	Diversity	Diversity, land use, entropy, residential, workplace, job
	Design	Design, road network, road design, road capacity, lanes, transport infrastructure, transportation infrastructure
	Destination accessibility	Destination accessibility, accessibility, proximity, commute

The “Changes in Transportation in an era of Vehicular Automation” section reviews the papers relating to AVs and their impact on transportation. We identified the most significant changes in the transportation system in each of the three AV impact categories (travel choices, traffic volumes and flow, and travel costs). The “The Impact of AVs and Their Effects on the BE” section reviews literature related to AVs and the BE, and the effect of AVs on each D-variable was analyzed. The “Discussion and Conclusions” section proposes a future research agenda to better understand the AV–BE relationship.

Changes in Transportation in an era of Vehicular Automation

In this section, we identify and categorize significant transportation changes that can be brought on by AVs. These changes will be considered when we examine the effects of the changes on BE in the following section. Our review of the literature (Table 2) on AVs’ likely impacts on transportation shows that the most significant one can be an increase in car-sharing (Fagnant and Kockelman 2014; Krueger, Rashidi and Rose 2016; Levin et al. 2017; Ma et al. 2017; Martinez and Viegas 2017; Nazari, Noruzoliaee and Mohammadian 2018; Singh et al. 2022) and an enhancement in road capacity (Levin and Boyles 2016; Meyer et al. 2017; Olia et al. 2018; Lu et al. 2020). AVs can also reduce per-mile travel costs and utility (Singleton 2019; Compostella et al. 2020). The details of each of these impacts are discussed below in detail.

Table 2.

Key Studies and Findings Regarding the Impacts on Transportation in an AV-Dominated era.

	Travel choice	Traffic volumes/flows	Travel cost
Significant impacts	Car-sharing culture	Enhancement of road capacity	Reduction per-mile cost/utility
Selected papers	Fagnant and Kockelman (2014) Gkartzonikas and Gkritza (2019) Levin et al. (2017) Ma et al. (2017) Martinez and Viegas (2017) Nazari, Noruzoliaee and Mohammadian (2018) Singh et al. (2022) Yap, Correia and van Arem (2016) Zhang, Guhathakurta and Khalil (2018)	Harper et al. (2016) Hult et al. (2016) Levin and Boyles (2016) Lu et al. (2020) Manivasakan et al. (2021) Meyer et al. (2017) Olia et al. (2018) Talebpour, Mahmassani and Elfar (2016) Zhang, Guhathakurta and Khalil (2018)	Compostella et al. (2020) Kolarova, Steck and Bahamonde-Birke (2019) Krueger, Rashidi and Dixit (2019) Le Vine, Zolfaghari and Polak (2015) Mokhtarian (2018) Rashidi, Waller and Axhausen (2020) Singleton (2019) Steck et al. (2018) Zhong et al. (2020)
Findings	AVs will promote car-sharing; SAVs are expected to become one of the major travel modes; The use of SAVs will be affected by various urban environments and public perception	Ceteris paribus, denser driving by AVs may increase road capacity; However, the increase in road capacity can be offset by additional demand from new user groups and unoccupied cars	Fuel-efficient driving and fewer accidents reduce travel costs; Various activities within AVs increase positive travel utility; VTTS (the benefits of faster travel that saves time) will decrease

Note. AV = autonomous vehicle; SAV = shared autonomous vehicle; VTTS = value of travel time saving.

Impact on Car-Sharing Culture

Most of the studies that investigated changes in travel behavior caused by AVs predicted that there would be an increase in the use of SAVs. For example, Merfeld et al. (2019) argued that among the numerous changes caused by AVs, an increase in car-sharing would be one of the most prominent phenomena observed in the future. However, there has been no consensus regarding the scale of car-sharing; many studies have discussed car-sharing using a scenario-based approach (e.g., Milakis et al. 2017; Simoni et al. 2019; Stead and Vaddadi 2019). The amount of SAVs will depend on user preference and the public acceptance of AVs, which are closely related to the socio-economic characteristics of users, such as age, gender, income level, household composition, and travel purpose (Krueger, Rashidi and Rose 2016; Gkartzonikas and Gkritza 2019).

Car-sharing is expected to change the proportion of the main modes of travel (Mouratidis, Peters and van Wee 2021). Although previous studies reached varying conclusions regarding the scale of the changes, many have predicted that the proportion of cars (AVs and conventional cars) may decrease. Zhang and Guhathakurta (2021) indicated that the introduction of shared AVs might reduce the total number of private cars by about 9.5 percent. Moreover, Millard-Ball (2018) suggested that the incidence of walking may increase with SAVs. The extent of this effect will depend on the trip's lengthp and the purpose of travel. For example, walking is more likely to increase with shorter travel distances and more routine travel purposes (Ashkrof et al. 2019). In addition, some studies predicted that an increase in shared mobility may separate first-mile and last-mile travel using SAVs from long-distance travel using public transportation (Ohnemus and Perl 2016; Zhang and Guhathakurta 2021).

Impact on Road Capacity

With vehicular automation (especially full automation), it is expected that driving efficiency will be significantly improved by innovative technology such as Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication (Talebpour and Mahmassani 2016). An intelligent transportation system will enable individuals to drive in denser traffic (Hult et al. 2016; Talebpour and Mahmassani 2016), thus contributing to expanding road capacity even without any changes to the existing road networks (Olia et al. 2018). Furthermore, AVs have the potential to improve traffic behavior at link roads and intersections (Levin and Boyles 2016). Hence, the introduction of AVs to the road network can improve road capacity by enabling faster responses, more densely packed vehicles, and a more steady flow of traffic (Lu et al. 2020). Olia et al. (2018) found that a scenario with purely cooperative vehicles raised the highway capacity by 300% compared to conventional vehicles. This increased capacity could be more beneficial to motorways than urban streets (Milakis et al. 2017). Furthermore, AVs provide opportunities for more flexible uses of road infrastructure, such as the implementation of pedestrian-only zones or public transportation lanes at specific times of the day, so that roads can be used for different purposes at different times of the day (or week) (Stead and Vaddadi 2019). AVs are also expected to allow for the simplification of certain transportation infrastructures such as signaling systems (Lu et al. 2020).

However, the increase in road capacity may be less than expected if traditional vehicles continue to operate alongside AVs due to safety or regulatory concerns (Manivasakan et al. 2021). The enhanced road capacity might be also counteracted by additional travel demands caused by the introduction of AVs. Previous studies anticipated that AVs would increase the travel demands of individuals who are non-driving, elderly, or have travel-restrictive medical conditions (Harper et al. 2016). In addition, the introduction of SAVs is expected to result in greater numbers of unoccupied cars, which could increase vehicle miles traveled (VMT) by about 29.8 percent (Truong et al. 2017; Zhang, Guhathakurta and Khalil 2018). Furthermore, there is likely to be induced demand due to the relative expansion of road capacity. Milakis et al. (2017) took these various factors into account and predicted that the overall VMT would increase by up to 27 percent by 2050. Other studies (e.g., Bridgelall and Stubbing 2021) estimated that travel demand could double or even triple due to the introduction of AVs.

