1. Introduction
Advancement and expansion in the field of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs) are burgeoning quickly, with the intense competition among motor companies to capture market share. The Navigant Research Leaderboard set ten criteria (e.g., vision, technology, marketing, and others) for evaluating which manufacturers are better positioned in the sector of automated driving [
1,
2]. Considering the increasing role of AVs and SAVs as future travel modes worldwide, increased legislation is being reviewed and implemented in various countries and regions [
3,
4]. In addition to AVs, SAVs will also likely emerge as an on-demand travel service, being used as taxis or for carsharing [
5]. Consequently, growth in the AV and SAV sectors opens a wide field of research and development both in industrial and academic contexts.
Since AVs are new travel modes with no human intervention [
6], users of AVs can utilize their travel time more effectively by executing other activities such as reading, working, or even sleeping instead of driving [
6,
7]. Moreover, AV sensors can assess the environment and traffic conditions, thus providing comfort and safety for users [
8]. AVs and SAVs are expected to have substantial benefits, particularly in regard to improving energy consumption, reducing environmental impacts, and increasing accessibility [
9,
10]. However, the impact of AVs and SAVs on road network congestion is unclear and may lead to serious problems [
11,
12,
13].
On the one hand, the tighter headways between AVs and optimal utilization of intersections will increase traffic throughput [
7,
14,
15]. On the other hand, AVs and SAVs will most likely increase the number of trips and traveled miles on roads due to their improved accessibility. In particular, their use by those who cannot drive because of age or disability will increase the number of cars on the road and aggravate congestion [
10,
16].
Road pricing (RP) is regarded by many transport professionals, economists, and traffic engineers as a successful measure in mitigating traffic-related problems such as congestion and reducing carbon emissions [
17,
18,
19]. Moreover, RP is being adopted by several cities worldwide such as Singapore, Oslo, London, Stockholm, and Milan [
20]. Therefore, the use of RP as a travel demand management tool can also play an important role in tackling the expected upsurge in congestion associated with the emergence of AVs and SAVs.
Despite the expected improvement of traffic-related problems through the application of RP, there is also public resentment towards such schemes, as drivers do not want to pay for the use of roads that were previously free [
21,
22,
23]. Low public acceptance of implementing an RP scheme hinders its introduction [
24]. For example, authorities in Auckland, Copenhagen, the Netherlands, and Edinburgh failed to implement RP schemes due to public rejection [
25,
26,
27]. However, case studies from Stockholm and Milan showed that public acceptance of RP schemes could be enhanced if they are properly introduced and include measures such as using revenues for improving public transport (PuT) services [
28,
29,
30].
Adoption of new travel technologies such as AVs and SAVs, and the successful implementation of RP, require initial public acceptance. Therefore, since the late 1980s, numerous studies have investigated the acceptability of RP and generated a vast amount of literature. Likewise, many researchers and consultancy companies have developed questionnaires to investigate public perceptions of the advantages and disadvantages of AVs and SAVs [
31]. However, to the best of the authors’ knowledge, there is no study yet that has investigated RP acceptability in connection with the adoption of AVs and SAVs and the factors influencing them. Therefore, addressing this gap was the primary research aim of the authors of this study. Considering AVs and SAVs will likely be operating on the streets in the future, RP will be a suitable measure to manage the travel demand, but it still requires public acceptance.
In this research, a questionnaire was developed and distributed to residents of Brazil, Jordan, Ukraine, and Hungary. Six hundred and fifty-seven valid responses were received. The questionnaire included various latent variables derived from previous well-known models to explore public preferences relating to RP, AVs, and SAVs. An analysis of the received data using different econometric models provided insight into the public perception of RP, AVs, and SAVs. It also sheds light on the relationship between the survey’s latent variables and public perception.
A review of previous research studies on the acceptability of RP and the adoption of AVs and SAVs indicates that this study is distinctive with respect to simultaneously studying the relationships among RP acceptability, preference for future cars, latent variables, and socio-demographic characteristics. Therefore, this research opens new avenues for understanding the factors influencing RP acceptability for addressing traffic congestion issues foreseen as a result of the increased accessibility of self-driving cars and an increase in their presence due to their various benefits. Here, it is worth mentioning the recent study by Shatanawi et al., that discussed RP adaptation to future cars, in which the authors deployed a stated preference experiment including different attributes (e.g., travel time and travel cost). However, the research results were limited to the impact of socio-demographic characteristics on both RP acceptability and future car choice [
32].
