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Examining human attitudes toward shared mobility options and autonomous vehicles
Abstract
This paper presents a comprehensive analysis of people’s attitudes toward shared mobility options and autonomous vehicles (AVs), with a focus on the underlying patterns and potential determinants. A stated preference (SP) survey was designed and implemented in the U.S. Four sets of questions were included in the questionnaire, each focused on one unique aspect of user attitudes, including a) preferences for mobility options and lifestyle (such as overall view of driving, factors in mode choice decisions and technology engagement), b) perceived benefits and concerns of shared mobility option, c) reasons toward or against private vehicle ownership, and d) motivations for and desired features of AVs. A structural equations model was developed to identify latent attitudinal factors and examine the correlations between the latent attitudes (as the endogenous variables) and the observed covariates (including the socio-economic and demographic characteristics, and users’ current mobility profile, such as mode use frequency, travel distance, and trip fare). The model identified eleven latent factors that represent various aspects of attitudes toward AVs and shared mobility options. The findings could be used by policymakers and Transportation Network Companies (TNCs) to a) recognize the users’ latent attitudes, b) understand the underlying patterns of attitudes, c) implement plans and policies more efficiently, d) guide or influence users’ perceptions, and e) enhance travel behavior models. This study lays the foundation for further analysis on understanding user acceptance and adoption of these emerging mobility options, which is essential to estimate the likelihood and magnitude of behavior shifts in the era of automated, connected and shared mobility.
... factors that influence people's intentions to purchase and use AVs. The following research questions frame this study: 1) What are the perceptions, opinions, and expectations of people about AVs (Bansal and Kockelman 2018;Rahimi et al. 2020;Schoettle and Sivak 2014b)? 2) How would people's socioeconomic and demographic characteristics influence BI to purchase AVs for their travel purposes (Nazari et al. 2018;Nordhoff et al. 2020;Rahimi et al. 2020)? 3) How would awareness of AVs, and perception of their convenience, comfort, and safety influence people's BI to purchase and use AVs (Bansal et al. 2016;Kapser and Abdelrahman 2020;Nordhoff et al. 2020)? ...
... The following research questions frame this study: 1) What are the perceptions, opinions, and expectations of people about AVs (Bansal and Kockelman 2018;Rahimi et al. 2020;Schoettle and Sivak 2014b)? 2) How would people's socioeconomic and demographic characteristics influence BI to purchase AVs for their travel purposes (Nazari et al. 2018;Nordhoff et al. 2020;Rahimi et al. 2020)? 3) How would awareness of AVs, and perception of their convenience, comfort, and safety influence people's BI to purchase and use AVs (Bansal et al. 2016;Kapser and Abdelrahman 2020;Nordhoff et al. 2020)? ...
... Negative (Farzin et al. 2023;Ha et al. 2020;Hulse et al. 2018;Kenesei et al. 2022;Kim and (Haboucha et al. 2017;Nazari et al. 2018;Rahimi et al. 2020;Rahman and Thill 2023b) Urban area/Rural Positive /less likely (Daziano et al. 2017;König and Neumayr 2017;Long and Axsen 2022;Nazari et al. 2018) Transportation factors The Theory of Reasoned Action (TRA) is a widely recognized model in social psychology that aims at explore the core determinants of individual BI towards an action (Ajzen and Fishbein 1980;Madden et al. 1992). According to TRA, BI for a specific action is jointly determined by one's attitude (i.e., positive or negative) towards the behavior and by subjective norms (i.e., the influence of other people on behavioral action). ...
This study aims to investigate people’s perceptions and opinions on Autonomous Vehicles (AVs) and the key factors that influence their Behavioral Intention (BI) to purchase and use AVs. Data were sourced from the 2019 California Vehicle Survey to explore the determinants of AV purchase. A Structural Equation Model (SEM) of stated intentions is estimated to validate a theoretical framework drawn on relevant bodies of literature. The descriptive statistics show that many people are already aware of AVs. Many people also think that traveling by AVs is enjoyable, safe, and effective, although some of them would miss the joy of driving and would not entrust a driverless AV to shuttle their children. Results from the SEM indicate that being working-age adults, having children, household income, per capita income, and educational attainment are attributes positively associated with AV purchase intention. Similarly, psychological factors (e.g., perceived enjoyment, usefulness, and safety), prior knowledge of AVs, and experience with emerging technologies (e.g., electric vehicles) significantly enhance BI to purchase AVs. This study finds that family structure and psychological factors are the most influential factors of AV purchase intention, and more so than the built environment, transportation, and other socioeconomic factors.
... Unattended drop-offs and pick-ups of children and the anticipated increased number of pedestrian traffic crashes may make AV adoption more challenging [58]. Similar to traffic safety concerns, the lack of personal data privacy from hackers (i.e., location tracking, surveillance) poses a major threat to the adoption and use of AVs [24,47,63]. Thus, it is imperative to raise the perception of safety, security, and privacy of people to boost AV adoption. ...
... Researchers also mentioned that people who value fuel economy [12] and greener transportation [47,51] are more interested in adopting and using AVs compared to their counterparts, as AVs are anticipated to reduce energy consumption, pollution, and transport costs. The same can be said of the possibility afforded by AVs to reduce traffic congestion and travel time, thus enabling people's engagement in other activities [48,49,63]. Finally, AVs are touted for providing mobility to disadvantaged people (e.g., elderly, disabled) [48,51] and improved amenities and services [12,63] motivate people to use AVs. ...
... The same can be said of the possibility afforded by AVs to reduce traffic congestion and travel time, thus enabling people's engagement in other activities [48,49,63]. Finally, AVs are touted for providing mobility to disadvantaged people (e.g., elderly, disabled) [48,51] and improved amenities and services [12,63] motivate people to use AVs. ...
This article presents a state-of-the-art literature review to understand people’s perceptions and opinions of Autonomous Vehicles and the factors that influence their adoption. A strategic literature search was conducted to select articles for this review. Most of the articles were published since 2015 and they used a household questionnaire survey to collect data. Mostly, they used statistical and econometric methods to evaluate the factors that affect people’s intentions to adopt Autonomous Vehicles. The results show that psychological factors often appear as the most important internal factors of people’s willingness to adopt Autonomous Vehicles. Additionally, other internal factors such as the socioeconomic profile of individuals and their household, and knowledge and familiarity with Autonomous Vehicle technologies would affect adoption tendencies. User attributes also indirectly affect adoption of Autonomous Vehicles by influencing the psychological factors of users. We identify several critical external factors such as opportunities (e.g., safety and security, low congestion, energy use) and challenges (e.g., system failures, privacy breaches, and legal issues), while another influential group includes transportation factors (e.g., travel mode, distance, and time), urban form (e.g., urban/rural, density, land use diversity), affinity to new technology, and the institutional regulatory environment. We discuss some recommendations for policy makers, auto industries, and private stakeholders to formulate policies and strategies to increase the market share of Autonomous Vehicles. Finally, we identify some limitations of previous studies and provide a blueprint for future research on Autonomous Vehicle adoption.
... Unattended drop-offs and pick-ups of children and the anticipated increased number of pedestrian traffic crashes may make AV adoption more challenging [57]. Similar to traffic safety concerns, the lack of personal data privacy from hackers (i.e., location tracking, surveillance) poses a major threat to adopt and use AVs [24,46,62]. Thus, it is imperative to raise the perception of safety, security, and privacy of people to boost AV adoption. ...
... Researchers also mentioned that people who value fuel economy [12] and greener transportation [46,50] are more interested in adopting and using AVs compared to their counterparts as AVs are anticipated to reduce energy consumption, pollution, and transport costs. The same can be said of the possibility afforded by AVs to reduce traffic congestion and travel time, thus enabling people's engagement in other activities [47,48,62]. Finally, AVs are touted for providing mobility to disadvantaged people (e.g., elderly, disabled) [47,50] and improved amenities and services [12,62] motivate people to use AVs. ...
... The same can be said of the possibility afforded by AVs to reduce traffic congestion and travel time, thus enabling people's engagement in other activities [47,48,62]. Finally, AVs are touted for providing mobility to disadvantaged people (e.g., elderly, disabled) [47,50] and improved amenities and services [12,62] motivate people to use AVs. ...