Impact on Travel Cost and Utility

In the long term, AVs are expected to reduce per-mile travel costs (Compostella et al. 2020), especially when automation technology is fully realized. A reduction in congestion and travel time, more precise acceleration and deceleration, and less frequent lane changes can result in lower fuel costs (Chen et al. 2019). The improved safety of AVs is also likely to reduce accidents and insurance costs (Fagnant and Kockelman 2015). Chen et al. (2019) predicted that fuel-efficient driving using AVs would lead to fuel savings of approximately 30 and 45 percent in pessimistic and optimistic scenarios, respectively. In addition, the initial investment required for vehicle purchases may be reduced as SAVs become more widely commercialized. The introduction of AVs may also cause people to become “passengerized,” reducing the burden of driving and allowing them to engage in other activities during vehicular travel (Mokhtarian 2018). Singleton (2019) predicted that the introduction of AVs may increase the positive utility of travel (see Mokhtarian and Salomon 2001) by enhancing physical and psychological comfort and increasing the opportunities for multitasking.

These changes can decrease the VOT, which refers to the opportunity cost that is obtained when travel time can be invested into other valuable activities (Harb et al. 2021). Steck et al. (2018) reported that autonomous driving in a privately owned vehicle may reduce the VTTS by 31 percent compared to manual driving; this is similar to the in-vehicle times of public transportation. Zhong et al. (2020) also predicted an 18–32 percent reduction in VOT in private AVs as well as an 8–14 percent reduction in VOT in shared AVs. In addition, Kolarova, Steck and Bahamonde-Birke (2019) found that the VTTS of SAVs is higher than that of PAVs. Although there were some differences between the studies, the introduction of AVs, whether privately owned or shared, is expected to reduce travel costs.

Summary of Impacts

In summary, the literature review showed that in the era of AVs, SAVs are likely to become a prominent mode of travel. The enhancement of road capacity due to AVs may increase traffic supply and potentially increase traffic demand. Travel costs are expected to decrease both economically and psychologically. However, the extent of these major changes may vary depending on uncertainties related to technology, policy and regulation, and preferred modes of transportation (Cottam 2018). For example, the use of SAVs may be affected by technological advancements in AVs as well as the public's perception of AVs and car-sharing (Yap, Correia and van Arem 2016). The level of changes in road capacity and travel costs can be influenced by the proportions of different modes of transportation (e.g., the relative ratio of AVs to conventional cars, the ratio of SAVs to PAVs) (Nazari, Noruzoliaee and Mohammadian 2018; González-González, Nogués and Stead 2019). Hence, predicting these changes with any degree of precision is challenging and they must be regularly adjusted as AVs are adopted.

The Effects of AVs on the BE

Effects on Density

AVs are expected to have a significant effect on density. AVs can result in different density patterns depending on the specifics of each scenario (Gelauff, Ossokina and Teulings 2019; Dianin, Ravazzoli and Hauger 2021). Milakis, Kroesen and van Wee (2018) established that AVs are likely to have two opposing implications for urban development: the densification of the city center and additional urban sprawl.

SAVs may contribute to increasing the density of city centers (Milakis, Kroesen and van Wee 2018) due to the possibility that it needs relatively small roads and parking spaces (Mounce and Nelson 2019). Fully automated SAVs would be able to drive themselves to parking spaces outside of the city, which would free up downtown land to be used for other purposes. The unused land could be redeveloped for alternative residential or commercial uses with higher density, especially if urban planning policies focus on building a compact urban form (Heinrichs 2016; Zakharenko 2016; Stead and Vaddadi 2019). The increasing utilization of city centers can contribute to the concentration of the active population (Papa and Ferreira 2018).

The extent to which SAVs can facilitate the expansion of peripheral areas is closely related to the business model of shared mobility (Duarte and Ratti 2018). The residential density of peripheral areas may increase if SAVs are available for those who live and/or work in the area. In contrast, if shared mobility services only cover the area around a city center for the sake of profitability (Zhang and Guhathakurta 2021), changes in density may be mainly observed within the city center. Overall, the extent to which SAVs affect the density of a city is related to many factors such as the relative ratio of SAVs to PAVs, the relative cost of using the different modes, as well as the business model of shared mobility (Nazari, Noruzoliaee and Mohammadian 2018).

The increase in road capacity due to the more efficient use of infrastructure by AVs may improve mobility between regions, contributing to increases in the density of peripheral areas (Meyer et al. 2017; Soteropoulos, Berger and Ciari 2019). Change in road capacity and the utility of travel time influnce users’ willingness to travel longer distances (Dianin, Ravazzoli and Hauger 2021). In scenarios with high automation levels, an increasing number of people may relocate to suburban areas due to increasing travel efficiencies. However, the residential density of peripheral areas may not increase substantially if the expanded road capacity is offset by additional demand for transportation (caused by AVs) (Cohn et al. 2019). The offset of road capacity by additional demand may vary between 0 and 180 percent depending on the region (Meyer et al. 2017). In this case, an individual's willingness to travel longer may not greatly increase. In practice, density may or may not increase depending on many factors including liability and attractiveness of newly created (or redeveloped area) (Stead and Vaddadi 2019).

The decrease in travel costs caused by AVs may increase the residential density in peripheral areas of the city. Meyer et al. (2017) identified that reductions in travel costs might expedite urban sprawl. As the cost of travel decreases, the disadvantages of distance also decrease, and residents may be encouraged to move to the outskirts of the city where better living conditions can be found. Gelauff, Ossokina and Teulings (2019) also suggested that the productive use of time during long-distance car travel might result in sub-urbanization. However, it should be noted that the extent to which VOT is reduced varies by region. The effect is smaller in areas that are further from the city center. For example, the VOT reduction when using PAVs for commuting in urban, suburban, and rural areas was 32, 24, and 18 percent, respectively, while the VOT reduction when using SAVs was 14, 13, and 8 percent, respectively (Zhong et al. 2020). It should be noted that the extent of urban sprawl will be subject to the availability of land as well as land use policies, such as any regulations that allow for or limit the extent of the urban expansion or the reallocation of road and parking spaces (Faisal et al. 2019).

Effects on Diversity

Many studies have predicted that vehicular automation could cause a decrease in diversity in a city because residential areas may be separated from the central business districts (Carrese et al. 2019; Gelauff, Ossokina and Teulings 2019; Zhang and Guhathakurta 2021). These effects may differ depending on regional variations in traffic supply and demand, and travel purposes as well as the specific focus of land use policies (Zhang et al. 2012).

