- Email: [email protected]
An Examination of User Adoption Behavior of Autonomous Vehicles and Urban Sustainability Implications
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect Transportation Research Procedia 00 (2016) 000–000
Available online at www.sciencedirect.com
ScienceDirect
www.elsevier.com/locate/procedia
Transportation Research Procedia 00 (2016) 000–000
Transportation Research Procedia 41 (2019) 187–190
www.elsevier.com/locate/procedia
International Scientific Conference on Mobility and Transport UrbanScientific Mobility –Conference Shaping the Together International onFuture Mobility and Transport mobil.TUM 2018, 13-14 June 2018, Munich, Germany Urban Mobility – Shaping the Future Together mobil.TUM 2018, 13-14 June 2018, Munich, Germany
An Examination of User Adoption Behavior of Autonomous Vehicles and An Examination of User Adoption Behavior of Autonomous Vehicles and Urban Sustainability Implications Urban Sustainability Implications a
Ransford A. Acheamponga* Federico Cugurulloa, Ivana Dusparicb, Maxime Guériaub a a b b Ransford A.Trinity Acheampong * Federico Cugurullo , Ivana Dusparic , Maxime Guériau Department of Geography, College Dublin, the University of Dublin, Dublin 2, Ireland | School of Computer Science and Statistics, Trinity College b
Dublin, the University of Dublin, Dublin 2, Ireland a Department of Geography, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland | b School of Computer Science and Statistics, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland
© 2017 The Authors. Published by Elsevier B.V.
© 2019 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of Elsevier the organizing committee of mobil.TUM 2018. © 2017 Theopen Authors. Published B.V.BY-NC-ND This is an access article by under the CC license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the organizing committee of mobil.TUM Peer-review under responsibility of the scientific committee of the2018. mobil.TUM18. Keywords: Self-driving cars; Autonomous transport; Attitudes; User adoption behaviour; Travel behaviour; Urban sustainability Keywords: Self-driving cars; Autonomous transport; Attitudes; User adoption behaviour; Travel behaviour; Urban sustainability
1. Background and Problem Statement 1. Background and Problem Statement Autonomous vehicles (AVs) constitute one of the emerging socio-technical innovations in the transportation sector that will revolutionize motorized in futureone cities. For emerging the first time in the history of transportation, fully-autonomous Autonomous vehicles mobility (AVs) constitute of the socio-technical innovations in the transportation sector cars that (i.e. will Level-4 and Level-5 automation), sensing andFor communication safety-critical control functions and revolutionize motorized mobility using in future cities. the first time technologies in the historywill of assume transportation, fully-autonomous cars (i.e. navigate cities without a human driver (NHTSA, 2013; Hashimoto et al., 2016). At the moment, AVs are being tested in real-world Level-4 and Level-5 automation), using sensing and communication technologies will assume safety-critical control functions and urban environments such as Greenwich (UK), California (USA) and One-North District (Singapore) to be thentested deployed in other navigate cities without a human driver (NHTSA, 2013; Hashimoto et al., 2016). At the moment, AVs are being in real-world cities. urban environments such as Greenwich (UK), California (USA) and One-North District (Singapore) to be then deployed in other The autonomous mobility revolution presents enormous uncertainties about the future of transportation in cities. On the one cities. hand, are expected to reduce crash presents rates andenormous fatalities, uncertainties increase accessibility and promote free-floating TheAVs autonomous mobility revolution about thelevels futureinofcities transportation in cities. On the carone sharingAVs services, thereby to contributing to rates a reduction in car-ownership, congestion, levels CO2 emissions andpromote parking free-floating demand in cities hand, are expected reduce crash and fatalities, increase accessibility in cities and car(Fagnant and Kockelman, 2015; Schoettle Sivak, 2015; Zhang et al., congestion, 2015; Milakis et al., 2017). On other hand, automating sharing services, thereby contributing to aand reduction in car-ownership, CO2 emissions andthe parking demand in cities driving change travel behaviours increasing motorized vehicle occupancy andhand, public transport (Fagnantcould and Kockelman, 2015; Schoettlebyand Sivak, 2015; Zhang trips et al.,while 2015;decreasing Milakis et al., 2017). On the other automating mode (Fuller, 2016; Litman, 2017).by Moreover, justmotorized as the popularization the early automobiles in the 1920s, fundamentally drivingshare could change travel