This article presents a state-of-the-art literature review to understand people’s perceptions and opinions of Autonomous Vehicles (AVs) and the factors that influence AV adoption. A strategic literature search was conducted to select articles for this review. Most of the articles were published within the last five years and they used a household questionnaire survey to collect data. Mostly, they used statistical and econometric methods to evaluate the factors that affect people’s intentions to adopt AVs. The results show that psychological factors often appear as the most important internal factors of people’s willingness to adopt AVs. Additionally, other internal factors such as the socioeconomic profile of individuals and their household, and knowledge and familiarity with AV technologies would affect AV adoption tendencies. User attributes also indirectly affect AV adoption by influencing the psychological factors of users. We identify several critical external factors such as opportunities (e.g., safety and security, low congestion, energy use) and challenges (e.g., system failure, privacy breach, and legal issues), while another influential group includes transportation factors (e.g., travel mode, distance, and time), urban form (e.g., urban/rural, density, land use diversity), affinity to new technology, and the institutional regulatory environment. We discuss some recommendations for policy makers, auto industries, and private stakeholders to formulate policies and strategies to increase market share of AVs. Finally, we identify some limitations of previous studies and provide a blueprint for future research on AV adoption.
... Reviewing type of choice behavior is to inform what types of behavior have been empirically investigated and why. Travel choice behavior is typically classified into three top-level categories with multiple subcategories under each [12,26,28]: (1) long-term impacts on home/work location choice and urban development; (2) mid-term technology adoption and willingness-to-pay (WTP); and (3) daily choice including whether, where, when, and how to travel. Table 1 summarizes the categories of choice behavior and innovative technologies involved in this review. ...
... Reviewing the sources of data is to have a clear picture of what methods have been used for data collection and which offer better quality of data. As demonstrated in Table 2, the majority of the studies to date [10,[12][13][14]20,28,37,38,40,44,46,48,50,[54][55][56][57][58][59][60][61][62] adopted Stated Preferences (SP) to collect people's responses to SAEVs in a completely hypothetical environment (SP without technology savvy), in which participants had little knowledge of mobility innovations. A few studies interviewed participants either by phone [47] or face-to-face [61], collecting nuncupative choice decisions which yet were also made virtually. ...
... Socio-demographics, as indicated in Table 3, is the most recognized factor that manipulates adoption/WTP behavior, especially age, gender, household income, vehicle ownership, and individual education level. The seniors are less likely to embrace new technologies than youth [12,28,37]. Both male and female respondents believe in stress relief and cost-effectiveness as the top benefit of automated ride-sourcing, while they present opposite attitudes in others such as driving assistance and safety and ownership cost [12]. ...
Vehicle sharing, automation, and electrification are anticipated to move mobility and society towards low carbon and sustainability. Yet their positives and negatives to the society and natural environment largely depend on how people response to them which is poorly understood. A systematic review is performed in this study to discuss the modeling approaches that quantify travel behavior and demand implications of those technologies and summarize the pros-and-cons of the state-of-the-art. Evidences suggest that the negative rebound effect of the technologies lack mature discussion and require comprehensive quantification multidimensionally in technology adoption, trip generation, timing, travel distance, mode shift, and urban sprawl. Meanwhile, it is worth addressing the uncertainties of the net impact of the technologies resulted from data quality, population and regional heterogeneity, level of vehicle automation, temporal dynamics, behavior chain reactions, and societal issues like aging, fertility drop, and the pandemic. The findings of this review will enlighten the future endeavors of improving the estimation of mobility demand, energy, and carbon emissions implications of the innovative mobility technologies.
... To determine the variations in attitudes, Rahimi et al. (2020) surveyed individuals across USA and revealed that Hispanics, college degree holders, and frequent transit users were more likely to engage with SAV technology. The authors also found that due to privacy concerns and personal convenience, females were less likely to renounce their private vehicles in favor of SAVs, whereas seniors were more likely to relinquish their vehicles and utilize SAVs (Rahimi et al., 2020). ...
... To determine the variations in attitudes, Rahimi et al. (2020) surveyed individuals across USA and revealed that Hispanics, college degree holders, and frequent transit users were more likely to engage with SAV technology. The authors also found that due to privacy concerns and personal convenience, females were less likely to renounce their private vehicles in favor of SAVs, whereas seniors were more likely to relinquish their vehicles and utilize SAVs (Rahimi et al., 2020). Beyond demographic factors, geographic location is likely to influence SAV acceptance and adoption. ...
The mobility landscape is experiencing major changes due to two emerging transportation trends, autonomous vehicles (AVs) and on-demand transportation, and the convergence of these smart mobility innovations as shared autonomous vehicles (SAVs) can considerably alter travel behavior and consequently the ecological and societal aspects of the transportation sector. On-demand autonomous mobility is a promising transportation mode, but further research is necessary to evaluate its various aspects and implications prior to widespread adoption. Thus, this study investigates the effects of integrating automation and on-demand mobility by analyzing the effects on the environment, public transportation, land use, vehicle ownership, and public acceptance. A comprehensive literature review was performed, and through a detailed review of 210 articles, the impacts of each of these categories were determined and classified according to their causes, and the number of publications with which they were cited in the literature was determined. The review showed that SAVs can either positively or negatively impact categories and have the potential to minimize mobility obstacles and transportation inequity if legislators use technology to develop a better transportation system by initiating effective policies that govern the four impacted areas. A list of 22 policy recommendations designed to avoid the negative consequences of SAVs by maximizing the benefits of the technology while limiting the associated risks was also identified. The findings of this review will be beneficial to AV manufacturers, transportation professionals, and especially policymakers, who play an integral role in shaping how society benefits from SAV technology.
... Using shared rides to limit transportation growth, and thereby limit greenhouse gas emissions from transportation, has been examined in the Swedish context for some time (Åkerman and Höjer, 2006). In terms of CAVs, there have been studies done in other countries of users' potential willingness to share; for example, Lavieri and Bhat (2019) found that users might be more willing to share commuting than leisure trips, and Rahimi et al. (2020) found that attitudes can be influential on the choice to share, sometimes more so than demographic variables. ...
... There are examples in the literature which show that when people envision future transportation systems, they carry forward a vision of how they use the transportation system today, even if they are aware of its drawbacks, such as those of an increase in privately owned vehicles (Fraedrich, 2021). While there are many factors that influence willingness-to-share (Rahimi et al., 2020), ride-sharing is perceived as being both more inconvenient, and in some cases more costly than a private vehicle (Wadud and Chintakayala, 2021;Wadud and Mattioli, 2021). Thus, a shift to ridesharing could represent a broader change in behavior that could be connected to societal drivers, hence being a response that could in fact target the entire DPSIR chain (see Fig. 7). ...
... This study represents one of the early efforts to understand the differences and similarities in 32 terms of the factors affecting the ridesourcing service usage frequency based on their primary 33 purpose of usage. Unlike most previous studies, three groups of users are considered based on their 34 primary trip purpose when using ridesourcing services, including commute users (primarily for 35 home-to-work trips), social/recreational users (primarily for social/recreational trips), and transfer 36 users (primarily for trips to/from airport/train station). ...
... It is possible that these users consider ridesourcing services as the 30 less mentally and physically taxing travel mode compared to often crowded public transportation 31 or driving by themselves in often congested peak-hour traffic of Shanghai. 32 Regarding the short waiting time indicator, it has a random parameter. According to the mean 33 and variance of random parameters submitted to the normal distribution, 61.0% of commute users 34 who chose short waiting time as a reason were more likely to have a higher level of usage frequency, 35 while the rest were less likely. ...
1 This study investigates the influencing factors that impact ridesourcing service usage frequency 2 and explore the potential similarities and differences among groups of population based on their 3 primary usage purposes. A revealed-preference survey developed for this study was conducted 4 among 783 ridesourcing service users from Shanghai, China in September 2020. Separate random 5 parameters ordered probit models were estimated for users with different primary purposes of 6 usage to capture unobserved heterogeneity. The identified influencing factors include travelers' 7 sociodemographic characteristics, reasons to choose ridesourcing services, and other behavioral 8 characteristics. In addition, the impacts of these contributing factors were different based on their 9 primary usage purpose. The model estimation and descriptive statistics findings suggest that groups 10 of ridesourcing service users may respond differently to various types of promotional strategies. 11 The study insights may be used to design future strategies that can potentially improve the service 12 usage frequency of existing users and attract new users. 13 14
... Issues regarding sibling constructs, jingle-jangle-fallacies and theory building may exacerbate the crisis of replication and reputation in the field (de Winter & Nordhoff, 2022;Fried, 2020;Lawson & Robins, 2021;Smaldino, 2017). Not all research supports the notion of a GAF, and there is need for further research to clarify how best to conceptualize these factors (Kacperski et al., 2021;Rahimi et al., 2020). ...