SAVs may encourage individuals to relocate to peripheral areas (Duarte and Ratti 2018; Milakis, Kroesen and van Wee 2018), which could result in the separation of residential areas from workplaces, affecting land use diversity in a city. Simulation conducted by Zhang and Guhathakurta (2021) revealed that the introduction of SAVs might allow families to leave the areas near their workplaces and move to regions with more appealing housing and better schools. Where individuals choose to live may become more significantly influenced by the quality of residential environments rather than a desire to live closer to work (Heinrichs 2016). This potential migration is expected to be expressed more prominently among younger demographics and lower-income groups (Kim, Mokhtarian and Circella 2020; Zhang and Guhathakurta 2021).

SAVs may induce land use reorganization in a city center (e.g., re-densification due to a decrease in the demand for parking spaces) (Gkartzonikas and Gkritza 2019), contributing to increasing diversity of land use especially if land use policies promote mixed land use around key transportation hubs. Further reorganization of land use across the metropolitan area might take place due to the differential change in rent prices betweena city center and peripheral areas (Zakharenko 2016; Liu et al. 2021). The impact on diversity will be inextricably influenced by existing policy initiatives such as compact city development which focuses on high-density mixed-use pedestrian-friendly urban designs (Lee, Arts and Vanclay 2021).

Only a few studies have directly investigated the relationship between road capacity and diversity. It has been suggested that individuals may become more willing to travel longer distances due to the increased road capacity, which might affect land use diversity in a city. However, to fully understand the impact of road capacity on diversity, it is necessary to examine the extent to which traffic supply increases due to higher road capacity, as well as the extent to which traffic demand increases by region (e.g., Meyer et al. 2017), which is in turn based on travel purposes (e.g., Bridgelall and Stubbing 2021). Freemark, Hudson and Zhao (2019) addressed the need to carefully consider exogenous factors affecting land use patterns – e.g., lifestyle changes such as telecommuting, online shopping, and online education affecting people's travel behavior (Mouratidis, Peters and van Wee 2021).

The reduction in travel costs caused by AVs may also affect diversity because reductions in VOT influence how households make decisions about the location of their residences (Krueger, Rashidi and Dixit 2019), which can result in changing land use patterns in cities and regions. Gelauff, Ossokina and Teulings (2019) showed that the more productive use of time during car trips might separate residential areas from the city center. Carrese et al. (2019) also demonstrated that AVs might cause residents to relocate from city centers to the suburbs due to changes in how people perceive travel time and its value. However, this result was mainly relevant for commuting trips; no significant VTTS reduction effects were shown for leisure or shopping trips (Carrese et al. 2019). Kolarova, Steck and Bahamonde-Birke (2019) also reported that, compared to conventional cars, AVs reduce the VTTS of commutes by 41 percent. These studies suggest that potential land use reallocation due to reductions in travel costs may vary depending on whether the land is currently used for business, residential, commercial, or leisure purposes.

Effects on Design

AVs will dramatically improve the driving efficiency as well as the efficiency of the entire transportation network (Alessandrini et al. 2015), possibly affecting long-term road design (Milakis et al. 2017). The road design changes in the era of AVs will be closely related to the level of change in VMT, travel mode share, design preferences, the direction of planning policies, and existing land use (Manivasakan et al. 2021).

The increase in car-sharing due to AVs will be likely to influence the road network. Yap, Correia and van Arem (2016) showed that individuals might prefer SAVs to bicycles or buses for their last-mile trips, highlighting the possible need to redesign road networks in urban and low-density areas (Ohnemus and Perl 2016). With the increasing car-sharing culture, the number of lanes and branches of road networks may increase, especially in areas where individuals require door-to-door service for their last-mile trips. In this situation, road density in urban areas could increase and pedestrian accessibility can be negatively affected. With the increasing dependance on SAVs, traditional transit-oriented development (TOD) strategies in urban areas may need to be modified (Lu et al. 2017; Faisal et al. 2019). If SAVs gradually contribute to reducing the demand for parking spaces, TOD may focus on utilizing disused sites for establishing new conceptual urban road systems where pedestrians and vehicles can effectively co-exist (Millard-Ball 2018; Botello et al. 2019).

In the future, increasing road capacity may require changes in the design of road infrastructure (Duarte and Ratti 2018). Since AVs have excellent lane-keeping performance, lane widths can be reduced and more lanes can be built (Talebpour, Mahmassani and Elfar 2017). With the assumption that travel demands is fixed, enhancements in road capacity could consequently decrease the proportion of road surfaces in cities in the long term (Faisal et al. 2019; González-González, Nogués and Stead 2019).

Only few studies have investigated potential changes in road networks due to the effects ofAVs on road capacity. Most studies have concentrated on changes in driving parameters within the fixed road network (e.g., Le Vine, Zolfaghari and Polak 2015; Levin and Boyles 2016; Olia et al. 2018; Lu et al. 2020). Studies on road capacity and road design have mainly focused on lanes rather than other elements of road design, such as the number of branches or the shape of the roads. Overall, studies on the impact on road networks are limited, and further research in this area is essential.

Previous research on AVs has rarely discussed the relationship between travel costs and road design, even though changes in travel costs may influence road design. In the short term, construction costs might increase with the infrastructure necessary for the use of AVs. In the long term, maintenance and travel costs are expected to decrease (Compostella et al. 2020). However, if the replacement of conventional cars with AVs is delayed, additional maintenance costs may be required because each transportation mode needs its own infrastructure (Manivasakan et al. 2021).

Effects on Destination Accessibility

The effects of AVs on destination accessibility may vary depending on regional characteristics (e.g., urban, suburban, or rural), travel mode share (Meyer et al. 2017), and social groups (Milakis, Kroesen and van Wee 2018). Although it is difficult to precisely predict the scale of this effect due to uncertainties regarding the aforementioned characteristics, many studies expected an overall improvement in accessibility (e.g., Meyer et al. 2017; Milakis, Kroesen and van Wee 2018; Cordera et al. 2021).

Several studies have highlighted that car-sharing will positively affect destination accessibility (Cohn et al. 2019; Dianin, Ravazzoli and Hauger 2021). However, many factors including a nature and scale of SAV services could affect where and who will benefit from the enhanced accessibility (Papa and Ferreira 2018). For example, if SAVs are operated by the private sector, which primarily focuses on the profitability of its services, the city centers will experience increased accessibility compared to the peripheral areas (Mounce and Nelson 2019). This situation could increase the imbalance in destination accessibility between a city center and its peripheral regions (Milakis, Kroesen and van Wee 2018; Papa and Ferreira 2018). Conversely, if SAVs are operated publicly with a focus on equal access, then destination accessibility will improve across most regions. Furthermore, some studies have noted that shared mobility may contribute to inequalities in accessibility between regions or social groups (Cohn et al. 2019) by increasing the separation between job-related and residential areas (Zhang and Guhathakurta 2021), and increasing the regional gap in rent prices in the long term (Zakharenko 2016).. Moreover, it should be noted that if the advantages of SAVs are less pronounced than expected, public transit systems may still compete with SAVs and shared AVs will not make a substantial contribution to increasing accessibility (Fraedrich et al. 2019).