Thus, the contribution of this paper to the literature on RP, AVs, and SAVs is twofold: firstly, it investigates the relationship among RP acceptability, future car choice (AV or SAV), and the studied latent variables. Secondly, it explores the cognitive determinants of RP acceptability and future car choice. The collected data were analyzed using various econometric models, including a factor analysis, multiple linear regression (MLR), multinomial logit model (MNL), and descriptive statistics.
The economic level of a given country has been shown to play a role in influencing the adoption of automated vehicles through GDP per capita [
33]. Therefore, the countries selected in this research illustrate its breadth by analyzing the research impacts in countries from four different regions: Hungary (Central Europe), Brazil (South America), Ukraine (Eastern Europe), and Jordan (Middle East). These countries also represent different economic conditions: Jordan and Brazil have developing economies, Ukraine has an economy in transition, and Hungary has a developed economy [
34]. However, little research has been carried out in these countries with reference to RP, AVs, and SAVs. In light of this, participants belonging to different demographics, cultures, languages, and exposures (in terms of transportation systems, economic conditions, environmental conditions, and other factors) are involved in this research, highlighting its broad scope.
Table 1 summarizes a few characteristics of the four studied countries to provide an overview. The data for the Area, Population, Density, and GDP are based on the year 2020, while the data for Vehicles in Use/1000 People, Passenger Vehicles Annual Sales, Roadway Density (Km/100 Km
2), and Rail Network Length (Km) are based on the data of the years 2015, 2016, 2018, and 2019, respectively.
This paper is structured as follows:
Section 2 provides a review of the previous research relevant to RP, AVs, and SAVs.
Section 3 presents the theoretical background of the latent variables used to investigate the acceptability of RP, AVs, and SAVs.
Section 4 elaborates on the survey design and presents the survey instruments along with the analytical framework of the research. The results of this study are provided in
Section 5 and discussed in
Section 6. Finally,
Section 7 highlights the conclusions of this research and provides insights into policy implications as well as the limitations of the study.
3. Theoretical Background
This section investigates the latent variables which may affect the acceptability of RP, AVs, and SAVs. Many of them were drawn from a previously developed heuristic model [
24,
45,
93,
94] and are based on the theory of reasoned action and planned behavior [
95,
96]. Ajzen’s “Theory of planned behavior” aimed to anticipate the behavior of people in life’s different aspects and believed that most patterns of social behavior are consciously controlled. On the other hand, Fishbein and Ajzen’s “Theory of reasoned action” focuses on the relationship between behaviors and attitudes with regard to actions that determine a person’s behavior [
46]. The latent variables were derived from the before-mentioned model due to its coherent methodology, clear concept of definitions, detailed research framework, and the acceptance of the method by the scientific community as reflected in its use in various publications [
46,
47,
48]. The term acceptability refers to a potential decision regarding a hypothetical measure that would be presented in the course of time [
45]. The following factors and their expected impact on the acceptability of RP, AVs, and SAVs are described below.
People have lower levels of information about pricing measures like RP compared to other demand management measures like “improving PuT”. Lack of knowledge about RP results in a lower acceptability level [
94]. The hypothesis is that those with more information about the RP scheme will be more receptive towards its implementation due to a higher awareness of its benefits and effectiveness. A similar concept applies to the adoption levels of AVs and SAVs.
Perceived effectiveness represents the extent to which the policy objectives are achieved. For example, if the RP scheme is implemented in a city to achieve specific aims such as improving air quality, higher expectations of achieving the goals will result in higher acceptability of the scheme [
45]. This implies a positive relationship between the acceptability of a measure and its perceived effectiveness [
24,
46,
97]. However, people may justify their refusal of a coercive measure or policy by evaluating it as ineffective in the context of a strategic response [
41]. This research makes a distinction between perceived effectiveness and personal effectiveness. The latter represents a change in travel behavior due to the application of the RP scheme; reducing the number of trips using personal cars after RP implementation is an example of personal effectiveness [
41,
56].