Introduction
Shared automated vehicles (SAVs) could significantly enhance public transport by addressing urban mobility challenges. However, public acceptance of SAVs remains under-studied, particularly regarding how informational factors and individual personality traits influence acceptance.
Methods
This study explores SAV acceptance using data from an experimental survey of 1902 respondents across Norway. Participants were randomly presented with different informational conditions about SAV services, manipulating vehicle autonomy (fully autonomous vs. steward onboard), seating orientation (facing direction of travel vs. facing other passengers), and ethnicity of co-passengers. Personality traits from the Five Factor Model (FFM) and Social Dominance Orientation (SDO) were assessed. The General Acceptance Factor (GAF), derived from the Multi-Level Model of Automated Vehicle Acceptance (MAVA), was used as the primary outcome measure.
Results
No significant main or interaction effects were found from the experimentally altered information conditions. However, personality traits significantly influenced acceptance. Specifically, higher openness and agreeableness positively predicted SAV acceptance, while higher neuroticism and social dominance orientation negatively predicted acceptance.
Discussion
The absence of experimental effects suggests either a limited role of the manipulated factors or insufficiently robust manipulations. Conversely, the substantial impact of personality traits highlights the importance of psychological factors, particularly trust, openness, and social attitudes, in shaping SAV acceptance. These findings emphasize the need for tailored communication strategies to enhance SAV uptake, addressing specific psychological profiles and fostering trust in automation.
... oling intention, and trust in using the carpooling has no impact on the perceived risk (Tsai et al., 2021). The influence of perceived risks while sharing public space like sharing a vehicle with strangers might vary from region to region. Also, cultural and social values and individual attitudes influence people's decision to choose a travel mode (Rahimi et. al., 2020;Saha et al., 2024). Social transformation is important to make the significant modal shift from private cars to shared transport services (Vélez et al., 2023). ...
... Furthermore, COMSM does not commit to pursue a single online interface. It allows the use of diverse digital platforms with more safety and social inclusion and less collaboration requirements than an integrated online interface [96,[111][112][113][114][115]. In summary, COMSM can create compelling value for those pursuing higher efficiency and lower-barrier solutions. . ...
There is an increasing adoption of shared mobility for improving transport systems performance, reducing excessive private vehicle use, and making full utilization of existing infrastructure in urban traveling. Despite numerous studies in exploring the use of shared mobility for sustainable transport from different perspectives, how it has improved the sustainability of existing transport and what impact it has on various stakeholders are unclear. Therefore, a systematic literature review was carried out in this study on developing and adopting shared mobility for pursuing sustainable transport in urban traveling. Four emerging themes were identified, including attitude and intention, cooperation behaviors, operations and decisions, and performance evaluation, and some research gaps and challenges are discussed. An integrated framework for developing cooperation-oriented multi-modal shared mobility is proposed. This leads to better understanding of shared mobility and its use for sustainable transport in urban traveling.
... After a comprehensive literature review of the extant literature, we have identified the numerous barriers to SAVs, as presented in Table 1. Compliance with local laws [27] RPB4 Data privacy and security regulations [28] Public perception and trust PPT1 Safety concerns [29] PPT2 ...
Integrating shared autonomous vehicles (SAVs) in urban transportation systems holds transformative potential but is accompanied by notable challenges. This study, conducted in Saudi Arabia (KSA), aims to address these challenges by identifying and prioritizing the key barriers and policies that are necessary if we are to successfully adopt SAVs. A comprehensive analysis was performed through a literature review and expert consultations, revealing 24 critical barriers and 10 policies for solving them. The research employed a three-phase methodology to evaluate and rank the policies proposed to overcome these barriers. Initially, the study assessed the specific barriers and policies related to SAVs. Subsequently, the Fuzzy Analytic Hierarchy Process (FAHP) was employed to evaluate the relative importance of these barriers. Finally, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS) was applied to rank the policies; the process identified government-backed investment, urban planning integration, and funding for research and development in sensor and hardware technologies as the most effective policies. The study underscores the importance of targeted policies in addressing technical and infrastructural challenges. Emphasizing system reliability, cybersecurity, and effective integration of SAVs into urban planning, the findings advocate for robust government support and continued technological innovation. These insights offer a roadmap for policymakers and industry leaders in the KSA to foster a more sustainable and resilient urban transportation future.
... Other recent studies have established that travellers' attitude towards other modes also play key role in shaping their utilization of RHS, especially for users accustomed to hedonic benefits of PVs (Acheampong et al. 2020;Malik et al. 2021). This research also reviewed a few relevant studies in the domain of other emerging transport services (e.g., autonomous vehicles), to explore and compare latent constructs (Nazari et al. 2018;Rahimi et al. 2020). In addition to considering the attitudes relevant to RHS, the present study also reviews attitudes pertinent to other urban travel alternatives, as otherwise few key aspects would have been overlooked. ...
The study investigates the latent heterogeneity in travel behaviour among urban travellers, including ride-hailing service (RHS) users and non-users, by incorporating attitudes so as to reinforce conventional user-segmentation approaches. Simultaneously, prioritisation of ride-hailing specific attributes was carried out to assess how RHS will operate in a sustainable way. The study initially examines latent heterogeneity in travellers through a Latent Class Cluster Analysis (LCCA) model. Subsequently, it prioritises key RHS-specific attributes for each cluster using three established Multi Criteria Decision Making (MCDM) techniques. Three clusters were identified based on individuals’ attitudes and covariates (socio-demographics, travel habits, and built environment attributes). The largest cluster is the Tech-savvy ride-hailing-ready individuals (48%) with higher technological literacy, showing maximum acceptance towards ride-hailing. The second largest cluster comprises the Traditional active-mobility individuals (28%) who display the least proclivity towards RHS, probably due to their technological inhibition coupled with greater attachment to traditional travel alternatives. Lastly, the PV-loving multimodal individuals (24%) are primarily vehicle owners but prefer RHS for occasional trips. The final ranking obtained from the analysis has revealed that travel time, reliability, and flexibility are the motivators, while travel cost and waiting time are the deterrents, as perceived by the users, that influence RHS in the Indian context.
... While the sociodemographic characteristics of the study area are reported to be reasonably representative of larger urban areas in the U.S. (Wali and Khattak 2022), caution must be made in generalizing the study findings to other localities. Shared mobility use patterns are also impacted by individuals' awareness and concerns (e.g., technological awareness, convenience, affordability) (De Paepe et al. 2023, Wali 2023b) and attitudinal perceptions of service quality variables (e.g., data or privacy concerns, parking difficulty, response times) (Rahimi et al. 2020, Li et al. 2024. Future studies should incorporate and quantify the impacts of these variables that are unobserved in the data used in this study. ...
The rise of shared mobility services, including carsharing and ride-hailing, has transformative impacts on transportation systems. We present a behavioral framework to jointly model individuals’ carsharing and ride-hailing use with a focus on deciphering the substitutive vs. complementary roles of the built environment, transit accessibility, and active travel. Based on a sample of over 3,200 individuals from the 2019 Puget Sound Travel Survey, detailed travel behavior data are spatially integrated with neighborhood-level objectively assessed built environment and transit accessibility data. Joint heterogeneity-based multivariate ordered discrete choice models are specified to simultaneously account for random (unobserved) and systematic (observed) heterogeneity. The use patterns of carsharing and ride-hailing services exhibited positive dependence. Reflecting complementary impacts, neighborhood walkability, urban compactness, pedestrian-oriented urban design, and transit accessibility exhibited positive associations with individuals’ carsharing and ride-hailing use. Active travel behaviors (walking, biking, and transit use) also exhibited synergistic relationships with carsharing and ride-hailing use. While transit accessibility and active travel independently complement shared mobility services, our findings indicate that the interaction between the two could replace ride-hailing services. In particular, more physically active individuals (i.e., those engaging in greater active travel) may be choosing ride-hailing not out of preference but out of necessity due to lower transit accessibility around their home neighborhoods. Results suggest a mix of complementary vs. substitutive impacts, as opposed to the assumption of dichotomized (complementary or substitutive) impacts. Significant random and systematic heterogeneity in the behavioral, environmental, and demographic determinants of shared mobility services was revealed. We discuss the relevance and implications of the new findings considering scenario planning and travel demand modeling needs.
... Consequently, various stakeholders, including policymakers, are keenly interested in understanding attitudes towards autonomous driving and human-machine interaction in autonomous vehicles. This interest aims to align current mobility strategies with people's needs and expectations [18,23]. ...