behaviours increasing trips while of decreasing vehicle occupancy and public transport reshaped the(Fuller, form and structure citiesMoreover, (Kenworthy Laube, 1996) so of could AVsautomobiles change urban structures by potentially mode share 2016; Litman,of 2017). justand as the popularization the early in the 1920s, fundamentally favouring urban sprawl suburbanization (Meyer et al., 2017). AVs1996) couldso also affect the economy of cities negatively by causing reshaped the form and and structure of cities (Kenworthy and Laube, could AVs change urban structures by potentially huge job losses the transportation industry(Meyer (Davidson Spinoulas, 2015). favouring urbanin sprawl and suburbanization et al.,and 2017). AVs could also affect the economy of cities negatively by causing Atjob thelosses moment, and quantifying impactsand of Spinoulas, the AV revolution huge in theassessing transportation industry (Davidson 2015). on urban environments is fraught with many uncertainties. One of them is obtaining reliable assessment of public acceptance this environments new technology. Previouswith studies in At the moment, assessing and quantifying impacts of the AV revolution onfor urban is fraught many technology adoption when potential are presented withacceptance a new product or service, several factors influence their uncertainties. One ofshows them that is obtaining reliableusers assessment of public for this new technology. Previous studies in decision about the extent to that which theypotential will use users it (Straub, 2009; Venkatesh andproduct Davis, 2000; Rogers, 2000). Yet, so far, existing technology adoption shows when are presented with a new or service, several factors influence their studies userthe adoption diffusion cars have oversimplified conceptual and methodological approaches. decisionon about extent and to which they of willdriverless use it (Straub, 2009;taken Venkatesh and Davis, 2000; Rogers, 2000). Yet, so far, existing Previous (see e.g.and Daziano et al,of2017; Nair et al.,have 2017; Bansal et al., 2016;conceptual Lavieri et and al., 2017; Lavasani et al., 2016; studies onstudies user adoption diffusion driverless cars taken oversimplified methodological approaches. Bansal Kockelman, haveetfocused mainly onal., the2017; financial attributes of driverless only accounting for a Previousand studies (see e.g.2017) Daziano al, 2017; Nair et Bansal et al., 2016; Lavieritechnologies, et al., 2017; Lavasani et al., 2016; limited number of attitudinal and socio-demographic factors in examining AV user adoption decisions. Bansal and Kockelman, 2017) have focused mainly on the financial attributes of driverless technologies, only accounting for a limited number of attitudinal and socio-demographic factors in examining AV user adoption decisions.
Corresponding author. Tel.: +353-877461592 E-mail address: [email protected] Corresponding author. Tel.: +353-877461592 E-mail address: [email protected] 2214-241X © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the organizing committee of mobil.TUM 2018. 2214-241X ©2017 Authors. Published by Elsevier 2352-1465 2019The The Authors. Published by B.V. Elsevier Ltd. Peer-review of the organizing committee of mobil.TUM This is an under open responsibility access article under the CC BY-NC-ND license 2018. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the mobil.TUM18. 10.1016/j.trpro.2019.09.037
188 2
Ransford A. Acheampong et al. / Transportation Research Procedia 41 (2019) 187–190
Author name / Transportation Research Procedia 00 (2016) 000–000
Besides these opinion-based studies, simulation-based studies have sought to simulate optimal vehicle fleet-size in dynamic autonomous vehicle ride-sharing systems (Fagmant and Kockelman, 2015) as well as to assess the land use and environmental impacts of various driverless cars adoption and diffusion scenarios by largely using virtual, hypothetical cities (e.g. Fagmant and Kockelman, 2015; Zhang et al., 2015, 2017; Wadud et al., 2016). Using input data not so different from those captured in the opinion-based studies, the broad range of factors that could influence user adoption behavior for different driverless car options and how these are linked to realizing sustainable mobility imperatives, have so far not been given adequate attention in the literature.
1.1. Research aim and questions In view of the above, this paper examines the determinants of user adoption intentions for driverless cars and the urban sustainability implications of user preferences for different AV modes. In line with this overarching objective, the paper addresses the following questions: 1. What factors impact on individuals’ decision about adopting autonomous cars? 2. What are the modal preferences for autonomous vehicles in the population? 3. What are the urban sustainability implications of user preferences?