Social norm is a social factor that refers to the “perceived social pressure” to comply with certain behavior, where social pressure is defined as the perceptions, beliefs, and judgments of other households and community members. Both attitudes and social norms are grounded in the belief systems of an individual [
98,
99]. For instance, if close relations such as family or friends favor implementing a specific policy measure, this will create a positive social influence on the person to accept the same measure. Hence a policy or measure has a higher probability of being accepted if the social environment accepts it [
22,
24].
People who understand the implications of traffic-related problems are more open to accepting measures or policies that intend to mitigate their adverse effects [
45]. However, according to empirical findings, this approach is not fully confirmed and needs to be ascertained. For example, stakeholders in Spain refused an RP measure despite perceiving traffic related issues as serious problems [
100]. Rienstra et al., on the other hand, found a relationship between the acceptability of policy measures and problem perception, with the public supporting policy measures that improve safety, the environment, or reduce congestion [
41]. The same concept can be applied to AVs and SAVs, as they are expected to reduce the impact of traffic-related problems [
10,
83].
One of the main reasons for the low acceptability of RP schemes is that people consider them to be unfair. Hence, perceived equity is one of the essential requirements for a scheme’s acceptability [
101]. The “intrapersonal” component of perceived equity concerns the respondents’ personal cost–benefit ratio before and after applying the policy measure [
24]. In our study, equity refers to perceived intrapersonal equity and this component is considered for analysis.
Similar to equity, perceiving an RP scheme as fair is also a prerequisite for its successful implementation. This can be achieved by the appropriate utilization of expected revenues, which is proven to be an essential factor for the acceptability of RP schemes [
44,
101]. The fairness of an RP scheme can be assessed through perceived optimal revenue usage, the level of trust in their government, and the perception of other elements of fairness (e.g., RP should be implemented for all vehicles without exemptions; RP should vary according to the congestion level).
The items used to investigate the respondents’ travel behavior and attitudes are drawn from Haustein [
102]. Mobility-related attitudes are measured using a five-point Likert scale. Travel behavior and attitudes of respondents are measured to evaluate how respondents interpret a certain behavior (e.g., cycling, walking, and using PuT).
A number of studies have explored public opinion concerning new travel technologies and have shown that respondents regard safety as the paramount advantage and essential factor for adopting AVs and SAVs [
65,
103]. Some of the studies revealed concerns about the safety of AVs, such as a vehicle’s computer system being hacked or a vehicle’s system failure [
73,
104,
105]. Other issues include legal liability, traveler privacy, and interactions with CC.
Schade and Schlag argued that socio-demographic characteristics might influence the acceptability of RP [
45]. For instance, higher income groups should be more interested in the implementation of RP than lower income groups [
41]. Moreover, other researchers found a relationship between socio-demographic characteristics and variables which might affect acceptability. For example, Wang et al. concluded that “gender, age, and education level have a significant effect on the perceived uncertainty about effectiveness and fairness of congestion charging” [
106]. Conversely, the direct impacts of personal features on the acceptability of RP were found to be rather low in some studies [
22,
41]. On the other hand, other studies have found a relationship between age and adoption level of AVs and SAVs (e.g., younger travelers are more likely to use SAVs) [
5,
68].
5. Results
This section presents and examines the effects of the investigated factors on RP acceptability using MLR. In addition, the results of AV and SAV adoption using MNL models are analyzed to understand individual preferences toward AVs and SAVs based on the investigated factors.
5.1. RP Acceptability
The effects of the investigated factors and socio-demographic characteristics on RP acceptability using MLR in Hungary, Jordan, Ukraine, and Brazil are presented in
Table 6, which includes the factors’ estimated parameters, level of significance, and model fit. The socio-demographic characteristics are income and age. Overall, 20 parameters for each country were estimated for the RP acceptability model. The intercept significantly differs from zero in four models. The Hungarian model intercept is positive, indicating that Hungarian respondents would accept applying RP regardless of the effects of other factors, while a negative tendency is associated with respondents from other countries. While many of the estimated parameters vary across the countries, some of them were found to share a common sign.
