On investigating the potential effects of private autonomous vehicle use on home/work relocations and commute times

https://doi.org/10.1016/j.trc.2019.11.013Get rights and content

Highlights

  • This study examines residential/work relocation and commute choices in an AV future.

  • A multivariate joint model is estimated considering individual attitudes.

  • Provides guidance for land use/travel demand models.

  • Estimated value of travel time savings (VTTS) decrease due to AVs relatively low.

  • Substantial urban sprawl predicted due to AVs, without proactive policy steps.

Abstract

The current study is motivated by the need to better understand the potential impacts that vehicular automation may have on individual decisions of residential and work relocation in a future autonomous vehicle (AV) scenario. The study employs a multivariate approach to model five behavioral dimensions simultaneously: (1) technology-savviness (TS) propensity, (2) interest in productive use of travel time (IPTT) propensity, (3) interest in work relocation, (4) interest in residential relocation, and (5) tolerance to an increase in commute travel time. Data from a web-based survey of commuters in 2017 in the Dallas-Fort Worth Metropolitan Area (DFW) is employed. The results show that both TS and IPTT, as well as demographic variables, impact relocation decisions when individuals have a private AV available for their commute. Importantly, there is considerable heterogeneity across individuals in the willingness to relocate and/or accept longer commute times in an AV future. As such, our model results may be used to inform inputs to land use and travel demand models in an AV future. Also, our results suggest that the magnitude of value of travel time savings (VTTS) decrease considered in many earlier AV impact simulation studies may be much higher than reality. Relative to 50% and even 100% VTTS decreases assumed in many studies, our results suggest a much more modest 30% or so overall decrease in VTTS because of the ability to commute in a privately-owned AV. Finally, our results do predict a rather substantial extent of urban sprawl due to AVs, potentially up to a 68% increase in the horizontal spread of cities such as Dallas-Fort Worth, unless proactive planning and policies are implemented to avert such consequences of AVs.

Introduction

One of the main transportation research challenges of the current decade is to understand and estimate the potential changes that self-driving vehicular technology may bring to transportation systems, individual behavior, and urban form. While fully automated vehicles (AVs) have the potential to enhance transportation supply and operations by allowing better traffic coordination and reduced roadway crashes, their impacts on the demand for travel and on the built environment are rather complex (see Milakis et al., 2017, Duarte and Ratti, 2018, Lavieri and Bhat, 2019a). One consequence of automation that is central to many of these potential impacts is the dissociation between the use of automobiles and the need to drive, that is, the increased “passengerization” of automobile travel, as discussed by Mokhtarian (2018).

Concept-AVs are being advertised by manufacturers as “new living areas” and “new alternatives to flights” (see Audi, 2019, Volvo, 2019), since the removal of the steering wheel will allow a complete re-design of the interior of vehicles with the objective of improving comfort levels and facilitating the meaningful use of the time traveling (from sleeping to socializing to working). Such changes are likely to reduce the disutility commonly attributed to time spent traveling, which could potentially influence individuals’ mode choices, increase their propensity to travel (number and/or distance of trips) and attenuate the perceived inconvenience of congestion.

A decrease in travel time disutility is already observed by recent studies on the impacts of multitasking on travel choices. For instance, based on revealed preference data, Malokin et al. (2017) found that the ability to multitask contributes to lower values of travel time savings (VTTS) among Californian millennial commuters. The same authors also observed that public transport modes would have their mode share decreased (by around 1.5 percentage points) if individuals did not have the option to use laptops/tablets while commuting. This result also led the authors to infer that, in a hypothetical AV scenario, the solo-auto mode can have an increase in share of a similar magnitude due to the added multitasking possibility (Malokin et al., 2019). Similarly, Lavieri and Bhat (2019b) observed that the interest in using travel time productively currently contributes to the use of ride-hailing services (revealed choice) and reduces perceived VTTS under hypothetical AV scenarios (stated choice). Also based on stated choice data, de Almeida Correia et al. (2019) identified that in-vehicle work activities have greater potential to reduce VTTS than in-vehicle leisure activities.

While available research on the effects of multitasking on value of travel time is predominantly focused on a mode choice setting, changes in in-vehicle time use can also impact out-of-vehicle time use (see, for example, Pudāne et al., 2018) and, consequently, influence other transportation related decisions, such as activity locations. In particular, if longer commute distances and times are tolerated, individuals may expand their job search areas or choose to relocate to more affordable or isolated areas. Such actions may not only result in increased distances traveled but could also contribute to urban sprawl. Considering that suburbanization is again on the rise in the United States (U.S.) (see Frey, 2018), automation could trigger an acceleration of this process and a surge in potentially more unsustainable patterns of energy and resources consumption (Ewing and Hamidi, 2015).