With an assumption that travel demand is fixed, destination accessibility may improve as road capacity increases. However, many empirical studies have predicted that the enhancement of destination accessibility may be limited in the long term due to the possible increase in VMT in the era of AVs (e.g., Childress et al. 2015; Stead and Vaddadi 2019; Wang and Zhang 2021). In practice, the precise manner in which AVs will affect destination accessibility remains unclear. For example, studies that utilized an activity-based model suggest that VMT changes caused by SAVs may range from 20 (Heilig et al. 2017) to 83 percent (Harb et al. 2018) depending on characteristics such as the specifics of the study area, the ratio of SAVs to PAVs, travel cost changes, and modal shifts.

The changes in VOT in the AV-dominated era will affect the concept of time-distance (Correia et al. 2019).Reduction in both economic and psychological travel costs may improve destination accessibility (Mokhtarian 2018). In this context, It is necessary to review and consider an alternative way to measure destination accessibility in the future.

Discussion and Conclusions

This study has identified the changes in transportation induced by AVs, the effects of these changes on the BE, and the important gaps in AV–BE research (Table 3). AV–BE relationships were not always clear, and uncertainties in technology, policy and regulation, and transportation modes need to be considered when predicting spatial changes in an AV-dominated era. Factors such as existing land use and the rigidity of the policy-making process may act as barriers to change, making forecasting even more difficult. Nevertheless, AVs are expected to precipitate the reorganization of the BE by significantly affecting travel choices, traffic volume and flow, and travel costs. In general, as a result of technological advancements, more people could use transportation in more efficient and effective ways. Our study indicates that with increasing movement of individuals to peripheral areas, commercial and residential areas may be separated. At the same time, high-density development may become possible in urban areas through the reconstruction of city centers. Road networks and designs may also be reconfigured due to the changes in traffic supply and demand caused by the introduction of AVs, especially SAVs. Destination accessibility will improve, but its benefits are expected to differ depending on certain variables, such as regions and groups. The extent of all these effects may vary depending on traffic volumes and the scale of additional traffic demands.

Table 3.

Summary of the Impacts of AVs on the BE.

	Impact on travel choices: increase in car-sharing culture	Impact on traffic volumes and flows: increase in road capacity	Impact on travel costs: reduction in costs	Summary
Density	Varies with the scale of SAV adoption. The density of city centers is expected to increase. However, the precise scale of these impacts cannot be clearly defined.	The residential density of peripheral areas may increase, which may result in urban sprawl.	Significant, especially in suburban areas.	AVs catalyze changes in density across the region. Policies on urban sprawl will influence these changes.
Diversity	Separation of commercial and residential areas. In a city center, diversity of land use may increase, depending on the scope and the scale of t the re-densification of the center.	Effects are unclear.	Separation of commercial and residential areas. Separation is influenced by existing land use purposes.	AVs lead to the separation of workplaces and residences. Existing land use could curb these changes.
Design	SAV-oriented road redesign will occur.	Reduction in road surfaces. Variable road designs may be introduced.	Effects are unclear.	Road designs will be simplified. Impacts will vary according to regional characteristics.
Destination accessibility	Accessibility will generally increase. The level of change may vary across a city and social groups, depending on the scale and the nature of SAV services.	Effects depend on the scale of the additional demand and the enhanced road capacity.	An improvement is expected.	An overall improvement in accessibility is expected. Impact mayvary by area and group. Destination accessibility needs to be redefined.

Note. AV = autonomous vehicle; SAV = shared autonomous vehicle; PAV = private autonomous vehicle.

Our study reveals that several key factors greatly affect AVs' impact on the BE. First, additional traffic demand induced by AVs may affect the degree to which road capacity increases as well as the extent of changes in travel costs; consequently, the impact of AVs on the D-variables is volatile. For example, if road capacity ultimately decreases due to the additional demand, the movement of individuals to peripheral areas may not increase as much as expected and the impact of AVs on the D-variables may not change significantly in the long term. Second, the effect of AVs may vary depending on how SAV services are operated. If the privite sector operates SAVs, they may predominantly serve areas that can increase profitability (e.g., highly populated areas such as city centers). However, if SAVs aim to supply services to the wider population, peripheral areas may also be covered (Papa and Ferreira 2018; Dianin, Ravazzoli and Hauger 2021). Third, our review showed that AVs might reduce or increase inequalities in accessibility due to potential changes in spatial structure (Cohn et al. 2019). Changes in the spatial structure of the city, such as the creation of new peripheral centers due to the relocation of residential areas to city outskirts or the extension of regional transportation networks can contribute to differential accessibility across several regions (Milakis, Kroesen and van Wee 2018). It is important to note that these impacts are dependent on many factors such as the level of AV penetration, the ratio of public to private SAVs, the degree to which SAVs replace public transit, and the reorganization of road networks.

As demonstrated in this review, the impacts of AVs on the BE is still highly uncertain, and we propose potential directions that further studies can take to examine this relationship in more detail (Table 4). First, it is necessary to consider how different socio-demographic characteristics affect the use of SAVs (Nazari, Noruzoliaee and Mohammadian 2018), changes in VOT (Liu et al. 2021), and additional travel demands (Milakis 2019). For example, there is a need for research into how different demographics (e.g., young, middle-aged, or the elderly) have different preferences regarding the adoption of SAVs (Acheampong et al. 2021; Zhang and Guhathakurta 2021), as well as how such differences are related to the changes in diversity of land use in cities.

Table 4.

Potential Directions for Future Research.

	Impact on car-sharing culture	Impact on road capacity	Impact on travel cost and utility
Overall BE	The impact of technological advancement and public perception on the choice of AVs and SAVs as a mode of transport.	The effect of simultaneous use of conventional cars and AVs on road capacity after the commercialization of AVs. Impact of the uncertainties in policy and regulation on road capacity.	Impact of the uncertainties in policy and regulation on travel cost.
Density	Variations in usage patterns of SAVs across regions and demographic groups. Effect of SAVs on urban sprawl.	Regional distribution of additional demand.	Contextual changes in VOT by region.
Diversity	Changes in land use according to SAV business models (i.e., publicly vs. privately operated).	Changes in traffic supply and demand by region and the consequent changes in land use.	Differentiation of land use by region according to changes in travel cost.
Design	Plans to promote active travel in the era of SAVs.	Prediction and design guidelines for changes in road design caused by AVs. Influence of existing land use and design preference on future designs.	Short- and long-term impacts on the road network due to changes in travel costs in the AVs era.
Destination accessibility	The variation in the accessibility gap between the city center and its peripheries according to SAV business models. Changes in destination accessibility from a spatial or social equity perspective.	Clarification on the size of additional demand by region and demographic groups.	The effect of the change in the value of time on accessibility caused by improved opportunities for multitasking, etc.