2. Methodology The paper adopts a case study design and focuses on the city of Dublin in Ireland. Dublin is a medium-sized European city that is on a path to redefine itself as a smart and sustainable city. In recent years, the city has started a number of initiatives including car-sharing, bike sharing, and installation of electric-cars charging points with the aim of leveraging eco-innovations to transform the city’s transportation system. The city therefore provides a suitable context to examine the extent to which public attitudes, opinions and sentiments will affect the adoption and diffusion of driverless cars. In order to capture the multiple facets of user adoption behavior and diffusion of AVs, this paper assembles and deploys a theory-grounded, behaviorally consistent framework that taps into existing models of technology adoption and diffusion (Venkatesh and Davis, 2000; Rogers, 2000 ), socio-psychological and socio-ecological models of human choice behavior under volitional control (Ajzen, 1991; McLeroy et al., 1988; Sigurdardottir et al., 2013) and economic theory of consumer choice (see e.g. Breidert et al., 2006). Moreover, to understand how adoption decisions at the individual level are linked to urban sustainability imperatives, the paper also examines the influence on AV mode preferences of individuals’ attitudes towards the environment (see e.g. Milfont and Duckitt, 2010) and the emerging ethos of collaborative consumption/sharing (see e.g. Bostman and Rogers; Bardhi and Echardt, 2012). Data for the paper is from a cross-sectional attitudinal and modal preferences survey of a representative sample of 1,233 individuals in Dublin. Using the conceptual framework outlined above, the survey elicits data covering individuals’: background socio-demographic information and current travel characteristics; attitudes towards technology; attitudes towards the environment; attitudes towards sharing/collaborative consumption; perception of the benefits and ease of use of AVs perception of the influence of significant others on adoption decisions; interests in AVs and adoption intensions; and preferences for different AV modes (e.g. ownership, sharing, and public transport) as well as engine fuel source for preferred AV modes (e.g. electricity, petrol/diesel). Given the multiple behavioral constructs and indicator variables involved in this research, Structural Equation Modeling (SEM) is adopted as the statistical modelling technique in this paper. SEM allows us to represent and validate hypothesized relationships among the elements of the conceptual framework deployed in the user adoption survey while accounting for measurement errors in latent variables included in the analysis.
4. Results and Contributions This paper identifies the key determining factors of user adoption intentions for driverless cars. In particular, it identifies the extent to which socio-demographic factors interact with various attitudinal factors and attributes of driverless cars as perceived by individuals to influence adoption decisions. The time horizons over which individuals intend to adopt AVs when the technology becomes available on the market is also captured. In addition, the paper presents results on the heterogeneity of individual’s preferences for different AV modes including ownership, sharing and public transit and engine fuel sources for preferred AV modes. The sustainability implications of individuals’ preferences are discussed. The paper, therefore, contributes to our
Ransford A. Acheampong et al. / Transportation Research Procedia 41 (2019) 187–190 Author name / Transportation Research Procedia 00 (2016) 000–000
189
3
understanding of user adoption factors with respect to driverless cars and the possibilities this new technology offers in the transition towards sustainable urban futures.
Acknowledgements This research is funded by the Irish Research Council (IRC) under the New Horizons Grant Scheme.
References Ajzen, I., (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes 50, 179–211. Bansal, P., & Kockelman, K. M. (2017). Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies. Transportation Research Part A: Policy and Practice, 95, 49-63. Bardhi, F., & Eckhardt, G. M. (2012). Access-based consumption: The case of car sharing. Journal of consumer research, 39(4), 881-898. Botsman, R., & Rogers, R. (2010). What's mine is yours: the rise of collaborative consumption. New York: Harper Business Breidert, C., Hahsler, M., & Reutterer, T. (2006). A review of methods for measuring willingness-to-pay. Innovative Marketing, 2(4), 8-32. Davidson, P., & Spinoulas, A. (2015). Autonomous vehicles: what could this mean for the future of transport. In Australian Institute of Traffic Planning and Management (AITPM) National Conference, 2015, Brisbane, Queensland, Australia (p. 7). Fagnant, D. J., & Kockelman, K. M. (2015). Dynamic ride-sharing and optimal fleet sizing for a system of shared autonomous vehicles. In Transportation Research Board 94th Annual Meeting (No. 151962). Fuller, B. (2016). Cautious Optimism About Driverless Cars and Land Use in American Metropolitan Areas. Cityscape, 18(3), 181. Hashimoto, Y., Gu, Y., Hsu, L. T., Iryo-Asano, M., & Kamijo, S. (2016). A probabilistic model of pedestrian crossing behavior at signalized intersections for connected vehicles. Transportation research part C: emerging technologies, 71, 164-181. Kenworthy, J. R., & Laube, F. B. (1996). Automobile dependence in cities: an international comparison of urban transport and land use patterns with implications for sustainability. Environmental impact assessment review, 16(4), 279-308. Lavasani, M., Jin, X., & Du, Y. (2016). Market penetration model for autonomous vehicles based on previous technology adoption experiences. In Transportation Research Board 95th Annual Meeting (No. 16-2284). Lavieri, P. S., Garikapati, V. M., Bhat, C. R., Pendyala, R. M., Astroza, S., & Dias, F. F. (2017). Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies. 96th Annual Meeting of the Transportation Research Board (No. 17-05843). McLeroy, K. R., Bibeau, D., Steckler, A., & Glanz, K. (1988). An ecological perspective on health promotion programs. Health education quarterly, 15(4), 351-377. Milakis, D., Van Arem, B., & Van Wee, B. (2017). Policy and society related implications of automated driving: A review of literature and directions for future research. Journal of Intelligent Transportation Systems, 1-25. NHTSA (2013), Preliminary Statement of Policy Concerning Automated Vehicles, National Highway Traffic Safety Administration (www.nhtsa.gov). Rogers M. E. (2000). Diffusion of innovations. New York: The Free Press Schonberger B and Gutmann S (2013), A Self-Driving Future: At the Intersection of Driverless Cars and Car Sharing, Sightline Institute (www.sightline.org); at http://daily.sightline.org/2013/06/04/a-selfdriving-future.
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Author name / Transportation Research Procedia 00 (2016) 000–000
Shaheen, S. A., & Cohen, A. P. (2013). Carsharing and personal vehicle services: worldwide market developments and emerging trends. International Journal of Sustainable Transportation, 7(1), 5-34 Sigurdardottir, S. B., Kaplan, S., Møller, M., & Teasdale, T. W. (2013). Understanding Adolescents’ intentions to commute by car or bicycle as adults. Transportation research part D: transport and environment, 24, 1-9. Sivak M. and Schoettle B (2015), Potential Impact of Self-Driving Vehicles on Household Vehicle Demand and Usage, Sustainable Worldwide Transportation Program, Transportation Research Institute, The University of Michigan. Report No. UMTRI-2015-3 Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of educational research, 79(2), 625-649. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. Wadud, Z., MacKenzie, D., & Leiby, P. (2016). Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles. Transportation Research Part A: Policy and Practice, 86, 1-18. Zhang, W., & Guhathakurta, S. (2017). Parking spaces in the age of shared autonomous vehicles: How much parking will we need and where. In Transportation Research Board 96th Annual Meeting.
ScienceDirect ScienceDirect Transportation Research Procedia 00 (2016) 000–000
Available online at www.sciencedirect.com
ScienceDirect
www.elsevier.com/locate/procedia
Transportation Research Procedia 00 (2016) 000–000
Transportation Research Procedia 41 (2019) 187–190
www.elsevier.com/locate/procedia
International Scientific Conference on Mobility and Transport UrbanScientific Mobility –Conference Shaping the Together International onFuture Mobility and Transport mobil.TUM 2018, 13-14 June 2018, Munich, Germany Urban Mobility – Shaping the Future Together mobil.TUM 2018, 13-14 June 2018, Munich, Germany
An Examination of User Adoption Behavior of Autonomous Vehicles and An Examination of User Adoption Behavior of Autonomous Vehicles and Urban Sustainability Implications Urban Sustainability Implications a
Ransford A. Acheamponga* Federico Cugurulloa, Ivana Dusparicb, Maxime Guériaub a a b b Ransford A.Trinity Acheampong * Federico Cugurullo , Ivana Dusparic , Maxime Guériau Department of Geography, College Dublin, the University of Dublin, Dublin 2, Ireland | School of Computer Science and Statistics, Trinity College b
Dublin, the University of Dublin, Dublin 2, Ireland a Department of Geography, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland | b School of Computer Science and Statistics, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland
© 2017 The Authors. Published by Elsevier B.V.