Research conducted previously has focused on either attitudes toward or behaviors associated with autonomous driving. In this paper, we bridge these two dimensions by exploring how attitudes towards autonomous driving influence behavior in an autonomous car. We conducted a field experiment with twelve participants engaged in non-driving related tasks. Our findings indicate that attitudes towards autonomous driving do not affect participants’ driving interventions in vehicle control and eye glance behavior. Therefore, studies on autonomous driving technology lacking field tests might be unreliable for assessing the potential behaviors, attitudes, and acceptance of autonomous vehicles.
... Consequently, various stakeholders, including policymakers, are keenly interested in understanding attitudes towards autonomous driving and human-machine interaction in autonomous vehicles. This interest aims to align current mobility strategies with people's needs and expectations Rahimi, Azimi, and Jin (2020), Ward, Raue, Lee, D'Ambrosio, and Coughlin (2017). ...
Research conducted previously has focused on either attitudes toward or behaviors associated with autonomous driving. In this paper, we bridge these two dimensions by exploring how attitudes towards autonomous driving influence behavior in an autonomous car. We conducted a field experiment with twelve participants engaged in non-driving related tasks. Our findings indicate that attitudes towards autonomous driving do not affect participants' driving interventions in vehicle control and eye glance behavior. Therefore, studies on autonomous driving technology lacking field tests might be unreliable for assessing the potential behaviors, attitudes, and acceptance of autonomous vehicles.
... Furthermore, according to Philipsen et al. (2019), the exact pickup and arrival times were critical for SAV acceptance.Second, a body of previous literature has examined the association between sociodemographic variables and the adoption of PAVs and SAVs, and concluded that the choice patterns would most likely differ across different population sub-groups(Krueger et al., 2016;J. Zmud et al., 2016;Hulse et al., 2018;Guo et al., 2021;Rahimi et al., 2020). For instance,Tan et al. (2020) found that young people, students, and employees of businesses and organizations were among those who were more likely to use AVs than other travelers. ...
New mobility technologies, such as shared mobility services (e.g., car-sharing) and, more importantly, autonomous vehicles (AVs), continue to evolve. The supply-side advancement will likely disrupt and transform transportation mode choice behaviors, and create a new paradigm since they are emerging and becoming increasingly feasible alternatives to the existing modes of transportation. Accordingly, this dissertation employs discrete choice modeling (DCM) and machine learning (ML) using a U.S. nationwide stated choice experiment to understand how travelers adopt new transportation modes or continue to use conventional modes of transportation.
This dissertation consists of three papers. The first examines future market shares of each available mode of transportation in the era of AVs, factors influencing mode choice behaviors, and their marginal effects using a mixed logit model. The second uses interpretable ML to investigate the optimal algorithm (i.e., stochastic gradient boosting decision tree model) in greater depth, including feature importance and non-linear marginal effects. Focusing on methodology, the final paper assesses the limitations of ML when applied to transportation mode choice modeling and suggests future research directions for methodological improvements by comparing ML to DCM.
The dissertation contributes to three major elements of the current understanding of transportation mode choice behavior in the era of AVs and choice modeling as follows: First, consumers in the AV era could choose from a variety of transportation modes likely to coexist, including private AVs, shared mobility services, and conventional transportation modes. This dissertation thus makes a significant contribution by examining more comprehensive transportation mode choice behaviors and expanding demand-side discussions. Second, since current transportation planning efforts have relied on estimates and expectations, this dissertation contributes to the decision-making process by offering crucial underlying knowledge not currently available. Third, this dissertation assesses the limitations of ML for transportation mode choice modeling and suggests potential future avenues for methodological improvement.
... Still, there is a gap in terms of existing research on attitudes and personality traits in the scope of carsharing and shared mobility in general, mostly when comparing it to studies focusing on sociodemographics, which have been well examined and explored in the literature (Monteiro et al. 2023;Efthymiou and Antoniou 2016;Efthymiou et al. 2013). Several of these psychological factors are still under exploration and their role in the mode choice travel decision (Rahimi et al. 2020a) in general, and shared mobility use in particular, is not well understood.To the best of the authors' knowledge, many aspects of carsharing services have not yet been studied, such as the perceived service and feature offerings by different carsharing operators, including digital operator aspects (often reflected in the operator rating on the app store), as well as their impact on service adoption and use frequency (Monteiro et al. 2022). ...
Carsharing services have a significant potential for improving urban mobility by increasing the independence and freedom of travel and reducing traffic externalities. Although carsharing has been used for over a decade, several aspects need further investigation, such as the impact of user’s psychological factors on service use, as well as the factors impacting users’ choices between different carsharing operators, in particular their preferences for different payment schemes, and their perceptions of the operators’ application rating. Accordingly, four hybrid choice models (HCM) were estimated to investigate factors impacting (i) the knowledge about carsharing services, (ii) carsharing adoption, (iii) the shift from other modes to carsharing, (iv) the choice between carsharing operators with different payment schemes, using a large survey sample (N = 1044 responses 9469 SP observation) from Munich, Germany. The models showed the significance of sociodemographics, such as income level, education level, household size, employment status, ownership of a bike, access to a car, the availability of a driving license, and public transport subscription-based tickets on the carsharing use directly and indirectly, and four psychological factors encompassing different personality traits (i.e., adventurous), travel behavior, and attitudes were found to be significant in the various models; the latter covered service-related attitudes (perceived carsharing app importance) and travel behavior attitudes or profiles (frequent public transport user and frequent shared micromobility user). This research raises questions regarding the inequitable use of carsharing, the impacts of mobile applications on using the service, and the potential of integrating carsharing in mobility as a Service platforms to increase the potential for multimodality.
... Apart from enhancing transport safety, AVs can also optimize traffic by increasing transport efficiency and reducing congestion. Autonomous driving technology features technological innovation in transportation, requiring artificial intelligence, smart sensors, vehicle-to-vehicle (V2V) and vehicleto-infrastructure (V2I) communication technology, etc., which can bring high efficiency, convenience and better passenger experience [18]. Specifically, AVs can exchange information with other AVs on the road and predict the driving trajectories of surrounding vehicles, such as whether they will brake or accelerate in the following time [16]. ...
The rapid growth of the sharing economy has propelled shared mobility to the forefront of attention. The continuous advancement of autonomous driving technology also brings new opportunities and challenges to the shared mobility market. This study comprehensively analyzes the market potential concerning autonomous vehicles (AVs) to provide shared mobility services, utilizing SWOT analysis, PESTLE analysis, and Porter’s Five Forces. The findings reveal that AVs can provide improved shared mobility services by increasing transportation safety, reducing emissions, reducing costs, enhancing traffic efficiency, and increasing customer satisfaction and the profitability of shared mobility services. However, challenges such as technological and policy uncertainties, safety concerns, high initial costs, inadequate public communication infrastructure, and the absence of standardized regulations can hinder the widespread adoption of AVs. The benefits are also restricted with a low market penetration rate of AVs. To promote AVs in the shared mobility market, this study also provides implications for AV stakeholders tailored to the evolving shared mobility market dynamics.
... Apart from enhancing transportation safety, AVs can also optimize traffic by increasing transportation efficiency and reducing congestion. Autonomous driving technology features technological innovation in transportation, requiring artificial intelligence, smart sensors, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technology, etc., which can bring high efficiency, convenience, and better passenger experience [19]. Specifically, AVs can exchange information with other AVs on the road and predict the driving trajectories of surrounding vehicles, such as whether they will brake or accelerate [18]. ...
The rapid growth of the sharing economy has propelled shared mobility to the forefront of the public’s attention. Continuous advancements in autonomous driving technology also bring new opportunities and challenges to the shared mobility industry. This study comprehensively analyzes the impact of using land-based autonomous vehicles (AVs) to provide shared mobility services, utilizing SWOT analysis (Strengths, Weaknesses, Opportunities, and Threats), PESTLE analysis (Political, Economic, Social, Technological, Legal, and Environmental), and Porter’s Five Forces (the bargaining power of suppliers, the bargaining power of buyers, threats of new entrants, substitutes, and rivalry). The findings reveal that AVs can provide improved shared mobility services by increasing transportation safety, reducing emissions, reducing costs, enhancing traffic efficiency, and increasing customer satisfaction as well as the profitability of shared mobility services. However, challenges such as technological and policy uncertainties, safety concerns, high initial costs, inadequate public communication infrastructure, and the absence of standardized regulations can hinder the widespread adoption of AVs. The benefits are also restricted by the low market penetration rate of AVs. To promote AVs in the shared mobility market, this study also provides implications for AV stakeholders tailored to the evolving shared mobility market dynamics.