Unsurprisingly, the “Environmental_Oriented_Users” factor has a significant positive effect on RP acceptability. This indicates that respondents who consider their environmental impact while planning their trips (e.g., using less polluting vehicles) are more likely to accept the implementation of RP. Similarly, in all models, “Social_Norm” is statistically significant and has a remarkable positive effect on RP acceptability; this implies that the respondents from the four countries can be positively influenced by their family and friends to accept the application of RP. The explained variance differed across countries of interest. Models of Hungary and Ukraine explained about 54.6% and 54.9% of the variation in RP acceptability, respectively, whereas the Brazilian model explained 31.4%. The least explanatory model was Jordan, with only 16.4%.
5.2. AV and SAV Adoption
The estimated parameters of the MNL models are presented in this section. Two models were generated for each country to assess the respondents’ behavior regarding the adoption of AVs and SAVs. Model 1 describes the vehicle choice as a function of 19 factors derived from the original survey’s questions using a factor analysis. Model 2 enumerates the estimation results for the MNL model, such that both Model 1 variables and individual-related variables were involved in this model. Model 2 was generated to determine the impact of the investigated factors in Model 1 plus the inclusion of socio-demographic characteristics and driving habit variables, including “Age”, “Income”, “Gender”, “Education”, “Employment”, “Driving license”, “Car ownership”, “Access to car as driver”, and “Access to car as passenger”.
Table 7,
Table 8,
Table 9 and
Table 10 display the parameters of the two models for each country separately. The parameters of AVs and SAVs are relative to the reference mode CC. The models representing each country’s data are determined based on statistical tests like the Akaike information criterion (AIC), Bayesian information criterion (BIC), and McFadden R
2.
The alternative specific constant (ASC) represents the mean of all unobserved resources of the utility. It could be noticed that ASCAV and ASCSAV are significantly different from zero in the MNL models. Based on the Hungarian responses, both constants have a positive sign; however, there is a preference for choosing AV over the other two modes. Regarding the Jordanian model, both ASCAV and ASCSAV are significant and positively affect the utility functions of these modes. Unlike Hungarian respondents, Jordanian respondents prefer SAVs over AVs and CCs. In terms of the Ukrainian responses, ASCSAV is associated with a negative sign, which increases the disutility of the SAV mode. Thus, based on the Ukrainian respondents’ viewpoint, CCs are still the preferred mode compared to SAVs. The responses from the Brazilian model show that ASCSAV is significant and positively affects the utility function, and SAVs are considered the most preferred mode.
7. Conclusions
In conclusion, this study investigates the effect of attitudinal factors and socio-demographic characteristics on RP acceptability and AV and SAV adoption. The results of the current study show that respondents from different countries have different behaviors regarding RP acceptability and AV and SAV adoption. Such findings are in line with previous research by Fürst and Dieplinger, who replicated the AFFORD study [
45] in Vienna to investigate the factors that affect the acceptability of RP. They concluded that “both studies differ in terms of influencing factors” [
46]. In terms of RP acceptability, according to the responses obtained, all investigated factors have a significant effect on RP acceptability in most or all of the studied countries. An exception can be seen in the factor “AV_Perceived_Ease_of_Use” which is significant only in the Hungarian model. Socio-demographic characteristics do not show a strong significant relationship with RP acceptability. Among the factors used, it was found that the following factors, “Environmental_Oriented_Users”, “Negative_Expectations_RP”, and “Social_Norm” are statistically significant and positively affect RP acceptability in all countries from the respondents’ perspective. Hungarian respondents would accept applying an RP scheme regardless of the effect of other factors, while a negative tendency is found for respondents from other countries.
Regarding AV and SAV adoption, the examined factors have a significant effect on the respondent’s adoption of AVs and SAVs in all or most of the countries. The “Environmental_Oriented_Users” factor is positively significant in all countries except Ukraine. Similarly, respondents willing to share their trips with others due to the application of RP are more likely to use AVs and SAVs. It can also be seen that respondents with a high level of education are more likely to adopt AVs and SAVs. The results do not demonstrate a strong relationship between age and the tendency to use any of the presented alternative transportation modes. Furthermore, the results show that respondents do not trust their government to use the revenues from road tolls to support the state’s budget, and they prefer more clear and transparent approaches to using RP revenues.
