Unfolding the state of the adoption of connected autonomous trucks by the commercial fleet owner industry

Ahmadreza Talebian and Sabyasachee Mishra

Transportation Research Part E: Logistics and Transportation Review, 2022, vol. 158, issue C

Abstract: This paper attempts to address two particular questions about the adoption of connected autonomous trucks (CATs) by trucking companies: (i) what are the factors affecting the decisions to adopt different levels of autonomous trucks? and (ii) how many with what sizes are the groups of CAT adopters? We employ choice modeling and latent-class cluster analysis (LCCA) to address the two questions. US companies working in the freight industry are contacted and 400 full responses are collected. The data is analyzed descriptively and detailed results of modeling efforts are presented and discussed. Focusing on the first question, companies view automation Level 2 not significantly different than Regular trucks. We observe that small-sized companies are more likely to adopt the higher levels of automation, and large companies may be willing to adopt only when the technology become more affordable. Cargo type is found to have some impact on the adoption: for example, companies carrying foodstuff are more likely to adopt higher levels of automation. Having promoters of new technologies in the company increases the likelihood of adoption and the impact is more visible for the higher levels of automation. Turning to the second question, our results indicate that there could be five categories of CAT adopters which is consistent with what the Theory of diffusion of Innovations (DOI) suggests. However, the sizes of the Innovators and Early Majority classes would respectively be four and two times of DOI’s general suggestion. Overall, it is speculated that the CAT adopter distribution may not be a bell-shaped curve but more of a right-skewed figure. This can be contributed to explicit financial benefits of CATs which could incentivize companies to adopt earlier.

Keywords: Autonomous deriving; Adoption; Diffusion of innovations; Latent class cluster analysis; Discrete choice modeling; Trucks (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.tre.2022.102616

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