... Shared mobility refers to a range of transportation services that allow people to share vehicles rather than owning them individually [1]. This can include ride-hailing services like Uber, Lyft, Baidu and Ola, bike and scooter sharing, and car-sharing programs like Zipcar [2,3,4,5]. ...
Shared mobility is changing urban transportation in India by providing transportation services without the need for ownership. Sharedautorickshaws (also called as share-autos) are a popular mode of shared mobility in the country. These informal vehicles can hold six to ten passengers and operate on a hail-to-board basis. It is important to evaluate the service quality of share-autos as they gain popularity. While research on passenger satisfaction with shared mobility services exists, studies on service quality perception related to share-autos are limited. To address this research gap, a survey was conducted with 581 shareauto users in India. The study created a Confirmatory Factor Analysisbased model with five latent variables and 22 manifest variables. The results revealed that 18 variables significantly influenced service quality. Variables that had weaker factor loading in the overall analysis were found more important when analysed for different subsets of the sample population. For instance, female-only or low-income-group respondents may prioritize different factors than the overall sample, and the ranking of factor loading changes across the subsets. The study shows that subset-based analysis can provide a more nuanced understanding of the passenger experience in share-autos, identifying potential opportunities to improve the quality of these services.
... Research by [55] showed that concern for the environment, social influence, and perceived financial benefits play a role in the creation of consumers' attitudes towards shared ride-hailing services. Similarly, ref. [13] also argued that attitude towards carsharing is influenced by environmental, social, and economic factors. ...
Shared mobility platforms have built scalable digital marketplaces that facilitate the allocation and sharing of transportation and promote sustainable urban travel. Generation Z’s attitude toward shared consumption is closely linked to their perceptions of the importance of sustainability. This study identifies Generation Z’s awareness of shared mobility platforms in India and the factors that influence their use. Data were collected from 318 respondents from Generation Z in India and analyzed using partial least squares structural equation modeling. Findings indicate that Generation Z’s intention to use shared mobility is influenced by environmental consciousness, social aspects, economic benefits, and perceived risks. Results also show that perceived risks have an indirect effect on intention, which is mediated by attitude. The novel conceptual model developed and tested in this research can be used to inform policies and business models for the adoption of shared mobility services for Generation Z, ultimately promoting more sustainable transportation systems and improved urban mobility.
... Second, a body of previous literature has examined the association between socio-demographic variables and the adoption of PAVs and SAVs and concluded that the choice patterns would most likely differ across different population sub-groups (Krueger et al., 2016;Zmud et al., 2016;Hulse, Xie, & Galea, 2018;Guo et al., 2021;Rahimi, Azimi, & Jin, 2020). For instance, Tan et al. (2020) found that young people, students, and employees of businesses and organizations were among those who were more likely to use AVs than others. ...
... EFA transforms a large number of observed correlated variables into a smaller set of uncorrelated factors (Taherdoost et al., 2020). These latent constructs represent underlying attitudes or beliefs that may not be directly (Misra et al., 2022;Rahimi et al., 2020;Sarker et al., 2022;Titiloye et al., 2023). In this study, the eigenvalue, which represents how much of the variance of the parameters that a factor can explain, was used to determine the number of factors to retain. ...
This paper presents a study that explored the behavioral heterogeneity in changes in people's ICT usage and travel patterns at the end of the pandemic. A quasi-longitudinal approach was employed to collect data from Florida residents, capturing their online durations and trip frequencies for various activities before the pandemic and at the end of 2021. Utilizing the latent class analysis (LCA) approach to identify subgroups based on the online activity durations and trip frequencies, four distinct classes were identified. A little more than one third (35%) of the respondents are resilient users who showed minimal changes in both online activity durations and trip frequencies. About 33% of respondents are trip minimizers who maintained similar online activity durations but reduced travel for non-mandatory activities. About 16% of the respondents are substitutive adapters who showed increased online activity durations combined with reduced travel for non-mandatory activities. Another 16% of the respondents are complementary users who demonstrated higher online activity durations as well as trip frequencies for non-mandatory activities. These four latent classes reflect the diverse ways in which people have adjusted their daily routines and activities. The findings offer a starting point for understanding the complexities of behavioral changes in virtual and physical mobility as we transition to the new normal.
... [30], [31], [32] and [33] highlight that connected vehicles' interaction with infrastructure is based on a two-way cellular/wireless connectivity, and that capacity provides overthe-air (OTA) updates and upgrades. ...
... Despite the knowledge gained in transportation research in recent years, the understanding about mobility decisions of individuals and their determinants is still relatively limited, e.g., the possible influences of attitudes or the relevance of long-distance trips. Indeed, recently more studies, for example Moody (2019), Groth et al. (2021), and Rahimi et al. (2020), with questions on psychological factors have been used to capture non-objectively measurable parameters on individuals' travel behavior. Moreover, increasing attention is also being paid to the interrelationship between long-distance trips -which have a massive influence on the CO2 balance of people -and peoples' everyday travel behavior. ...
... Thus, there is an argument to be made that the theoretical boundaries between constructs in the UTAUT are more theoretical than practical. However, other research has found grounds for more than one acceptance factor (Kacperski et al., 2021;Rahimi et al., 2020;Yuen et al., 2020). Further research is crucial to ascertain whether acceptance of AVs should be represented as a single latent variable or multiple latent variables. ...
The technology behind shared autonomous vehicles (SAVs) is developing rapidly and may revolutionize public transport in metropolitan areas. To take full advantage of the potential benefits, it is paramount to understand the public acceptance of this new technology. One of the leading models for explaining technology uptake is the UTAUT (Unified theory of acceptance and use of technology). This model is vast and has received numerous suggested extensions and revisions, even being developed into the Multi-Level Model of Autonomous Vehicle Acceptance (MAVA). More research is needed to consolidate the model to best measure the acceptance of SAVs, and to determine which extensions capture the unique social situation arising within SAVs. The current study used survey data from 1902 respondents to perform a principal component analysis (PCA) of key constructs suggested by the MAVA. We found that these items were reducible to a single general acceptance factor (GAF), with three additional constructs measuring interpersonal security, sociability, and attractivity. The GAF was, by a large margin, the most efficacious predictor of intention to use SAVs. The overlap between GAF and intention to use may suggest that these are best conceptualized as a single component. The GAF could be further reduced to as little as two predictors, trust and usefulness, accounting for over 70 % of the variance in intention to use. There is, however, also an argument to be made that the other three components of SAV acceptance may be important for capturing different nuances of the service. Interaction terms show that there is differences between genders in their rating of sociability, and how this impacts intentions to use SAVs. Our results have important implications for future research within the field. It cements the importance of trust and usefulness and corroborates the claim that acceptance of SAVs is best represented by a single latent component. However, more research should investigate the individual level moderating effects on the other components, as this may unlock new insights about how best to design a future SAV service.
... Recent studies have demonstrated that decisions about the use of automated driving are largely dependent on one's environmental concern (Rahimi et al., 2020). For example, Herrenkind et al. (2019) proposed that publicizing the eco-friendliness of self-driving public buses would be effective in attracting elderly people to use them. ...
Shared autonomous vehicles (SAVs) have potential benefits for the society and environment. This study constructs a novel framework to investigate the combined effects of the external environment and personal attributes on SAVs adoption. Using questionnaire data from 669 Chinese consumers, a range of methods, including partial least squares structural equation modeling and fuzzy-set qualitative comparative analysis, were employed to conduct the empirical study. Results show that both the external environment (i.e., policy support, social norms, and media publicity) and personal attributes (i.e., environmental concern, risk preference, and personal innovativeness) significantly affect consumers’ intentions to adopt SAVs, while the former plays a greater role. Any single factor of external environment or personal attributes cannot lead to high SAVs adoption, thus four combinations covering different factors are provided to achieve a high adoption intention of SAVs. The findings can help global transportation managers make informed decisions to promote the adoption of SAVs.
... Ease-of-use and accessibility, defined in terms of sign-up procedures and walk time respectively, have been found to be important themes that can constitute a barrier to join in case a traveller has a negative perception of these elements, in particular for bikesharing services ( Fishman, 2016;Fishman et al., 2012;2015;Hess and Schubert, 2019;Whittle et al., 2019 ). Other factors, associated particularly with the attractiveness of carsharing services, are their compatibility with daily life, reliability, data privacy, convenience, and parking hassle ( Burghard and Dütschke, 2019;Rahimi et al., 2020;Winter et al., 2020 ). There is empirical evidence in support of the proposition that shared-use vehicle systems have the potential to alter mobility behaviour. ...