Note. AV = autonomous vehicle; SAV = shared autonomous vehicle; VOT = value of time; BE = built environment.

Second, there needs to be additional research into areas where the initial assumptions strongly influence the results of the investigation (see Soteropoulos, Berger and Ciari 2019; Harb et al. 2021; Wang and Zhang 2021), or where uncertainties are oversimplified (Cottam 2018). In particular, it is necessary to examine the uncertainties related to the level of technological advancement and public perception and attitudes toward AVs. Subjective attitudes toward the safety of AVs can fluctuate over time depending on certain conditions, especially since AVs have not yet become a widespread mode of transportation (Acheampong et al. 2021). Due to the safety concerns, the public is still likely to hesitate accepting AVs. In such cases, the simultaneous use of conventional cars and AVs may continue, and thus changes to the BE may be slow.

Third, further research should carefully consider policies and regulations that strongly influence the AV–BE relationship. The various uncertainties assumed in this review are highly dependent on active and conscious political choices that are made in society. Many of these factors are knowable but not yet decided. Further research needs to consider land use and infrastructure policies, taxation, subsidizing, and amending standards related to AVs. It may help to avoid adverse outcomes by mapping the uncertainties in different policies and regulations scenarios. Moreover, it should be noted that there is limited research on how the policy decision process could influence BE in the era of AVs (Kim, Mokhtarian and Circella 2020). Existing land use and the people that use them, stakeholders in existing transportation modes and infrastructure, the financial resources of central and local governments, long-term plans, and political irrationality can all contribute to uncertainties in policy-making (Papa and Ferreira 2018; Manivasakan et al. 2021). Critical research is required, especially regarding the complex network of politically influential actors and groups behind the introduction of AV technologies to urban transportation (Cugurullo et al. 2021).

Fourth, further studies should focus on areas that are still poorly understood, such as the effect of changes in the value of time in an AV-dominated environment on transportation accessibility, the impacts of SAVs on active travel in the city center and TOD schemes, and the short- and long-term road design changes caused by travel cost changes in an AV-dominated era. It is also necessary to advance spatial and social equity research by considering long-term land use and transportation interactions. There should be needs to explore how AVs may change the spatial structure of urban areas as well as whether the socio-spatial effects of AVs will contribute to a fairer distribution of equity among different social groups.

Finally, we suggest that further studies focus on other D-variables, including distance to transit, demand management, and demographics. Such studies are important as AVs are likely to generate changes in the scope and pattern of public transit, the conventional methods of managing traffic demand, and the demographics of transportation. With regard to distance to transit, further research should consider the extent to which SAVs may enable door-to-door services (Yap, Correia and van Arem 2016) as well as separate travel into short- and long-distance modes (Yap, Correia and van Arem 2016) by changing first-mile and last-mile travel behavior (Mounce and Nelson 2019). In demand management, it would be valuable to explore new strategies to control demand, such as detailed regulations based on the time of the day, the day of the week, and the region (Metz 2018). Congestion pricing and road tolls could be also used to reduce VMT (Simoni et al. 2019). In terms of demographic variables, future research needs to focus on the effects of shared mobility on job accessibility as well as the effects of reductions in travel costs across regions and social groups, as SAVs may provide equity benefits across demographic groups (Meyer et al. 2017; Cohn et al. 2019).

Studies on how the BE impacts the usage of AVs are relatively rare, and the current studies assume a static equilibrium that focuses on short-term effects (Hawkins and Habib 2019). We suggest that it is necessary to consider the impact of the BE to determine critical implications for future urban planning and policy-making. Once the circular causation between AVs and the BE has been comprehensively investigated, research on autonomous driving technology can benefit the urban population and create desirable social outcomes.

Footnotes

Acknowledgements

The authors are grateful for the anonymous reviewers who helped improve this paper by directly providing original ideas or indicating potential issues with the previous versions.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (grant number: NRF-2021S1A3A2A01087370).

ORCID iD

Jindo Jeong

Author Biographies

Jindo Jeong, Ph.D. Candidate, Graduate School of Environmental Studies, Seoul National University, South Korea.

Juhyun Lee, Assistant Professor, Department of Urban Planning and Design, Xi’an Jiaotong-Liverpool University, P.R.China.

Tae-Hyoung Tommy Gim, Associate Professor and Chair, Department of Environmental Planning, Graduate School of Environmental Studies, Interdisciplinary Program in Landscape Architecture, and Environmental Planning Institute, Seoul National University, South Korea.

References

Acheampong

Ransford A

Cugurullo

Federico

Gueriau

Maxime

Dusparic

Ivana

. 2021. “Can Autonomous Vehicles Enable Sustainable Mobility in Future Cities? Insights and Policy Challenges from User Preferences Over Different Urban Transport Options.” Cities (London, England) 112: 103134.

Acheampong

Ransford A.

Silva

Elisabete A.

. 2015. “Land use–Transport Interaction Modeling: A Review of the Literature and Future Research Directions.” Journal of Transport and Land Use 8 (3): 11–38.

Alessandrini

Adriano

Campagna

Andrea

Site

Paolo Delle

Filippi

Francesco

Persia

Luca

. 2015. “Automated Vehicles and the Rethinking of Mobility and Cities.” Transportation Research Procedia 5: 145–60.

Anderson

James M

Nidhi

Kalra

Stanley

Karlyn D

Sorensen

Paul

Samaras

Constantine

Oluwatola

Oluwatobi A

. 2014. Autonomous Vehicle Technology: A Guide for Policymakers: Rand Corporation.

Ashkrof

Peyman

de Almeida Correia

Gonçalo Homem

Cats

Oded

van Arem

Bart

. 2019. “Impact of Automated Vehicles on Travel Mode Preference for Different Trip Purposes and Distances.” Transportation Research Record 2673 (5): 607–16.

Bösch

Patrick M.

Becker

Felix

Becker

Henrik

Axhausen

Kay W.

. 2018. “Cost-based Analysis of Autonomous Mobility Services.” Transport Policy 64: 76–91.

Botello

Bryan

Buehler

Ralph

Hankey

Steve

Mondschein

Andrew

Jiang

Zhiqiu

. 2019. “Planning for Walking and Cycling in an Autonomous-Vehicle Future.” Transportation Research Interdisciplinary Perspectives 1: 100012.

Bridgelall

Raj

Stubbing

Edward

. 2021. “Forecasting the Effects of Autonomous Vehicles on Land use.” Technological Forecasting and Social Change 163: 120444.

Cao

Xinyu

Mokhtarian

Patricia L

Handy

Susan L

. 2009. “Examining the Impacts of Residential Self–Selection on Travel Behaviour: A Focus on Empirical Findings.” Transport Reviews 29: 359–95.

10.

