IDEAS home Printed from https://ideas.repec.org/a/eee/eejocm/v39y2021ics1755534521000191.html
   My bibliography  Save this article

A day in the life with an automated vehicle: Empirical analysis of data from an interactive stated activity-travel survey

Author

Listed:
  • Pudāne, Baiba
  • van Cranenburgh, Sander
  • Chorus, Caspar G.

Abstract

Fully Automated Vehicles (AVs) have been widely expected to revolutionise the future travel experience. Recent studies have shown that their impact may also reach beyond the travel episode, and lead their users to alter other activities performed during the day – their daily lifestyles. This study is among the first to empirically investigate the changes that travellers expect in their daily activities with AVs. To this aim, we created an interactive stated activity-travel survey, in which respondents designed their current daily schedule and, following that, redesigned it while imagining that their most frequently used travel mode is replaced with an AV. We administered the survey to 509 commuters in the Netherlands and analysed (changes in) on-board and stationary activity patterns using the multiple discrete-continuous extreme value (MDCEV) model. Results show a clear increase in the prevalence of various on-board activities in the AV compared to current modes, and even stronger increase for the high income and higher educated groups. Changes in stationary activities are less pronounced: no changes in the aggregate, but some changes within particular socio-demographic groups. Specific changes in stationary activities were associated with specific changes in on-board activities for the higher educated respondents: switching to AVs, they were more likely than others to add on-board work, meals, and leisure to their trips and more likely to add a getting ready activity to their stationary schedules. This study contributes to the growing body of literature that recognises and models on-board activities as an integral part of daily schedules.