© 2019 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of Elsevier the organizing committee of mobil.TUM 2018. © 2017 Theopen Authors. Published B.V.BY-NC-ND This is an access article by under the CC license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the organizing committee of mobil.TUM Peer-review under responsibility of the scientific committee of the2018. mobil.TUM18. Keywords: Self-driving cars; Autonomous transport; Attitudes; User adoption behaviour; Travel behaviour; Urban sustainability Keywords: Self-driving cars; Autonomous transport; Attitudes; User adoption behaviour; Travel behaviour; Urban sustainability
1. Background and Problem Statement 1. Background and Problem Statement Autonomous vehicles (AVs) constitute one of the emerging socio-technical innovations in the transportation sector that will revolutionize motorized in futureone cities. For emerging the first time in the history of transportation, fully-autonomous Autonomous vehicles mobility (AVs) constitute of the socio-technical innovations in the transportation sector cars that (i.e. will Level-4 and Level-5 automation), sensing andFor communication safety-critical control functions and revolutionize motorized mobility using in future cities. the first time technologies in the historywill of assume transportation, fully-autonomous cars (i.e. navigate cities without a human driver (NHTSA, 2013; Hashimoto et al., 2016). At the moment, AVs are being tested in real-world Level-4 and Level-5 automation), using sensing and communication technologies will assume safety-critical control functions and urban environments such as Greenwich (UK), California (USA) and One-North District (Singapore) to be thentested deployed in other navigate cities without a human driver (NHTSA, 2013; Hashimoto et al., 2016). At the moment, AVs are being in real-world cities. urban environments such as Greenwich (UK), California (USA) and One-North District (Singapore) to be then deployed in other The autonomous mobility revolution presents enormous uncertainties about the future of transportation in cities. On the one cities. hand, are expected to reduce crash presents rates andenormous fatalities, uncertainties increase accessibility and promote free-floating TheAVs autonomous mobility revolution about thelevels futureinofcities transportation in cities. On the carone sharingAVs services, thereby to contributing to rates a reduction in car-ownership, congestion, levels CO2 emissions andpromote parking free-floating demand in cities hand, are expected reduce crash and fatalities, increase accessibility in cities and car(Fagnant and Kockelman, 2015; Schoettle Sivak, 2015; Zhang et al., congestion, 2015; Milakis et al., 2017). On other hand, automating sharing services, thereby contributing to aand reduction in car-ownership, CO2 emissions andthe parking demand in cities driving change travel behaviours increasing motorized vehicle occupancy andhand, public transport (Fagnantcould and Kockelman, 2015; Schoettlebyand Sivak, 2015; Zhang trips et al.,while 2015;decreasing Milakis et al., 2017). On the other automating mode (Fuller, 2016; Litman, 2017).by Moreover, justmotorized as the popularization the early automobiles in the 1920s, fundamentally drivingshare could change travel behaviours increasing trips while of decreasing vehicle occupancy and public transport reshaped the(Fuller, form and structure citiesMoreover, (Kenworthy Laube, 1996) so of could AVsautomobiles change urban structures by potentially mode share 2016; Litman,of 2017). justand as the popularization the early in the 1920s, fundamentally favouring urban sprawl suburbanization (Meyer et al., 2017). AVs1996) couldso also affect the economy of cities negatively by causing reshaped the form and and structure of cities (Kenworthy and Laube, could AVs change urban structures by potentially huge job losses the transportation industry(Meyer (Davidson Spinoulas, 2015). favouring urbanin sprawl and suburbanization et al.,and 2017). AVs could also affect the economy of cities negatively by causing Atjob thelosses moment, and quantifying impactsand of Spinoulas, the AV revolution huge in theassessing transportation industry (Davidson 2015). on urban environments is fraught with many uncertainties. One of them is obtaining reliable assessment of public acceptance this environments new technology. Previouswith studies in At the moment, assessing and quantifying impacts of the AV revolution onfor urban is fraught many technology adoption when potential are presented withacceptance a new product or service, several factors influence their uncertainties. One ofshows them that is obtaining reliableusers assessment of public for this new technology. Previous studies in decision about the extent to that which theypotential will use users it (Straub, 2009; Venkatesh andproduct Davis, 2000; Rogers, 2000). Yet, so far, existing technology adoption shows when are presented with a new or service, several factors influence their studies userthe adoption diffusion cars have oversimplified conceptual and methodological approaches. decisionon about extent and to which they of willdriverless use it (Straub, 2009;taken Venkatesh and Davis, 2000; Rogers, 2000). Yet, so far, existing Previous (see e.g.and Daziano et al,of2017; Nair et al.,have 2017; Bansal et al., 2016;conceptual Lavieri et and al., 2017; Lavasani et al., 2016; studies onstudies user adoption diffusion driverless cars taken oversimplified methodological approaches. Bansal Kockelman, haveetfocused mainly onal., the2017; financial attributes of driverless only accounting for a Previousand studies (see e.g.2017) Daziano al, 2017; Nair et Bansal et al., 2016; Lavieritechnologies, et al., 2017; Lavasani et al., 2016; limited number of attitudinal and socio-demographic factors in examining AV user adoption decisions. Bansal and Kockelman, 2017) have focused mainly on the financial attributes of driverless technologies, only accounting for a limited number of attitudinal and socio-demographic factors in examining AV user adoption decisions.