... Current mobility pattern of the respondents was found to be an influential factor in shaping their acceptance of SAMS in several of the studies. Frequent car users less willing to use the service for their commute trips (Haboucha et al., 2017;Krueger et al., 2016;Nordhoff, Stapel, et al., 2020;Rahimi et al., 2020). Those with higher mileage have more negative views towards SAMS. ...
The emergence of vehicle automation and its subsequent growth has led to new transport service offerings, generally known as Autonomous Mobility Services (AMS), that have the potential to replace human-operated vehicles. However, the functionalities of AMS are increasingly blurring the fine lines that currently distinguish different transport modes. For example, an autonomous shuttle bus, a form of autonomous transit, may serve a similar function as an autonomous taxi/robo-taxi, both coinciding with the concept of Shared Autonomous Mobility Services (SAMS). Even if the functionalities or operational principles are different, people may perceive sharing rides in any of these services as alike. Similarly, the absence of a human driver makes the concepts of autonomous carsharing and ridehailing similar. Hence, there is a need to review studies related to SAMS. However, few studies have attempted to perform a comprehensive review of public acceptance of SAMS. This study aims to fill this gap by reviewing studies related to public acceptability and acceptance, perception, intention to use, attitudes, mode choice and willingness constructs regarding SAMS. This review clearly distinguishes different types of SAMS while examining public’s acceptability and acceptance of SAMS across five dimensions: perception about the services, intention to choose and use those over other modal alternatives, frequency of usage and willingness constructs. Overall, the results from our review indicate the presence of heterogeneity across sub-groups regarding the adoption of SAMS. Discussing the factors affecting SAMS acceptance in a detailed manner, our study serves to provide a stocktake of the progress in this genre of research.
... While the first two groups of factors are already examined by a broad range of studies on general and type-specific AV usage intentions, few studies have examined intentions to use different AV types with a focus on factors not directly observable, such as the attitudes and needs behind these intentions; and not all of these studies examined the sustainability-related effects connected with these choices [20]. Especially with the influence of new technologies and demographic change on our lifestyle, it may no longer be sufficient to look only at socio-demographic characteristics and characteristics of the transport modes themselves (such as associated costs and travel duration) when examining the intention to use new mobility options [56,57]. Changes in the way we live and move-such as choosing more freely where to live or minimizing burdens for different mobility options through apps and online services-allow users to choose mobility options more appropriate for their needs and attitudes instead of being limited by external constraints. ...
When it comes to climate change, automated vehicles (AV) are often presented as a key factor to reducing emissions related with the transport sector. While studies promise emissions savings of up to 80%, it is often overlooked how AVs will be introduced and which transportation mode changes will arise from their implementation. Therefore, this online survey examined usage intentions regarding private and shared AV types, and underlying attitudes and mobility needs of 136 current users of different main modes of transport. Two main results counteract the general assumption of ecological sustainability benefits of AVs: First, current car drivers prefer private over shared AV types, even though notable sustainability gains can only be expected from shared AVs. Second, current users of more sustainable modes of transport (walking, bike, public transport) would replace theses modes by AVs for substantial shares of their trips, which represents a behavioural rebound effect, since AVs cannot be more sustainable than walking or biking. Group-specific mobility needs and knowledge gaps regarding the sustainability of different AV types are identified as reasons for these results and as starting points for deriving necessary measures accompanying the introduction of AVs into society through motivating ecologically sustainable transportation mode changes.
... Their research estimated the annual economic benefits of using AVs at around $27 billion with only a 10% market share and has an estimated potential of $450 billion in annual savings in the U.S. only. Rahimi et al. explain that negating public concerns surrounding AVs and promoting their benefits in terms of cost, time, and functionality will increase inclination towards AV adoption in the U.S. [129]. ...
Rapid worldwide research and development related to autonomous and shared autonomous vehicles (AVs and SAVs) and their expected presence on roads capture the attention of the public, decision-makers, industry, and academics. AVs and SAVs are expected to dominate automotive markets in the future due to their distinctive benefits: increased road safety, better utilization of travel time, improved energy consumption, enhanced traffic throughput, and expected environmental benefits are examples of some of the positive implications of these vehicles. However, AVs and SAVs will most likely increase the traveled miles and number of trips on roads because of their greater accessibility, which will most likely aggravate congestion. Therefore, there is a foreseen need for traffic regulation policies like road pricing (RP) to alleviate congestion-related problems in the era of AVs and SAVs.
On the one hand, AVs and SAVs possess advanced technology that allows for the application of advanced RP schemes that is anticipated to be implemented in the presence of driverless vehicles. On the other hand, RP has been proven effective in reducing traffic-related problems, for example, pollution in Milan and congestion in Stockholm. Despite this, the public acceptance of such a policy is considered low, which is a major reason for the scheme's failure. Therefore, this dissertation investigates the possible approaches to applying RP successfully and efficiently in the era of AVs and SAVs.
For a successful implementation of RP, the key requirement is public acceptability, which I investigated through a two-step approach: (1) I distributed a survey based on well-known methodologies in five capitals to define the factors that affect RP acceptability, (2) I developed the previous methodologies and disseminated a survey in four countries to investigate the factors that may influence RP acceptability in the era of driverless vehicles and driverless vehicle adoption in the presence of RP. I utilized different econometric models in analyzing the collected data to provide insight into the public perception of RP, AVs, and SAVs. For instance, a factor analysis was applied to minimize the large set of items into a lower number of factors. A multinomial logit model was generated to obtain the utility function parameters of conventional cars, AVs, and SAVs. In addition, multiple linear regression was applied to investigate RP acceptability as a function of all examined factors.
The results show that, in line with previous research, people who enjoy driving are less likely to choose AVs and SAVs, whereas environmentally oriented users are more likely to opt for AVs and SAVs. On the other hand, my research confirms the importance of other factors, such as the positive impact of the willingness to share personal trips with other passengers on RP acceptability and AV and SAV choice. Furthermore, the results demonstrate the interdependency between the factors influencing RP acceptability and AV and SAV choice. To the best of my knowledge, this study is the first to RP acceptability and AV and SAV adoption while also examining the impacts of various factors on both. Moreover, the results indicate that the identity of each case study and its general policy implications determine which factors significantly affect the public acceptability of the RP scheme.
For an efficient application of RP, I utilized dynamic traffic assignment using a transport network model for Budapest within the traffic macroscopic simulation software "Visum" through a two-step approach to investigate: (1) the impact of the emergence of AVs and SAVs on the Budapest network and consumer surplus in alternative future scenarios (2) the impact of three RP strategies (static and dynamic) on network performance and social welfare in the same alternative future scenarios.
Three future scenarios for the years 2030 and 2050 are presented and characterized by different penetration rates of AVs and SAVs to reflect the uncertainty in the market share of future cars. Moreover, the travel demand of the developed scenarios was obtained from The Centre for Budapest Transport projections for the respective years, where the total predicted private transport demand was 2.23, and 2.31 million trips per day for the years 2030, and 2050, respectively. In the "Mix-Traffic" scenario for 2030, conventional cars, AVs, and SAVs operate together in the network. The other two scenarios comprise only AVs and SAVs and are assumed for the year 2050, where the "AV-Focused" scenario represents high dependency on privately owned AV, and the "SAV-Focused" scenario reflects a high usage of SAV fleets. I also compared the implications of three distinct RP strategies in Budapest's proposed future traffic scenarios. The pricing schemes consisted of a static-fixed toll (bridge toll scheme), a static-variable toll (distance-based scheme), and a dynamic RP (link-based scheme).
The results regarding the impact of the deployment of AV and SAV on Budapest's network reveal that: from a traffic perspective, the emergence of AVs and SAVs would improve the overall network performance; furthermore, better performance was observed with increasing the share distribution of SAVs, where the lowest queues length, minimum delays, maximum velocity, and lowest vehicle kilometers traveled took place in the SAV-Focused scenario, followed by AV-Focused and Mix-Traffic scenarios, respectively. Similarly, the consumer surplus increased in all future scenarios, where the highest increment occurred in the AV-Focused scenario. Consequently, the advent of AVs and SAVs will improve traffic performance and increase consumer surplus, benefiting road users and authorities. The results regarding the implications of the applied pricing strategies demonstrate that the impact of RP schemes differs according to the change in penetration rates of AVs and SAVs. Nevertheless, considering the gained social benefits, implementing a dynamic pricing strategy (Link-based Scheme) in the case of AV-Focused and SAV-Focused scenarios performed better than static ones. On the contrary, the static pricing strategies (i.e., Bridge Toll and Distance-based Schemes) outperformed the dynamic ones in the Mix-Traffic scenario. Furthermore, the link-based scheme generated the maximum revenues (i.e., gathered tolls).