Carrese

Stefano

Nigro

Marialisa

Patella

Sergio Maria

Toniolo

Eleonora

. 2019. “A Preliminary Study of the Potential Impact of Autonomous Vehicles on Residential Location in Rome.” Research in Transportation Economics 75: 55–61.

11.

Cervero

Robert

Kockelman

Kara

. 1997. “Travel Demand and the 3Ds: Density, Diversity, and Design.” Transportation Research Part D 2 (3): 199–219.

12.

Chen

Yuche

Gonder

Jeffrey

Young

Stanley

Wood

Eric

. 2019. “Quantifying Autonomous Vehicles National Fuel Consumption Impacts: A Data-Rich Approach.” Transportation Research Part A 122: 134–45.

13.

Childress

Suzanne

Nichols

Brice

Charlton

Billy

Coe

Stefan

. 2015. “Using an Activity-Based Model to Explore the Potential Impacts of Automated Vehicles.” Transportation Research Record: Journal of the Transportation Research Board 2493 (1): 99–106.

14.

Cohn

Jesse

Ezike

Richard

Martin

Jeremy

Donkor

Kwasi

Ridgway

Matthew

Balding

Melissa

. 2019. “Examining the Equity Impacts of Autonomous Vehicles: A Travel Demand Model Approach.” Transportation Research Record: Journal of the Transportation Research Board 2673 (5): 23–35.

15.

Compostella

Junia

Fulton

Lewis M.

Kleine

Robert De

Kim

Hyung Chul

Wallington

Timothy J.

. 2020. “Near- (2020) and Long Term (2030–2035) Costs of Automated, Electrified, and Shared Mobility in the United States.” Transport Policy 85: 54–66.

16.

Cordera

Rubén

Nogués

Soledad

González-González

Esther

Moura

José L.

. 2021. “Modeling the Impacts of Autonomous Vehicles on Land Use Using a LUTI Model.” Sustainability 13 (4): 1608.

17.

Correia

Gonçalo Homem de Almeida

Looff

Erwin

van Cranenburgh

Sander

Snelder

Maaike

van Arem

Bart

. 2019. “On the Impact of Vehicle Automation on the Value of Travel Time While Performing Work and Leisure Activities in a car: Theoretical Insights and Results from a Stated Preference Survey.” Transportation Research Part A 119: 359–82.

18.

Correia

Gonçalo Homem de Almeida

van Arem

Bart

. 2016. “Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A Model to Explore the Impacts of Self-Driving Vehicles on Urban Mobility.” Transportation Research Part B 87: 64–88.

19.

Cottam

Bobby J

. 2018. “Transportation Planning for Connected Autonomous Vehicles: How It All Fits Together.” Transportation Research Record 2672 (51): 12–9.

20.

Cugurullo

Federico

Acheampong

Ransford A

Gueriau

Maxime

Dusparic

Ivana

. 2021. “The Transition to Autonomous Cars, the Redesign of Cities and the Future of Urban Sustainability.” Urban Geography 42: 833–59.

21.

Curtis

Carey

McLeod

Sam

Hultén

John

Pettersson-Lofstedt

Fredrik

Paulsson

Alexander

Sørensen

Claus Hedegaard

. 2021. “Knowledge for Policy-Making in Times of Uncertainty: The Case of Autonomous Vehicle Model Results.” Transport Reviews 41: 478–98.

22.

Dianin

Alberto

Ravazzoli

Elisa

Hauger

Georg

. 2021. “Implications of Autonomous Vehicles for Accessibility and Transport Equity: A Framework Based on Literature.” Sustainability 13 (8): 4448.

23.

Duarte

Fábio

Ratti

Carlo

. 2018. “The Impact of Autonomous Vehicles on Cities: A Review.” Journal of Urban Technology 25 (4): 3–18.

24.

Ewing

Reid

Cervero

Robert

. 2010. “Travel and the Built Environment.” Journal of the American Planning Association 76 (3): 265–94.

25.

Fagnant

Daniel J.

Kockelman

Kara M.

. 2014. “The Travel and Environmental Implications of Shared Autonomous Vehicles, Using Agent-Based Model Scenarios.” Transportation Research Part C 40: 1–13.

26.

Fagnant

Daniel J.

Kockelman

Kara

. 2015. “Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations.” Transportation Research Part A 77: 167–81.

27.

Faisal

Asif

Kamruzzaman

Yigitcanlar

Tan

Currie

Graham

. 2019. “Understanding Autonomous Vehicles: A Systematic Literature Review on Capability, Impact, Planning and Policy.” Journal of Transport and Land Use 12 (1): 45–72.

28.

Fraedrich

Eva

Heinrichs

Dirk

Bahamonde-Birke

Francisco J.

Cyganski

Rita

. 2019. “Autonomous Driving, the Built Environment and Policy Implications.” Transportation Research Part A 122: 162–72.

29.

Freemark

Yonah

Hudson

Anne

Zhao

Jinhua

. 2019. “Are Cities Prepared for Autonomous Vehicles?” Journal of the American Planning Association 85 (2): 133–51.

30.

Gelauff

George

Ossokina

Ioulia

Teulings

Coen

. 2019. “Spatial and Welfare Effects of Automated Driving: Will Cities Grow, Decline or Both?” Transportation Research Part A 121: 277–94.

31.

Gkartzonikas

Christos

Gkritza

Konstantina

. 2019. “What Have we Learned? A Review of Stated Preference and Choice Studies on Autonomous Vehicles.” Transportation Research Part C 98: 323–37.

32.

González-González

Esther

Nogués

Soledad

Stead

Dominic

. 2019. “Automated Vehicles and the City of Tomorrow: A Backcasting Approach.” Cities (London, England) 94: 153–60.

33.

Harb

Mustapha

Stathopoulos

Amanda

Shiftan

Yoram

Walker

Joan L.

. 2021. “What do we (Not) Know About our Future with Automated Vehicles?” Transportation Research Part C 123: 102948.

34.

Harb

Mustapha

Xiao

Circella

Giovanni

Mokhtarian

Patricia L.

Walker

Joan L.

. 2018. “Projecting Travelers into a World of Self-Driving Vehicles: Estimating Travel Behavior Implications via a Naturalistic Experiment.” Transportation 45 (6): 1671–85.

35.

Harper

Corey D.

Hendrickson

Chris T.

Mangones

Sonia

Samaras

Constantine

. 2016. “Estimating Potential Increases in Travel with Autonomous Vehicles for the non-Driving, Elderly and People with Travel-Restrictive Medical Conditions.” Transportation Research Part C 72: 1–9.

36.

Hawkins

Jason

Habib

Khandker Nurul

. 2019. “Integrated Models of Land use and Transportation for the Autonomous Vehicle Revolution.” Transport Reviews 39 (1): 66–83.

37.

Heilig

Michael

Hilgert

Tim

Mallig

Nicolai

Kagerbauer

Martin

Vortisch

Peter

. 2017. “Potentials of Autonomous Vehicles in a Changing Private Transportation System – a Case Study in the Stuttgart Region.” Transportation Research Procedia 26: 13–21.

