IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v170y2025icp110-119.html

Understanding preferences for autonomous trucks functions in China: Insights from drivers and organizational buyers

Author

Listed:
  • Fu, Hanlong
  • Fu, Xiaowen
  • Ye, Shi
  • Wang, Kun
  • Chen, Tiantian

Abstract

The transportation industry is undergoing a significant transformation as it integrates advanced driver assistance systems (ADAS) technologies. This shift is particularly important in the trucking industry. Driver assistance technologies offer a promising solution for improving safety and reducing traffic accidents. However, the trucking industry lags significantly behind passenger vehicles in the maturity and penetration rate of such technologies. This study uses a stated preference survey to explore the purchasing preferences for ADAS functions among truck drivers and organization buyers in China. Our findings show that truck drivers with a safe driving history prioritize reliability and assistance features such as automatic emergency braking, adaptive cruise control, and lane-centering control. In contrast, drivers with a record of unsafe driving favor more advanced ADAS functions, such as city or highway navigation on autopilot, owing to their ability to alleviate driving stress. Buyers from organizations, compared with individual truck drivers, are more averse to the additional costs of ADAS technologies, while larger companies seem more willing to invest in autonomous trucks than are smaller businesses and individuals. However, top management teams remain cautious, reflecting a lack of confidence in the operational and safety benefits of the current technology at Level 2 autonomy. Resistance to adopting autonomous trucks is also stronger among male (vs. female) drivers and older drivers, who comprise a large segment of the domestic market. The study recommends that autonomous vehicle system providers and governments prioritize active safety functions to further improve safety. Furthermore, it is suggested that extensive training and trials be provided to increase trust and confidence in autonomous truck technologies among industry stakeholders.

