Acceptability of Automated Vehicles in Portugal: Profiling Prospective Users
- Open Access
- 2026
- OriginalPaper
- Buchkapitel
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Abstract
1 Introduction
Automated vehicles (AVs) promise to radically change mobility of people and goods, improving road safety and traffic management, supporting climate actions, and promoting benefits for individuals and society as a whole [1]. Nevertheless, those benefits must be perceived by potential users, and must be bigger than the perceived risks. In other words, the success of the technology depends on societal acceptability [2, 3]. Moreover, AVs implementation requires considerable legal and business adaptation. For these reasons, research must focus on the evaluation of acceptability from the perspective of different users, in different contexts [4].
One practical application of AV technology is the idea behind truck platooning, where trucks resort to vehicle-to-vehicle (V2V) communications to travel closely in convoy. The deployment of such technology demands understanding the representations of truck drivers, logistics companies, road operators and regulators about the technology [5]. In a first approach, and to access the acceptability of AVs, not limited to freight transport, this study aims at unveiling the perceptions about AVs, in general, and investigates what acceptability profiles emerge from these perceptions.
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Using the Portuguese context as a support of our research, we have developed a comprehensive questionnaire. Data collected from the questionnaire was then treated and analyzed using statistical tools, from which resulted three users’ clusters. After presenting materials and methods, we characterize the three profiles in the results section, together with some concluding remarks.
2 Materials and Methods
Our study comprised the development and application of an online questionnaire for data collection, followed by a statistical analysis of the questionnaire results to identify and characterize AVs acceptability profiles and the determinant factors of AVs acceptability.
The questionnaire was composed by more than 70 items to explore prospective users’ representations about AVs: benefits and expectations (e.g., the possibility to perform secondary tasks during automated driving), risks and concerns (e.g., system limits, system failures, loss of control over the driving activity), previous experience with automated driving technology, and preferred use cases for AVs. Some of the questions addressed sociodemographic characteristics (gender, age, education, job, income, location), self-perception on the use of technology, and mobility habits. The preferred use cases aimed at assessing situations where respondents were more prone of using AVs, but can serve as an estimation for the adoption. The considered use cases were: intention to use a full automated vehicle, stated preference of a full automated vehicle over a non-automated vehicle, being comfortable with using a full automated vehicle for family trips, and intention to use and automated bus.
After data treatment process, the first analysis performed aimed at identifying different identify different Portuguese population clusters. At this stage, we performed a cluster analysis using the k-means algorithm to group the respondents according to their driving and mobility habits, knowledge/previous experience about AVs, and perceived risks and benefits, measured using a 5-point Likert scale. After, we used chi-square tests to characterize cluster membership based on their characterization regarding demographics, self-perception on the use of technology. Lastly, we compared the acceptability profiles defined, in the first stage, by the cluster analysis against the acceptability levels measured for the different use cases, using ANOVA post-hoc tests.
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In total, 501 respondents (51% male, 49% female) aged between 18 and 81 years participated in the survey (Table 1). The participants were well distributed in terms of education levels, with all levels of education (high school, bachelor’s degree, master, PhD) having between 20 and 30% representation in the sample. More than two thirds (70%) live in rural areas and are employed. Concerning participants’ self-perception towards the use of technology, 59% prefer to wait a while and be aware of other’s opinions, 36% are early adopters, and 6% stated they are not prone to technological changes.
Table 1.
Sample characterization.
Variable | % | |
|---|---|---|
Gender | Female | 50,7% |
Male | 49,3% | |
Age | [18–35[ yo | 37,8% |
[35–50[ yo | 36,0% | |
[50–99] yo | 26,2% | |
Education | Basic education | 1,6% |
High school | 20,0% | |
Bachelor's Degree | 28,9% | |
Master's degree | 25,7% | |
PhD | 23,8% | |
Occupation | Employed (salaried workers) | 68,5% |
Self-employed | 6,8% | |
Unemployed | 1,4% | |
Retired | 3,4% | |
Student | 20,0% | |
Income | Up to 2 national minimum wages | 34,3% |
Between 3 to 4 national minimum wages | 42,1% | |
5 or more national minimum wages | 23,6% | |
Residential area | Predominantly urban area | 70,3% |
Medium urban area | 22,0% | |
Predominantly rural area | 7,8% | |
Use of technologies | Willing to use new technologies as soon as they are available | 35,5% |
Prefer to wait a while and be aware of other people's opinions | 58,7% | |
Not very adept at technological change | 5,8% |
3 Results
The clusters analysis performed resulted in three clusters of prospective users: objectors, ambivalents, and enthusiasts. Naturally, the enthusiasts reported self-perception fits the behavior of early adopters, while ambivalents are late adopters, and objectors do not support huge technological advances.
The objectors stated that they enjoy driving more than the other groups and showed higher concerns with the safety and reliability of the technology (system limits and system failures), as well as with data privacy. On the other side, the enthusiasts highlight possibility of performing non-driving tasks while traveling as a benefit of AVs, and they also believe that automated driving technology can improve road safety and reduce emissions. Ambivalents, on their turn, are concerned about road safety, but at the same time perceive benefits in performing non-driving tasks. This type of contradiction suggests that this group needs more information about the true impacts of AVs.
In relation to the sociodemographic characteristics, income, education, place of residence, and self-perception about the adoption of new technologies reflected the main differences between clusters. Objectors are concentrated in rural areas, and have lower incomes, while enthusiasts, referring to early adopters, are concentrated in urban areas, have higher education levels (masters’ degree and PhD) and higher income. Gender, age, and professional situation (student, employed, unemployed, or retired) did not seem relevant for the acceptability profiles.
4 Discussion and Conclusions
The sociodemographic characteristics of the identified profiles suggest that AVs acceptability follow the patterns of other technologies, with three well-distinguished groups from technology averse to technology, late adopters, and early adopters. This study also presents the characteristics of these groups concerning their representations about AVs in terms of road safety, travel-time savings, reduction of gas emissions, possibility of performing non-driving tasks, technology limits and reliability. Considering participants preferences about AVs, the three clusters were characterized as objectors, ambivalents, and enthusiasts. Accordingly, participants reported intention to use is lower in the objectors group and higher in the enthusiasts group, in all use cases assessed in this study.
The questionnaire results contribute to a better understanding, not only of the acceptability profiles, but also about potential adoption of AVs. These results can be later used in the design of guidelines to increase AVs acceptability, target at objectors and ambivalents of important contributions. Namely, policymakers should focus on the reducing the risks perceived by those two groups, and remove any doubts regarding the perceived benefits. The design of the guidelines, for policymakers and manufacturers are possible future research. Still, narrowing our research by focusing on specific vehicles such as heavy vehicles for freight transport can also contribute to assess the acceptability of truck platooning, by professional drivers and the impacts for the driver profession.
Acknowledgements
This work is financially supported by national funds through the FCT/MCTES (PIDDAC), under the project with DOI 10. 54499/PTDC/ECI-TRA/4672/2020 and the grant with DOI 10. 54499/CEECINST/00010/2021/CP1770/CT0003.
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