38.

Heinen

Eva

Mackett

Roger

van Wee

Bert

Ogilvie

David

Panter

Jenna

. 2018. “Residential Self-Selection in Quasi-Experimental and Natural Experimental Studies.” Journal of Transport and Land Use 11: 939–59.

39.

Heinrichs

Dirk

. 2016. “Autonomous Driving and Urban Land use.” In Autonomous Driving, 213–31. Berlin, Heidelberg, Germany: Springer.

40.

Hult

Campos

G. R.

Steinmetz

Hammarstrand

Falcone

Wymeersch

. 2016. “Coordination of Cooperative Autonomous Vehicles: Toward Safer and More Efficient Road Transportation.” IEEE Signal Processing Magazine 33 (6): 74–84.

41.

Kii

Masanobu

Nakanishi

Hitomi

Nakamura

Kazuki

Doi

Kenji

. 2016. “Transportation and Spatial Development: An Overview and a Future Direction.” Transport Policy 49: 148–58.

42.

Kim

Sung Hoo

Mokhtarian

Patricia L.

Circella

Giovanni

. 2020. “Will Autonomous Vehicles Change Residential Location and Vehicle Ownership? Glimpses from Georgia.” Transportation Research Part D 82: 102291.

43.

Kolarova

Viktoriya

Steck

Felix

Bahamonde-Birke

Francisco J.

. 2019. “Assessing the Effect of Autonomous Driving on Value of Travel Time Savings: A Comparison Between Current and Future Preferences.” Transportation Research Part A 129: 155–69.

44.

Krueger

Rico

Rashidi

Taha H.

Dixit

Vinayak V.

. 2019. “Autonomous Driving and Residential Location Preferences: Evidence from a Stated Choice Survey.” Transportation Research Part C 108: 255–68.

45.

Krueger

Rico

Rashidi

Taha H.

Rose

John M.

. 2016. “Preferences for Shared Autonomous Vehicles.” Transportation Research Part C 69: 343–55.

46.

Lee

Juhyun

Arts

Jos

Vanclay

Frank

. 2021. “Stakeholder Views About Land Use and Transport Integration in a Rapidly-Growing Megacity: Social Outcomes and Integrated Planning Issues in Seoul.” Sustainable Cities and Society 67: 102759.

47.

Levin

Michael W.

Boyles

Stephen D.

. 2016. “A Multiclass Cell Transmission Model for Shared Human and Autonomous Vehicle Roads.” Transportation Research Part C 62: 103–16.

48.

Levin

Michael W.

Kockelman

Kara M.

Boyles

Stephen D.

Tianxin

. 2017. “A General Framework for Modeling Shared Autonomous Vehicles with Dynamic Network-Loading and Dynamic Ride-Sharing Application.” Computers, Environment and Urban Systems 64: 373–83.

49.

Le Vine

Scott

Zolfaghari

Alireza

Polak

John

. 2015. “Autonomous Cars: The Tension Between Occupant Experience and Intersection Capacity.” Transportation Research Part C 52: 1–14.

50.

Lin

Tao

Wang

Donggen

Guan

Xiaodong

. 2017. “The Built Environment, Travel Attitude, and Travel Behavior: Residential Self-Selection or Residential Determination?” Journal of Transport Geography 65: 111–22.

51.

Liu

Peng

Shu-Xian

Ong

Ghim Ping

Tian

Qiong

Shoufeng

. 2021. “Effect of Autonomous Vehicles on Travel and Urban Characteristics.” Transportation Research Part B: METhodological 153: 128–48.

52.

Zhongming

Rundong

Dunham-Jones

Ellen

Park

Haesun

Crittenden

John

. 2017. “Data-enabled Public Preferences Inform Integration of Autonomous Vehicles with Transit-Oriented Development in Atlanta.” Cities (London, England) 63: 118–27.

53.

Qiong

Tettamanti

Tamás

Hörcher

Dániel

Varga

István

. 2020. “The Impact of Autonomous Vehicles on Urban Traffic Network Capacity: An Experimental Analysis by Microscopic Traffic Simulation.” Transportation Letters 12 (8): 540–9.

54.

Jiaqi

Xiaopeng

Zhou

Fang

Hao

Wei

. 2017. “Designing Optimal Autonomous Vehicle Sharing and Reservation Systems: A Linear Programming Approach.” Transportation Research Part C: Emerging Technologies 84: 124–41.

55.

Manivasakan

Hesavar

Kalra

Riddhi

O'Hern

Steve

Fang

Yihai

Yinfei

Zheng

Nan

. 2021. “Infrastructure Requirement for Autonomous Vehicle Integration for Future Urban and Suburban Roads – Current Practice and a Case Study of Melbourne, Australia.” Transportation Research Part A 152: 36–53.

56.

Martinez

Luis M.

Viegas

José Manuel

. 2017. “Assessing the Impacts of Deploying a Shared Self-Driving Urban Mobility System: An Agent-Based Model Applied to the City of Lisbon, Portugal.” International Journal of Transportation Science and Technology 6 (1): 13–27.

57.

Merfeld

Katrin

Wilhelms

Mark-Philipp

Henkel

Sven

Kreutzer

Karin

. 2019. “Carsharing with Shared Autonomous Vehicles: Uncovering Drivers, Barriers and Future Developments – A Four-Stage Delphi Study.” Technological Forecasting and Social Change 144: 66–81.

58.

Metz

David

. 2018. “Developing Policy for Urban Autonomous Vehicles: Impact on Congestion.” Urban Science 2 (2): 33.

59.

Meyer

Jonas

Becker

Henrik

Bösch

Patrick M.

Axhausen

Kay W.

. 2017. “Autonomous Vehicles: The Next Jump in Accessibilities?” Research in Transportation Economics 62: 80–91.

60.

Milakis

Dimitris

. 2019. “Long Term Implications of Automated Vehicles: An introduction.” Transport Reviews 39 (1): 1–8.

61.

Milakis

Dimitris

Kroesen

Maarten

van Wee

Bert

. 2018. “Implications of Automated Vehicles for Accessibility and Location Choices: Evidence from an Expert-Based Experiment.” Journal of Transport Geography 68: 142–8.

62.

Milakis

Dimitris

Snelder

Maaike

van Arem

Bart

van Wee

Bert

de Almeida Correia

Gonçalo Homem

. 2017. “Development and Transport Implications of Automated Vehicles in the Netherlands: Scenarios for 2030 and 2050.” European Journal of Transport and Infrastructure Research 17 (1).

63.

Millard-Ball

Adam

. 2018. “Pedestrians, Autonomous Vehicles, and Cities.” Journal of Planning Education and Research 38 (1): 6–12.

64.

Mokhtarian

Patricia L

. 2018. “The Times They Are A-Changin”: What Do the Expanding Uses of Travel Time Portend for Policy, Planning, and Life?” Transportation Research Record 2672 (47): 1–11.