Suggested Citation

  • Fu, Hanlong & Fu, Xiaowen & Ye, Shi & Wang, Kun & Chen, Tiantian, 2025. "Understanding preferences for autonomous trucks functions in China: Insights from drivers and organizational buyers," Transport Policy, Elsevier, vol. 170(C), pages 110-119.
  • Handle: RePEc:eee:trapol:v:170:y:2025:i:c:p:110-119
    DOI: 10.1016/j.tranpol.2025.05.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X25001982
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tranpol.2025.05.019?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Prateek Bansal & Kara M. Kockelman, 2018. "Are we ready to embrace connected and self-driving vehicles? A case study of Texans," Transportation, Springer, vol. 45(2), pages 641-675, March.
    2. Chen, Yang & Tao, Kan & Jiao, Wen & Yang, Dong, 2020. "Investigating the underlying social psychology of the innovation adoption in container trucking industry," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 259-270.
    3. Chen, Tiantian & Fu, Xiaowen & Hensher, David A. & Li, Zhi-Chun & Sze, N.N., 2022. "The effect of online meeting and health screening on business travel: A stated preference case study in Hong Kong," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    4. Chen, Tiantian & Fu, Xiaowen & Hensher, David A. & Li, Zhi-Chun & Sze, N.N., 2024. "Effects of proactive and reactive health control measures on public transport preferences of passengers – A stated preference study during the COVID-19 pandemic," Transport Policy, Elsevier, vol. 146(C), pages 175-192.
    5. Hohenberger, Christoph & Spörrle, Matthias & Welpe, Isabell M., 2016. "How and why do men and women differ in their willingness to use automated cars? The influence of emotions across different age groups," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 374-385.
    6. Li, Hao & Gao, Kun & Tu, Huizhao, 2017. "Variations in mode-specific valuations of travel time reliability and in-vehicle crowding: Implications for demand estimation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 250-263.
    7. Raj, Alok & Kumar, J. Ajith & Bansal, Prateek, 2020. "A multicriteria decision making approach to study barriers to the adoption of autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 122-137.
    8. 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.
    9. Ballantyne, Erica E.F. & Lindholm, Maria & Whiteing, Anthony, 2013. "A comparative study of urban freight transport planning: addressing stakeholder needs," Journal of Transport Geography, Elsevier, vol. 32(C), pages 93-101.
    10. Ivanov, Stanislav & Kuyumdzhiev, Mihail & Webster, Craig, 2020. "Automation fears: Drivers and solutions," Technology in Society, Elsevier, vol. 63(C).
    11. repec:osf:socarx:jze3u_v1 is not listed on IDEAS
    12. William H. Greene & David A. Hensher, 2013. "Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model," Applied Economics, Taylor & Francis Journals, vol. 45(14), pages 1897-1902, May.
    13. Hensher,David A. & Rose,John M. & Greene,William H., 2015. "Applied Choice Analysis," Cambridge Books, Cambridge University Press, number 9781107465923, January.
    14. Talebian, Ahmadreza & Mishra, Sabyasachee, 2022. "Unfolding the state of the adoption of connected autonomous trucks by the commercial fleet owner industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    15. Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
    16. Loeb, Benjamin & Kockelman, Kara M., 2019. "Fleet performance and cost evaluation of a shared autonomous electric vehicle (SAEV) fleet: A case study for Austin, Texas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 374-385.
    17. Rico Krueger & Taha H. Rashidi & Vinayak V. Dixit, 2019. "Autonomous Driving and Residential Location Preferences: Evidence from a Stated Choice Survey," Papers 1905.11486, arXiv.org, revised Sep 2019.
    18. Wadud, Zia, 2017. "Fully automated vehicles: A cost of ownership analysis to inform early adoption," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 163-176.
    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. Talebian, Ahmadreza & Mishra, Sabyasachee, 2022. "Unfolding the state of the adoption of connected autonomous trucks by the commercial fleet owner industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    2. Waltermann, Juliana & Henkel, Sven, 2025. "The human element in autonomous driving: Motivations, expectations, and behavioral change," Technological Forecasting and Social Change, Elsevier, vol. 213(C).
    3. Chen, Tiantian & Fu, Xiaowen & Hensher, David A. & Li, Zhi-Chun & Sze, N.N., 2022. "The effect of online meeting and health screening on business travel: A stated preference case study in Hong Kong," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    4. Gu, Yingfan & Wang, Song & Li, Zhixia & Zhang, Guohui & Ai, Chengbo & Li, Pengfei, 2025. "Why to buy or why not to buy? - Revealing the inherent mechanism and the psychological impacts on the behavior of purchasing automated vehicles," Research in Transportation Economics, Elsevier, vol. 109(C).
    5. Dubey, Subodh & Sharma, Ishant & Mishra, Sabyasachee & Cats, Oded & Bansal, Prateek, 2022. "A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 63-95.
    6. Saeed, Tariq Usman & Burris, Mark W. & Labi, Samuel & Sinha, Kumares C., 2020. "An empirical discourse on forecasting the use of autonomous vehicles using consumers’ preferences," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    7. Manivasakan, Hesavar & Kalra, Riddhi & O'Hern, Steve & Fang, Yihai & Xi, 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: Policy and Practice, Elsevier, vol. 152(C), pages 36-53.
    8. Chen, Tiantian & Fu, Xiaowen & Hensher, David A. & Li, Zhi-Chun & Sze, N.N., 2024. "Effects of proactive and reactive health control measures on public transport preferences of passengers – A stated preference study during the COVID-19 pandemic," Transport Policy, Elsevier, vol. 146(C), pages 175-192.
    9. Kassens-Noor, Eva & Dake, Dana & Decaminada, Travis & Kotval-K, Zeenat & Qu, Teresa & Wilson, Mark & Pentland, Brian, 2020. "Sociomobility of the 21st century: Autonomous vehicles, planning, and the future city," Transport Policy, Elsevier, vol. 99(C), pages 329-335.
    10. Luo, Qi & Saigal, Romesh & Chen, Zhibin & Yin, Yafeng, 2019. "Accelerating the adoption of automated vehicles by subsidies: A dynamic games approach," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 226-243.
    11. Wu, Jingwen & Liao, Hua & Wang, Jin-Wei, 2020. "Analysis of consumer attitudes towards autonomous, connected, and electric vehicles: A survey in China," Research in Transportation Economics, Elsevier, vol. 80(C).
    12. Sharma, Ishant & Mishra, Sabyasachee, 2022. "Quantifying the consumer’s dependence on different information sources on acceptance of autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 179-203.
    13. Elvik, Rune, 2020. "The demand for automated vehicles: A synthesis of willingness-to-pay surveys," Economics of Transportation, Elsevier, vol. 23(C).
    14. Zhou, Heng & Norman, Richard & Xia, Jianhong(Cecilia) & Hughes, Brett & Kelobonye, Keone & Nikolova, Gabi & Falkmer, Torbjorn, 2020. "Analysing travel mode and airline choice using latent class modelling: A case study in Western Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 187-205.
    15. Alessandro La Delfa & Zheng Han, 2025. "Sustainable Mobility and Shared Autonomous Vehicles: A Systematic Literature Review of Travel Behavior Impacts," Sustainability, MDPI, vol. 17(7), pages 1-39, March.
    16. Péter Czine & Péter Balogh & Zsanett Blága & Zoltán Szabó & Réka Szekeres & Stephane Hess & Béla Juhász, 2024. "Is It Sufficient to Select the Optimal Class Number Based Only on Information Criteria in Fixed- and Random-Parameter Latent Class Discrete Choice Modeling Approaches?," Econometrics, MDPI, vol. 12(3), pages 1-16, August.
    17. Gurumurthy, Krishna Murthy & Kockelman, Kara M., 2021. "Impacts of shared automated vehicles on airport access and operations, with opportunities for revenue recovery: Case Study of Austin, Texas," Research in Transportation Economics, Elsevier, vol. 90(C).
    18. Martínez-Pardo, Ana & Orro, Alfonso & Garcia-Alonso, Lorena, 2020. "Analysis of port choice: A methodological proposal adjusted with public data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 178-193.
    19. Wang, Xianing & Lu, Linjun & Zhang, Zhan & Wang, Ying & Li, Haoming, 2025. "Introducing the vehicle-infrastructure cooperative control system by quantifying the benefits for the scenario of signalized intersections," Transportation Research Part A: Policy and Practice, Elsevier, vol. 192(C).
    20. David Hensher & Andrew Collins & William Greene, 2013. "Accounting for attribute non-attendance and common-metric aggregation in a probabilistic decision process mixed multinomial logit model: a warning on potential confounding," Transportation, Springer, vol. 40(5), pages 1003-1020, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:trapol:v:170:y:2025:i:c:p:110-119. 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.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    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.