Rural public transport networks face significant challenges, often characterised by suboptimal service quality. With advancements in technology, various applications have been explored to address these issues. Autonomous Demand-Responsive Transits (ADRTs) represent a promising solution that has been investigated over recent years. Their potential to enhance the overall quality of transport systems and promote sustainable transportation is well-recognised. In our research study, we evaluated the viability of ADRTs for rural networks. Our methodology focused on two primary areas: the suitability of ADRTs (considering vehicle type, service offerings, trip purposes, demographic groups, and land use) and the broader impacts of ADRTs (including passenger performance, social impacts, and environmental impacts). Perceptions of ADRT suitability peaked for university precincts and 24/7 operations. However, they were less favoured by mobility-disadvantaged groups (disabled, seniors, and school children). We also examined demographic heterogeneity and assessed the influence of demographic factors (age, gender, education, occupation, household income level, and disability status) on the implementation of ADRTs in rural settings. The findings delineate the varied perceptions across these socio-demographic strata, underscoring the necessity for demographic-specific trials. Consequently, we advocate for the implementation of ADRT services tailored to accommodate the diverse needs of these demographic cohorts.
Autonomous ride-sharing services (ARSS) offer promise in enhancing transportation, improving access for underserved populations, and addressing road safety by mitigating human error. However, their development and adoption are influenced by complex interplay of policies, implementation strategies, technological performance, and market penetration. This scoping review examined the evolving ARSS landscape in the US through literature published between 2018 and 2023. The review included 22 studies, capturing some national policies while no federal regulations related to ARSS were identified. The review predominantly covered market penetration, with few studies addressing performance and one study on implementation strategies. Findings were framed using the socio-ecological model. At the individual level, factors such as safety, affordability, and accessibility influence market penetration of ARSS. At the relational level, trust-building interactions, including the role of safety operators, emerged as key to addressing mobility concerns. At the community level, the findings indicate the need for technological improvements, public infrastructure investment, and education initiatives to enhance ARSS performance and implementation. At the societal level, the review did not include all existing policies in the US, requiring further investigation. These findings provide insights for researchers, transportation planners, and policymakers, guiding the development of evidence-based strategies to foster a sustainable transportation future.
The primary aim of this study was to develop an accurate measure of acceptance for shared autonomous vehicles (SAVs) and to assess whether this measure can predict intentions to use SAVs. One leading model for explaining technology uptake is the UTAUT (Unified theory of acceptance and use of technology). This model is extensive and has received numerous suggested extensions and revisions, even being developed into a Multi-Level Model of Autonomous Vehicle Acceptance (MAVA). The challenge is to consolidate a model that effectively measures SAV acceptance and to determine which extensions capture the unique social situation within SAVs.
The current study used survey data from 1902 respondents. The sample was split into two: one half underwent a principal component analysis (PCA) and the other half a confirmatory factor analysis (CFA). We found that the 24 items we included were reducible to a single general acceptance factor (GAF), with three additional factors measuring interpersonal security, sociability, and attractivity. The GAF was, by a large margin, the most efficacious predictor of intention to use SAVs. The GAF could be further reduced to as little as two predictors, trust and usefulness, accounting for over 70 % of the variance in intention to use. However, there is also an argument to be made that the other components of SAV acceptance may capture different nuances of the service, particularly relating to the social situation. Interaction terms show differences between genders in their rating of sociability and how this impacts intentions to use SAVs.
Our findings carry significant implications for future research in this field. They underscore the pivotal roles of trust and usefulness while corroborating the notion that SAV acceptance is best represented by a single latent component. However, further investigation is warranted to explore individual-level moderating effects on the other components, potentially offering novel insights for the design of future SAV services.
This paper explores the influencing factors of commuters’ willingness to use ridesplitting services in the post-COVID-19 era – including promotional strategies – and the possible differences of these factors among commuters with different home-to-work commuting distances. A survey developed for this study was conducted among 1600 commuters from Shanghai, China between September and November 2021. A correlated random parameters ordered probit model is used to estimate the impact of various factors on the willingness to use ridesplitting services for individuals with different trip distances. The model results indicate that the delay compensation strategy has the potential to offer the largest increase in the likelihood of using ridesplitting services, as compared to other promotional strategies (i.e., discount, credit, and priority service strategies), particularly for medium- and long-distance home-to-work commuters. At the same time, the likelihood of using ridesplitting services may vary across specific types of commuters, such as residence owners, commuters using automobile-based transportation modes, travelers with flexible work schedules, and commuters who frequently work overtime. The paper’s insights may be used by ridesplitting service providers to assist in designing effective strategies to promote ridesplitting services.
This study investigates acceptance of shared autonomous shuttles (SASs) in a suburban area. A model where contextual variables were mediated through trust in SASs and technology optimism was tested. We examined intentions to use SASs without a steward and the significance of social distancing. Data were collected at the start and end of a 2020–2021 pilot involving 922 and 608 participants respectively, operating at SAE level 3. Findings indicate that trust and technological optimism significantly influence the willingness to use SASs, though contextual variables show minimal impact. Older adults and women displayed lower trust and optimism, reducing their usage intentions. These two groups also feel that it is more important to be able to keep social distance while riding SASs. The study suggests that future pilots should avoid negative impacts from using immature technology and address the social needs of specific groups.
Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels.
Recently, the market has witnessed the emergence of intelligent vehicles equipped with diverse functionalities. Among these are connected automated vehicles (CAVs) boasting a comprehensive platooning capability, and connected vehicles (CVs), which essentially refer to human-driven vehicles (HVs) equipped with connectivity features. As a result, CAVs, CVs, and regular human-driven vehicles (RVs) will form a mixed CAV-CV-RV flow to affect stability of existing traffic flow, which is an important characteristic of traffic flow operations. This paper investigates stability of this new mixed CAV-CV-RV flow, explores effect of CVs on stability, and proposes CV management strategy aimed at achieving a stable traffic flow. We firstly establish definition of the novel mixed flow comprising CAVs, CVs, and RVs. Subsequently, we present a general analytical approach for evaluating stability of this mixed flow. Then we undertake an empirical study that incorporates car-following models, validated using real experiments involving the aforemen-tioned vehicle types. Based on findings from our stability analysis, we proceed to propose a CV management strategy designed to guarantee stable conditions within this mixed traffic flow. Results indicate the effectiveness of the proposed general analytical method in assessing influences of various system factors on stability of this new mixed flow. Subsequent to an application of the analytical method, empirical study uncovers a noteworthy finding that CVs prove to be more effective than CAVs in ensuring stable conditions within the mixed flow. Then we can manage market proportion of CVs in order to achieve stable mixed flow, according to varying CAV penetration rates. Specifically, some interesting lessons are learned based on the empirical study: (i) to guarantee stable operations of mixed traffic flow, about one third of CVs are needed to be among HVs, before CAVs running on road (CV-RV mixed flow); (ii) management strategy for stable flow requires an increase of minimum CV proportion among HVs from 33% to 55%, as CAV penetration rates increase up to 50%; (iii) when CAV penetration rates are greater than 50%, the required minimum CV proportion for ensuring traffic flow stability decreases with the increase of CAV penetration rates.
Mobility-as-a-Service (MaaS) is a promising solution for sustainable mobility and has therefore received considerable attention from researchers. People have complicated travel diaries. Larger households, especially with children, with a limited number of cars, are essentially prone to delays or travel cancellations. MaaS options fill this gap for such families by offering mobility options accessible at low ownership costs. This paper investigates the impact of non-mandatory trips (mandatory trips include work and school) on MaaS subscription preferences. Different likelihoods of occurrence and the corresponding travel pattern shifts are examined using a stated preference survey of 997 individuals in Hong Kong. A two-stage model is developed: we first calibrate individual risk attitudes using the Constant Relative Risk Aversion (CRRA) utility function. Then we develop several mixed logit models that incorporate risks associated with activities and the response of travelers to the scale of such uncertainty on mode shift decisions. The results reveal that respondents tend to be more multimodal after subscribing to a MaaS bundle, and the use of a taxi is greatly encouraged after subscription. In terms of users’ risk preference, we find that more risk-averse individuals tend to be reluctant to subscribe to MaaS bundles when the level of uncertainty increases. Moreover, our findings demonstrate that travelers are more willing to pay for flexible travel options and have a higher value of time when facing uncertainties (i.e., in the presence of occasional activities). These findings can provide valuable insights for both policymakers and MaaS operators, reflecting users’ perceptions regarding the features of MaaS.