The current study is motivated by the need to better understand the potential impacts that vehicular automation may have on individual decisions that can result in urban sprawl. Specifically, we examine individual preferences toward residential and work relocation in a future AV scenario taking into consideration people’s technology-savviness and interest in the productive use of travel time, two important elements for in-vehicle multitasking that can influence time sensitivity and work/home location choices (in the rest of this paper, the term AV will be used to refer to privately owned AVs and AV use will refer to the use of privately owned AVs). In doing so, we use a multivariate approach to model five behavioral dimensions simultaneously: (1) technology-savviness propensity, (2) interest in productive use of travel time (IPTT) propensity, (3) interest in residential relocation, (4) interest in work relocation, and (5) tolerance to an increase in commute travel time. Different relationships between the five dependent variables are tested and lifecycle, lifestyle, and built environment characteristics are used as explanatory variables. The model is estimated using data obtained through a web-based survey of commuters in the Dallas-Fort Worth Metropolitan Area (DFW). Using the model, we arrive at informed guesstimates of the VTTS decrease that may be expected due to AV use for the commute, as well as a first quantification of the extent of urban sprawl that may be engendered by AVs.

The DFW area is one of the fastest growing regions in the U.S. and covers a 13-county area in northern Texas. The population of the DFW metro region is about 7.4 million, accounting for 27 percent of the state of Texas’ total population (Hegar, 2019). And, over time, a higher percentage of this population is living at the urban edges as opposed to in the central parts, a trend reflected in the highest percentage of population increases (approximately 18–26% increase in ten years) occurring in four counties to the north and east of the metropolitan center (U.S. Census, 2019). This urban sprawl is already having an impact on travel patterns, with the average one-way commute time in the DFW area being 27 min, two minutes higher than both the U.S. and State of Texas averages (Data USA, 2017). As importantly, an increasing percentage of individuals have a “super commute” of over 90 min (rising 17%, from 1.75% in 2010 to 2.05% in 2017) (U.S. Census, 2017). In this sense, vehicular automation could contribute to an already worrisome scenario of urban sprawl and commute lengths in DFW.

The remainder of this paper is organized as follows. The next section presents a literature overview of the current discussions and investigations related to the potential impacts of automation on the built environment and on urban sprawl. Section 3 introduces the analytic framework, explains the data sources, survey instrument, sample characteristics and modeling methodology. Section 4 presents the model estimation results and goodness of fit measures. Section 5 discusses how the model results may be used to inform land use-transportation forecasting systems in an AV future, and also derives first-estimates of the potential VTTS decrease and urban sprawl that may be engendered in an AV future. The final section concludes the paper with a summary, limitations, and future research directions.

Section snippets

Literature overview

The adoption of AVs and its implications for transportation and society have been the focus of extensive research in the past few years. From willingness to pay for automation, to preferences between AV ownership and sharing, to changes in vehicle miles traveled (VMT), to reductions in parking requirements, the growing literature has investigated multiple hypotheses for the automated future (see for example, Bansal et al., 2016, Childress et al., 2015, Daziano et al., 2017, Kröger et al., 2019,

The survey

The data used for the analysis in this paper was obtained through a web-based survey developed and administered by the authors in the fall of 2017. The distribution was achieved through mailing lists held by multiple entities (local transportation planning organizations, universities, private transportation sector companies, non-profit organizations, and online social media) resulting in a convenience sample. The survey was implemented in the Dallas-Fort Worth (DFW) metroplex and was limited to

Model results

The final model specification was obtained based on a systematic process of testing alternative combinations of explanatory variables and eliminating statistically insignificant ones. Also, for continuous variables such as respondent current commute time, a number of functional forms were tested, including linear form and a dummy variable categorization. In the final model specification, not all the variables included are statistically significant at a 95% confidence level, but some of these

Input for land use-travel demand models

Most earlier studies attempting to examine the potential effects of AVs make strong assumptions regarding important parameters in travel demand modeling. Rather than make such strong assumptions, the model developed in this paper can serve the purpose of providing important information based on a more informed investigation of potential user behaviors in an AV future. In fact, the model in this paper can be absorbed into an agent-based integrated land-use and travel evolution model, such as the

Discussion and conclusions

The field of transportation research has seen multiple studies that tout the potential operational and safety gains brought forth by AVs. Many of these studies are based on simulations, and make some substantial assumptions of user behavior and infrastructure capacity increases, without much basis for those assumptions in the first place. Further, there have been review/synthesis studies that attempt to assimilate and integrate the findings of individual simulation studies. Unfortunately, there

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was partially supported by the U.S. Department of Transportation through the Data-Supported Transportation Operations and Planning (D-STOP) (Grant No. DTRT13GUTC58) Tier 1 University Transportation Center. The authors are grateful to Lisa Macias for her help in formatting this document. Two anonymous reviewers provided useful comments on an earlier version of this paper.

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