Suggested Citation

  • Pudāne, Baiba & van Cranenburgh, Sander & Chorus, Caspar G., 2021. "A day in the life with an automated vehicle: Empirical analysis of data from an interactive stated activity-travel survey," Journal of choice modelling, Elsevier, vol. 39(C).
    • Handle: RePEc:eee:eejocm:v:39:y:2021:i:c:s1755534521000191
    DOI: 10.1016/j.jocm.2021.100286
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1755534521000191
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jocm.2021.100286?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pawlak, Jacek & Polak, John W. & Sivakumar, Aruna, 2015. "Towards a microeconomic framework for modelling the joint choice of activity–travel behaviour and ICT use," Transportation Research Part A: Policy and Practice, Elsevier, vol. 76(C), pages 92-112.
    2. Choi, Sungtaek & Mokhtarian, Patricia L., 2020. "How attractive is it to use the internet while commuting? A work-attitude-based segmentation of Northern California commuters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 37-50.
    3. Bhat, Chandra R. & Sen, Sudeshna, 2006. "Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model," Transportation Research Part B: Methodological, Elsevier, vol. 40(1), pages 35-53, January.
    4. Malokin, Aliaksandr & Circella, Giovanni & Mokhtarian, Patricia L., 2019. "How do activities conducted while commuting influence mode choice? Using revealed preference models to inform public transportation advantage and autonomous vehicle scenarios," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 82-114.
    5. Pawlak, Jacek & Polak, John W. & Sivakumar, Aruna, 2017. "A framework for joint modelling of activity choice, duration, and productivity while travelling," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 153-172.
    6. Sobhani, Anae & Eluru, Naveen & Faghih-Imani, Ahmadreza, 2013. "A latent segmentation based multiple discrete continuous extreme value model," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 154-169.
    7. Pinjari, Abdul Rawoof & Bhat, Chandra, 2010. "A multiple discrete-continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 562-583, May.
    8. Chandra Bhat & Konstadinos Goulias & Ram Pendyala & Rajesh Paleti & Raghuprasad Sidharthan & Laura Schmitt & Hsi-Hwa Hu, 2013. "A household-level activity pattern generation model with an application for Southern California," Transportation, Springer, vol. 40(5), pages 1063-1086, September.
    9. Frei, Charlotte & Mahmassani, Hani S. & Frei, Andreas, 2015. "Making time count: Traveler activity engagement on urban transit," Transportation Research Part A: Policy and Practice, Elsevier, vol. 76(C), pages 58-70.
    10. Imre Keseru & Cathy Macharis, 2018. "Travel-based multitasking: review of the empirical evidence," Transport Reviews, Taylor & Francis Journals, vol. 38(2), pages 162-183, March.
    11. 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: Policy and Practice, Elsevier, vol. 119(C), pages 359-382.
    12. Molin, Eric & Adjenughwure, Kingsley & de Bruyn, Menno & Cats, Oded & Warffemius, Pim, 2020. "Does conducting activities while traveling reduce the value of time? Evidence from a within-subjects choice experiment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 18-29.
    13. Hess, Stephane & Palma, David, 2019. "Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    14. Taiebat, Morteza & Stolper, Samuel & Xu, Ming, 2019. "Forecasting the Impact of Connected and Automated Vehicles on Energy Use: A Microeconomic Study of Induced Travel and Energy Rebound," Applied Energy, Elsevier, vol. 247(C), pages 297-308.
    15. Jia Tang & Feng Zhen & Jason Cao & Patricia L. Mokhtarian, 2018. "How do passengers use travel time? A case study of Shanghai–Nanjing high speed rail," Transportation, Springer, vol. 45(2), pages 451-477, March.
    16. Aggelos Soteropoulos & Martin Berger & Francesco Ciari, 2019. "Impacts of automated vehicles on travel behaviour and land use: an international review of modelling studies," Transport Reviews, Taylor & Francis Journals, vol. 39(1), pages 29-49, January.
    17. Bhat, Chandra R., 2018. "A new flexible multiple discrete–continuous extreme value (MDCEV) choice model," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 261-279.
    18. Fagnant, Daniel J. & Kockelman, Kara, 2015. "Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 167-181.
    19. Mustapha Harb & Yu Xiao & Giovanni Circella & Patricia L. Mokhtarian & Joan L. Walker, 2018. "Projecting travelers into a world of self-driving vehicles: estimating travel behavior implications via a naturalistic experiment," Transportation, Springer, vol. 45(6), pages 1671-1685, November.
    20. Calastri, Chiara & Hess, Stephane & Daly, Andrew & Carrasco, Juan Antonio, 2017. "Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 1-20.
    21. Taiebat, Morteza & Stolper, Samuel & Xu, Ming, 2019. "Forecasting the Impact of Connected and Automated Vehicles on Energy Use: A Microeconomic Study of Induced Travel and Energy Rebound," LawArXiv dk6qv, Center for Open Science.
    22. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.
    23. Lyons, Glenn & Jain, Juliet & Holley, David, 2007. "The use of travel time by rail passengers in Great Britain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(1), pages 107-120, January.
    24. Bernardo, Christina & Paleti, Rajesh & Hoklas, Megan & Bhat, Chandra, 2015. "An empirical investigation into the time-use and activity patterns of dual-earner couples with and without young children," Transportation Research Part A: Policy and Practice, Elsevier, vol. 76(C), pages 71-91.
    25. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    26. Xiaojuan Yu & Vincent van den Berg & Erik Verhoef, 2019. "Autonomous cars and dynamic bottleneck congestion revisited: how in-vehicle activities determine aggregate travel patterns," Tinbergen Institute Discussion Papers 19-067/VIII, Tinbergen Institute.