Finally, the results show that the examined factors influence the acceptability of RP and the adoption of AVs and SAVs, demonstrating the interrelationship between them and the importance of their simultaneous study. For instance, people who enjoy driving are less willing to opt for AV, while those willing to share their trips are more likely to choose SAV. Family and friends can positively influence people to accept RP schemes, and people who consider the environment are more likely to accept RP and choose AVs and SAVs [
29,
112]. People in the four countries lack confidence in their governments to use the RP revenues appropriately. They require the allocation of RP revenues be utilized explicitly and for particular uses, such as improving PuT.
7.1. Insights for Policy Implication
Results of this research can provide meaningful insights to stakeholders and policymakers for anticipating and planning policy controls related to the adoption of AVs and SAVs and RP acceptability in the transportation sector. It is difficult to generalize the policy implications derived from this research due to the small sample size; however, some factors in the analyzed models have almost the same effect on RP acceptability and AV and SAV adoption across the investigated countries. We shed light on the effect of these factors in this section.
The results demonstrate that respondents’ awareness of new technologies and RP is an important factor in their adoption and implementation. Therefore, educational campaigns through different platforms and various methods should be held to inform people about the expected benefits of driverless vehicles and RP, which will help raise their acceptability. Similarly, the “Environmental_Oriented_Users” factor positively affects RP acceptability. Gaining the support of this group by spreading the word about the environmental benefits of implementing RP schemes will facilitate the authorities’ task in applying such changes in their countries. Additionally, respondents want their governments to use RP revenues in areas where the residents can feel their impact, such as enhancing PuT systems. Such policies are critical as the respondent’s trust is very low in government entities regarding the use of revenues. Therefore, it is advised to clearly explain the methods of utilizing the revenues from RP to satisfy the public’s requirements so that authorities can reduce the trust gap with their citizens. Furthermore, RP schemes that provide benefits to citizens are seen as more acceptable. Policy makers should thus consider giving special attention to these aspects.
It is evident from the results that respondents who are willing to share their trips with others are more open to accepting RP and using AVs and SAVs. This point can be utilized by promoting and allowing ride sharing services to operate freely; as the number of users of these services increases, the acceptability of RP, AVs, and SAVs is likely to increase. Respondents with safety and security concerns about AVs and SAVs are reluctant to use them. This information can be used to devise policies to promote the safety and security features of AVs and SAVs and reduce the anxiety of using these new travel modes. Most likely, the public will initially resent the implementation of RP, AVs, and SAVs; the results of this research shed light on the potential reasons for such rejection (e.g., safety and security concerns) and the positive influencing factors (e.g., environmentally oriented users). Consequently, public agencies can further elaborate on these insights to motivate the public to be in favor of RP, AVs, and SAVs. Moreover, this research shows that neither RP acceptability nor AVs and SAVs adoption can be generalized over a large population; therefore, city-specific policies will be necessary to efficiently shift the transportation mode from CCs to AVs and SAVs and, similarly, to increase the acceptability of RP.
7.2. Limitations and Directions for Future Research
This study paves the way to incorporate two research topics, RP acceptability and the adoption of AVs and SAVs, into a single study. However, the research faces a set of limitations. The first limitation can be identified in the sample size, which is relatively small compared to the total population of the selected countries. This is likely a consequence of the utilization of online questionnaires, which favor youth and individuals who have access to the internet. Although the online questionnaire provided a video to establish a unified perception of RP, AVs, and SAVs, it is still doubtful whether all the respondents have come to the same conclusion after watching the short video. Therefore, the use of the results in the context of the larger population groups should be carried out critically. Directions for further research are also possible and highly recommended. There are several approaches in regard to future research. Researchers can include new variables (e.g., cost, maintenance cost, technology maturity, AVs, SAVs perceived safety, AVs, SAVs legal liability, or AVs, SAVs perceived comfort). Moreover, they may conduct research, relying on a large sample size, using a prototype of AVs and SAVs alongside RP. This allows them to gain more useful insights into how the presence of RP and the inclusion of AV and SAV experience is likely to influence potential users to accept the concept of RP, AVs, and SAVs. In addition, future research may use a stated preference experiment to explore the effect of RP attributes (e.g., toll value) on AV and SAV adoption, which can give more insight into how different RP tolls could affect vehicle adoption.