Corresponding author. Tel.: +353-877461592 E-mail address: [email protected] Corresponding author. Tel.: +353-877461592 E-mail address: [email protected] 2214-241X © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the organizing committee of mobil.TUM 2018. 2214-241X ©2017 Authors. Published by Elsevier 2352-1465 2019The The Authors. Published by B.V. Elsevier Ltd. Peer-review of the organizing committee of mobil.TUM This is an under open responsibility access article under the CC BY-NC-ND license 2018. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the mobil.TUM18. 10.1016/j.trpro.2019.09.037
188 2
Ransford A. Acheampong et al. / Transportation Research Procedia 41 (2019) 187–190
Author name / Transportation Research Procedia 00 (2016) 000–000
Besides these opinion-based studies, simulation-based studies have sought to simulate optimal vehicle fleet-size in dynamic autonomous vehicle ride-sharing systems (Fagmant and Kockelman, 2015) as well as to assess the land use and environmental impacts of various driverless cars adoption and diffusion scenarios by largely using virtual, hypothetical cities (e.g. Fagmant and Kockelman, 2015; Zhang et al., 2015, 2017; Wadud et al., 2016). Using input data not so different from those captured in the opinion-based studies, the broad range of factors that could influence user adoption behavior for different driverless car options and how these are linked to realizing sustainable mobility imperatives, have so far not been given adequate attention in the literature.
1.1. Research aim and questions In view of the above, this paper examines the determinants of user adoption intentions for driverless cars and the urban sustainability implications of user preferences for different AV modes. In line with this overarching objective, the paper addresses the following questions: 1. What factors impact on individuals’ decision about adopting autonomous cars? 2. What are the modal preferences for autonomous vehicles in the population? 3. What are the urban sustainability implications of user preferences?
2. Methodology The paper adopts a case study design and focuses on the city of Dublin in Ireland. Dublin is a medium-sized European city that is on a path to redefine itself as a smart and sustainable city. In recent years, the city has started a number of initiatives including car-sharing, bike sharing, and installation of electric-cars charging points with the aim of leveraging eco-innovations to transform the city’s transportation system. The city therefore provides a suitable context to examine the extent to which public attitudes, opinions and sentiments will affect the adoption and diffusion of driverless cars. In order to capture the multiple facets of user adoption behavior and diffusion of AVs, this paper assembles and deploys a theory-grounded, behaviorally consistent framework that taps into existing models of technology adoption and diffusion (Venkatesh and Davis, 2000; Rogers, 2000 ), socio-psychological and socio-ecological models of human choice behavior under volitional control (Ajzen, 1991; McLeroy et al., 1988; Sigurdardottir et al., 2013) and economic theory of consumer choice (see e.g. Breidert et al., 2006). Moreover, to understand how adoption decisions at the individual level are linked to urban sustainability imperatives, the paper also examines the influence on AV mode preferences of individuals’ attitudes towards the environment (see e.g. Milfont and Duckitt, 2010) and the emerging ethos of collaborative consumption/sharing (see e.g. Bostman and Rogers; Bardhi and Echardt, 2012). Data for the paper is from a cross-sectional attitudinal and modal preferences survey of a representative sample of 1,233 individuals in Dublin. Using the conceptual framework outlined above, the survey elicits data covering individuals’: background socio-demographic information and current travel characteristics; attitudes towards technology; attitudes towards the environment; attitudes towards sharing/collaborative consumption; perception of the benefits and ease of use of AVs perception of the influence of significant others on adoption decisions; interests in AVs and adoption intensions; and preferences for different AV modes (e.g. ownership, sharing, and public transport) as well as engine fuel source for preferred AV modes (e.g. electricity, petrol/diesel). Given the multiple behavioral constructs and indicator variables involved in this research, Structural Equation Modeling (SEM) is adopted as the statistical modelling technique in this paper. SEM allows us to represent and validate hypothesized relationships among the elements of the conceptual framework deployed in the user adoption survey while accounting for measurement errors in latent variables included in the analysis.