    27. 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, Elsevier, vol. 68(C), pages 142-148.
    28. Morteza Taiebat & Samuel Stolper & Ming Xu, 2019. "Forecasting the Impact of Connected and Automated Vehicles on Energy Use A Microeconomic Study of Induced Travel and Energy Rebound," Papers 1902.00382, arXiv.org, revised May 2019.
    29. Fábio Duarte & Carlo Ratti, 2018. "The Impact of Autonomous Vehicles on Cities: A Review," Journal of Urban Technology, Taylor & Francis Journals, vol. 25(4), pages 3-18, October.
    30. Jacek Pawlak, 2020. "Travel-based multitasking: review of the role of digital activities and connectivity," Transport Reviews, Taylor & Francis Journals, vol. 40(4), pages 429-456, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chiara Calastri & Jacek Pawlak & Richard Batley, 2022. "Participation in online activities while travelling: an application of the MDCEV model in the context of rail travel," Transportation, Springer, vol. 49(1), pages 61-87, February.
    2. Pudāne, Baiba, 2019. "Departure Time Choice and Bottleneck Congestion with Automated Vehicles: Role of On-board Activities," MPRA Paper 96328, University Library of Munich, Germany.
    3. Shamshiripour, Ali & Rahimi, Ehsan & (Kouros) Mohammadian, Abolfazl & Auld, Joshua, 2020. "Investigating the influence of latent lifestyles on productive travels: Insights into designing autonomous transit system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 469-484.
    4. Bounie, Nathan & Adoue, François & Koning, Martin & L'Hostis, Alain, 2019. "What value do travelers put on connectivity to mobile phone and Internet networks in public transport? Empirical evidence from the Paris region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 158-177.
    5. Saxena, Shobhit & Pinjari, Abdul Rawoof & Paleti, Rajesh, 2022. "A multiple discrete-continuous extreme value model with ordered preferences (MDCEV-OP): Modelling framework for episode-level activity participation and time-use analysis," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 259-283.
    6. Palma, David & Hess, Stephane, 2022. "Extending the Multiple Discrete Continuous (MDC) modelling framework to consider complementarity, substitution, and an unobserved budget," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 13-35.
    7. Calastri, Chiara & Hess, Stephane & Daly, Andrew & Carrasco, Juan Antonio, 2017. "Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 1-20.
    8. Jara-Díaz, Sergio & Rosales-Salas, Jorge, 2017. "Beyond transport time: A review of time use modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 97(C), pages 209-230.
    9. Ozonder, Gozde & Miller, Eric J., 2021. "Longitudinal investigation of skeletal activity episode timing decisions – A copula approach," Journal of choice modelling, Elsevier, vol. 40(C).
    10. Sun, Shanshan & Wong, Yiik Diew, 2023. "Drivers’ attention economy and adoption to autonomous vehicle," Transport Policy, Elsevier, vol. 138(C), pages 108-118.
    11. Tang, Jia & Mokhtarian, Patricia L. & Zhen, Feng, 2020. "How do passengers allocate and evaluate their travel time? Evidence from a survey on the Shanghai–Nanjing high speed rail corridor, China," Journal of Transport Geography, Elsevier, vol. 85(C).
    12. Calastri, Chiara & Giergiczny, Marek & Zedrosser, Andreas & Hess, Stephane, 2023. "Modelling activity patterns of wild animals - An application of the multiple discrete-continuous extreme value (MDCEV) model," Journal of choice modelling, Elsevier, vol. 47(C).
    13. Aliaksandr Malokin & Giovanni Circella & Patricia L. Mokhtarian, 2021. "Do millennials value travel time differently because of productive multitasking? A revealed-preference study of Northern California commuters," Transportation, Springer, vol. 48(5), pages 2787-2823, October.
    14. Mark Wardman & Phani Chintakayala & Chris Heywood, 2020. "The valuation and demand impacts of the worthwhile use of travel time with specific reference to the digital revolution and endogeneity," Transportation, Springer, vol. 47(3), pages 1515-1540, June.
    15. Muhamad Rizki & Tri Basuki Joewono & Dimas B. E. Dharmowijoyo & Prawira Fajarindra Belgiawan, 2021. "Does multitasking improve the travel experience of public transport users? Investigating the activities during commuter travels in the Bandung Metropolitan Area, Indonesia," Public Transport, Springer, vol. 13(2), pages 429-454, June.
    16. Schmid, Basil & Molloy, Joseph & Peer, Stefanie & Jokubauskaite, Simona & Aschauer, Florian & Hössinger, Reinhard & Gerike, Regine & Jara-Diaz, Sergio R. & Axhausen, Kay W., 2021. "The value of travel time savings and the value of leisure in Zurich: Estimation, decomposition and policy implications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 186-215.
    17. Tapia, Rodrigo J. & de Jong, Gerard & Larranaga, Ana M. & Bettella Cybis, Helena B., 2020. "Application of MDCEV to infrastructure planning in regional freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 255-271.
    18. Molin, Eric & Adjenughwure, Kingsley & de Bruyn, Menno & Cats, Oded & Warffemius, Pim, 2020. "Does conducting activities while traveling reduce the value of time? Evidence from a within-subjects choice experiment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 18-29.
    19. Pinjari, Abdul Rawoof & Augustin, Bertho & Sivaraman, Vijayaraghavan & Faghih Imani, Ahmadreza & Eluru, Naveen & Pendyala, Ram M., 2016. "Stochastic frontier estimation of budgets for Kuhn–Tucker demand systems: Application to activity time-use analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 117-133.
    20. Bhat, Chandra R., 2018. "A new flexible multiple discrete–continuous extreme value (MDCEV) choice model," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 261-279.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eejocm:v:39:y:2021:i:c:s1755534521000191. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/journal-of-choice-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.