65.

Mokhtarian

Patricia L

Salomon

Ilan

. 2001. “How Derived is the Demand for Travel? Some Conceptual and Measurement Considerations.” Transportation Research Part A 35: 695–719.

66.

Mounce

Richard

Nelson

John D.

. 2019. “On the Potential for one-way Electric Vehicle car-Sharing in Future Mobility Systems.” Transportation Research Part A 120: 17–30.

67.

Mouratidis

Kostas

Peters

Sebastian

van Wee

Bert

. 2021. “Transportation Technologies, Sharing Economy, and Teleactivities: Implications for Built Environment and Travel.” Transportation Research Part D: Transport and Environment 92: 102716.

68.

Nazari

Fatemeh

Noruzoliaee

Mohamadhossein

Mohammadian

Abolfazl

. 2018. “Shared Versus Private Mobility: Modeling Public Interest in Autonomous Vehicles Accounting for Latent Attitudes.” Transportation Research Part C: Emerging Technologies 97: 456–77.

69.

NHTSA. 2013. Preliminary statement of policy concerning automated vehicles. Washington, DC, 1, 14.

70.

Ohnemus

Michael

Perl

Anthony

. 2016. “Shared Autonomous Vehicles: Catalyst of New Mobility for the Last Mile?” Built Environment 42 (4): 589–602.

71.

Olia

Arash

Razavi

Saiedeh

Abdulhai

Baher

Abdelgawad

Hossam

. 2018. “Traffic Capacity Implications of Automated Vehicles Mixed with Regular Vehicles.” Journal of Intelligent Transportation Systems 22 (3): 244–62.

72.

Papa

Enrica

Ferreira

António

. 2018. “Sustainable Accessibility and the Implementation of Automated Vehicles: Identifying Critical Decisions.” Urban Science 2 (1): 5.

73.

Rashidi

Taha Hossein

Waller

Travis

Axhausen

Kay

. 2020. “Reduced Value of Time for Autonomous Vehicle Users: Myth or Reality?” Transport Policy 95: 30–6.

74.

Simoni

Michele D.

Kockelman

Kara M.

Gurumurthy

Krishna M.

Bischoff

Joschka

. 2019. “Congestion Pricing in a World of Self-Driving Vehicles: An Analysis of Different Strategies in Alternative Future Scenarios.” Transportation Research Part C: Emerging Technologies 98: 167–85.

75.

Singh

Harprinderjot

Ghamami

Mehrnaz

Nouri

Hadis

Gates

Timothy

. 2022. “Multi-objective Framework for optimum Configuration of Human-Driven and Shared or Privately Owned Autonomous Vehicles.” International Journal of Sustainable Transportation 16 (5): 441–61.

76.

Singleton

Patrick A

. 2019. “Discussing the “Positive Utilities” of Autonomous Vehicles: Will Travellers Really use Their Time Productively?” Transport Reviews 39 (1): 50–65.

77.

Soteropoulos

Aggelos

Berger

Martin

Ciari

Francesco

. 2019. “Impacts of Automated Vehicles on Travel Behaviour and Land use: An International Review of Modelling Studies.” Transport Reviews 39 (1): 29–49.

78.

Stead

Dominic

Vaddadi

Bhavana

. 2019. “Automated Vehicles and how They may Affect Urban Form: A Review of Recent Scenario Studies.” Cities (London, England) 92: 125–33.

79.

Steck

Felix

Kolarova

Viktoriya

Bahamonde-Birke

Francisco

Trommer

Stefan

Lenz

Barbara

. 2018. “How Autonomous Driving May Affect the Value of Travel Time Savings for Commuting.” Transportation Research Record: Journal of the Transportation Research Board 2672 (46): 11–20.

80.

Stevens

Mark R

. 2017. “Does Compact Development Make People Drive Less?” Journal of the American Planning Association 83 (1): 7–18.

81.

Talebpour

Alireza

Mahmassani

Hani S.

. 2016. “Influence of Connected and Autonomous Vehicles on Traffic Flow Stability and Throughput.” Transportation Research Part C: Emerging Technologies 71: 143–63.

82.

Talebpour

Alireza

Mahmassani

Hani S.

Elfar

Amr

. 2017. “Investigating the Effects of Reserved Lanes for Autonomous Vehicles on Congestion and Travel Time Reliability.” Transportation Research Record: Journal of the Transportation Research Board 2622 (1): 1–12.

83.

Truong

Long T.

De Gruyter

Chris

Currie

Graham

Delbosc

Alexa

. 2017. “Estimating the Trip Generation Impacts of Autonomous Vehicles on car Travel in Victoria, Australia.” Transportation 44 (6): 1279–92.

84.

Wang

Kaidi

Zhang

Wenwen

. 2021. “The Role of Urban Form in the Performance of Shared Automated Vehicles.” Transportation Research Part D: Transport and Environment 93: 102744.

85.

Wegener

Michael

Fuerst

Franz

. 2004. “Land-Use Transport Interaction: State of the Art.” SSRN Electronic Journal. 10.2139/ssrn.1434678.

86.

Yap

Menno D.

Correia

Gonçalo

van Arem

Bart

. 2016. “Preferences of Travellers for Using Automated Vehicles as Last Mile Public Transport of Multimodal Train Trips.” Transportation Research Part A 94: 1–16.

87.

Zakharenko

Roman

. 2016. “Self-driving Cars Will Change Cities.” Regional Science and Urban Economics 61: 26–37.

88.

Zhang

Wenwen

Guhathakurta

Subhrajit

. 2017. “Parking Spaces in the Age of Shared Autonomous Vehicles: How Much Parking Will We Need and Where?” Transportation Research Record: Journal of the Transportation Research Board 2651 (1): 80–91.

89.

Zhang

Wenwen

Guhathakurta

Subhrajit

. 2021. “Residential Location Choice in the Era of Shared Autonomous Vehicles.” Journal of Planning Education and Research 41 (2): 135–48.

90.

Zhang

Wenwen

Guhathakurta

Subhrajit

Khalil

Elias B.

. 2018. “The Impact of Private Autonomous Vehicles on Vehicle Ownership and Unoccupied VMT Generation.” Transportation Research Part C: Emerging Technologies 90: 156–65.

91.

Zhang

Lei

Hong

Jinhyun

Nasri

Arefeh

Shen

Qing

. 2012. “How Built Environment Affects Travel Behavior: A Comparative Analysis of the Connections Between Land use and Vehicle Miles Traveled in US Cities.” Journal of Transport and Land Use 5: 40–52.

92.

Zhong

Haotian

Wei

Burris

Mark W.

Talebpour

Alireza

Sinha

Kumares C.

. 2020. “Will Autonomous Vehicles Change Auto Commuters’ Value of Travel Time?” Transportation Research Part D: Transport and Environment 83: 102303.