4. Results and Contributions This paper identifies the key determining factors of user adoption intentions for driverless cars. In particular, it identifies the extent to which socio-demographic factors interact with various attitudinal factors and attributes of driverless cars as perceived by individuals to influence adoption decisions. The time horizons over which individuals intend to adopt AVs when the technology becomes available on the market is also captured. In addition, the paper presents results on the heterogeneity of individual’s preferences for different AV modes including ownership, sharing and public transit and engine fuel sources for preferred AV modes. The sustainability implications of individuals’ preferences are discussed. The paper, therefore, contributes to our
Ransford A. Acheampong et al. / Transportation Research Procedia 41 (2019) 187–190 Author name / Transportation Research Procedia 00 (2016) 000–000
189
3
understanding of user adoption factors with respect to driverless cars and the possibilities this new technology offers in the transition towards sustainable urban futures.
Acknowledgements This research is funded by the Irish Research Council (IRC) under the New Horizons Grant Scheme.
References Ajzen, I., (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes 50, 179–211. Bansal, P., & Kockelman, K. M. (2017). Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies. Transportation Research Part A: Policy and Practice, 95, 49-63. Bardhi, F., & Eckhardt, G. M. (2012). Access-based consumption: The case of car sharing. Journal of consumer research, 39(4), 881-898. Botsman, R., & Rogers, R. (2010). What's mine is yours: the rise of collaborative consumption. New York: Harper Business Breidert, C., Hahsler, M., & Reutterer, T. (2006). A review of methods for measuring willingness-to-pay. Innovative Marketing, 2(4), 8-32. Davidson, P., & Spinoulas, A. (2015). Autonomous vehicles: what could this mean for the future of transport. In Australian Institute of Traffic Planning and Management (AITPM) National Conference, 2015, Brisbane, Queensland, Australia (p. 7). Fagnant, D. J., & Kockelman, K. M. (2015). Dynamic ride-sharing and optimal fleet sizing for a system of shared autonomous vehicles. In Transportation Research Board 94th Annual Meeting (No. 151962). Fuller, B. (2016). Cautious Optimism About Driverless Cars and Land Use in American Metropolitan Areas. Cityscape, 18(3), 181. Hashimoto, Y., Gu, Y., Hsu, L. T., Iryo-Asano, M., & Kamijo, S. (2016). A probabilistic model of pedestrian crossing behavior at signalized intersections for connected vehicles. Transportation research part C: emerging technologies, 71, 164-181. Kenworthy, J. R., & Laube, F. B. (1996). Automobile dependence in cities: an international comparison of urban transport and land use patterns with implications for sustainability. Environmental impact assessment review, 16(4), 279-308. Lavasani, M., Jin, X., & Du, Y. (2016). Market penetration model for autonomous vehicles based on previous technology adoption experiences. In Transportation Research Board 95th Annual Meeting (No. 16-2284). Lavieri, P. S., Garikapati, V. M., Bhat, C. R., Pendyala, R. M., Astroza, S., & Dias, F. F. (2017). Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies. 96th Annual Meeting of the Transportation Research Board (No. 17-05843). McLeroy, K. R., Bibeau, D., Steckler, A., & Glanz, K. (1988). An ecological perspective on health promotion programs. Health education quarterly, 15(4), 351-377. Milakis, D., Van Arem, B., & Van Wee, B. (2017). Policy and society related implications of automated driving: A review of literature and directions for future research. Journal of Intelligent Transportation Systems, 1-25. NHTSA (2013), Preliminary Statement of Policy Concerning Automated Vehicles, National Highway Traffic Safety Administration (www.nhtsa.gov). Rogers M. E. (2000). Diffusion of innovations. New York: The Free Press Schonberger B and Gutmann S (2013), A Self-Driving Future: At the Intersection of Driverless Cars and Car Sharing, Sightline Institute (www.sightline.org); at http://daily.sightline.org/2013/06/04/a-selfdriving-future.
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Ransford A. Acheampong et al. / Transportation Research Procedia 41 (2019) 187–190
Author name / Transportation Research Procedia 00 (2016) 000–000
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