The development of shared mobility is short and unbalanced. To promote a healthy development of shared mobility enterprises, in this study, the development trend of shared mobility from the perspective of market pattern, market capacity, and market ecology based on relevant data is analyzed. The business models of three typical enterprises, Didi, CaoCao, and Uber, are then analyzed from the perspective of value proposition, profit model, promotion model, and operation model, and then from the perspective of integration of resources, mode guidance, and technology driven to build the China’s shared mobility enterprise development hierarchy. Finally, from the perspective of new models, advanced technology, and industrial integration, development strategies and suggestions for future shared mobility enterprises are presented.Keywordsshared mobilitybusiness modeltypical enterpriseshierarchical structuresmart mobility
Public transit disruption is becoming more common across different transit services, and can have a destructive influence on the resiliency of the transportation system. Even though transit agencies have various strategies to mitigate the probability of failure in the transit system by conducting preventative actions, some disruptions cannot be avoided because of their either unpredictable or uncontrollable nature. Utilizing recently collected data of transit users in the Chicago Metropolitan Area, the current study aims to analyze how transit users respond to an unplanned service disruption and disclose the factors that affect their behavior. In this study, a random parameter multinomial logit model is employed to consider heterogeneity across observations as well as panel effects. The results of the analysis reveal that a wide range of factors including socio-demographic attributes, personal attitudes, trip-related information, and built environment are significant in passengers' behavior in case of unplanned transit disruptions. Moreover, the effect of service recovery time on passengers is not the same among all types of disrupted services; rail users are more sensitive to the recovery time as compared with bus users. The findings of this study provide insights for transportation authorities to improve the transit service quality in relation to user satisfaction and transportation resilience. These insights help transit agencies to implement effective recovery strategies.
Autonomous vehicles (AVs) represent a new, growing segment of transportation research. While there have been prior studies and deployments of AVs worldwide, full autonomy in bus transit has gained interest among researchers and practitioners within the last decade, which presents an opportunity to synthesize early trends. Therefore, the objective of this paper is to provide a review of the latest research on fully autonomous buses to summarize findings and identify gaps needing future research. Forty studies were reviewed in detail, and five main themes were identified, which are (1) technology deployment; (2) user acceptance; (3) safety; (4) social and economic aspects; and (5) regulations, policies, and legal issues. The results reveal that most prior studies have focused on technology development, and the area of regulation and policy would benefit from additional study. Noteworthy differences between research in Europe and the United States were also identified. In Europe, large funded projects involving real-world deployments have focused on user acceptance, security and safety, costs, and related legal issues, whereas in the United States, research has primarily concentrated on simulation modelling with limited real-world deployments. The results of this review are important for policy-makers and researchers as AV technology continues to evolve and become more widely available.
With the goal of understanding autonomous vehicle (AV) adoption and use behavior, numerous behavioral studies and surveys have included variables intended to capture individuals’ perceptions of and attitudes toward AVs. However, the selection of questions to measure these psychometric variables appears to be ad hoc and, in many cases, arbitrary. In contrast, this study defines psychometric latent variables (LVs) that are related to the adoption and use of AVs and develops a set of questions to reliably measure them. By considering three psychological concepts (norms, perceptions, and attitudes) and nine qualitative utility constructs that influence individuals’ travel behavior, this study defines a comprehensive list of LVs and identifier questions to support their construction. A factor analysis of a nationwide n = 347 sample was used to obtain a minimum set of relevant LVs and questions to measure them. Ultimately, the factor analysis resulted in a final set of nine LVs specified by 44 questions (four or five questions for each LV). The final set of questions may be used by researchers or survey organizations interested in studying future trends of demand and adoption for AVs or other emerging transportation modes. The approach used in this study may also be employed in other contexts to define psychometric variables of interest and the questions needed to reliably measure them.
From early studies of time allocation onward, it has been acknowledged that the "productive" nature of travel could affect its utility. Currently, at the margin an individual may choose transit over a shorter automobile trip, if thereby she is able to use the travel time more productively. On the other hand, recent advancements toward partly/fully automated vehicles are poised to revolutionize the perception and utilization of travel time in cars, and are further blurring the role of travel as a crisp transition between location-based activities. To quantify these effects, we created and administered a survey to measure travel multitasking attitudes and behaviors, together with general attitudes, mode-specific perceptions, and standard socioeconomic traits (N = 2229 Northern California commuters). In this paper, we present a revealed preference mode choice model that accounts for the impact of multitasking attitudes and behavior on the utility of various alternatives. We find that the propensity to engage in productive activities on the commute, operationalized as using a laptop/tablet, significantly influences utility and accounts for a small but non-trivial portion of the current mode shares. For example, the model estimates that commuter rail, transit, and car/vanpool shares would respectively be 0.11, 0.23, and 1.18 percentage points lower, and the drive-alone share 1.49 percentage points higher, if the option to use a laptop or tablet while commuting were not available. Conversely, in a hypothetical autonomous vehicles scenario, where the car would allow a high level of engagement in productive activities, the drive-alone share would increase by 1.48 percentage points. The results empirically demonstrate the potential of a multitasking propensity to reduce the disutility of travel time. Further, the methodology can be generalized to account for other properties of autonomous vehicles, among other applications.
Using online survey data from N = 2041 Uber and Lyft users in the United States collected in 2016 and 2018, this paper establishes the validity, reliability, and invariance of a measure of rider-to-rider race and social class discrimination. This measure is then incorporated into three structural models that investigate associations between rider-to-rider discriminatory attitudes and four aspects of ridesharing behavior. We find no significant relationship between rider-to-rider discriminatory attitudes and whether a TNC user has ever used a ridesharing service (such as uberPOOL or Lyft Line). However, among those who have used ridesharing services before, rider-to-rider discriminatory attitudes are strongly negatively predictive of an individual's level of satisfaction with the sharing option, and marginally negatively predictive of an individual's percentage of shared TNC trips. Furthermore, among those who have not yet used ridesharing services, rider-to-rider discriminatory attitudes are strongly negatively predictive of willingness to consider using uberPOOL or Lyft Line in the future. Together, these findings suggest that rider-to-rider discriminatory attitudes may discourage sustained and frequent use of ridesharing services among TNC users. Further research is required to identify strategies for addressing discriminatory attitudes in the ridesharing context and overcoming reluctance to sharing.
Autonomous vehicles (AVs) hold considerable promise for maintaining aging adults’ mobility as they develop impairments in driving skill. Nonetheless, attitudes can be a significant barrier to adoption as has been shown for other technologies. We investigated how different introductions to AV, video with a driver in the front seat, the rear seat, and a written description, affected attitudes, as well as how individual difference variables such as age, gender, prior knowledge, and personality traits predict attitudes within a middle-aged (Median age = 34, IQR = 20, n = 441) Amazon Mechanical Turk sample. The 16-item attitude survey uncovered three factors: Concern with AV, Eagerness to Adopt AV technology, and Willingness to Relinquish Driving Control. ANOVAs showed that only age (younger less concerned) and gender, (females more concerned) were significant factors in Concern with AV. Only gender affected Willingness to Relinquish Driving Control, with males more willing. Multiple regressions that included previous knowledge level and personality traits showed a different pattern. Female gender and greater conscientiousness were associated with greater Concern about AV. Prior knowledge of AV was associated with less concern. Emotional stability and openness to experience were positive predictors of Eagerness to Adopt AV, whereas conscientiousness was a negative predictor. Prior knowledge and openness to experience, positively, and extraversion, negatively, were associated with being willing to relinquish driving control. These results suggest that different information dissemination campaigns are needed to persuade consumers to adopt AV technology. We discuss potential approaches.
Implementing measures to improve transportation systems requires detailed knowledge of potential travelers’ behavior and underlying psychological factors. Traditional travel surveys often provide information about travel behavior only. Crucial aspects such as information about attitudes and norms are usually not conducted. However, this information are further relevant to gain knowledge about acceptance of planned measures, e.g., new mobility offers. Our research tries to overcome these challenges by elaborating an integrated survey approach. Therefore, we combine questions on mobility patterns, using a travel skeleton, and questions related to people’s norms and attitudes. We conducted a travel survey in selected German cities and clustered data to define and identify different urban mobility types. Therefore, we brought together travel behavior as well as attitudes and norms. Our results showed the importance of both aspects as travel behavior does not necessarily correspond to people`s attitudes (e.g., people who like driving a car, but using transit). Our clusters distinguished both: travel behavior and attitudes. Among those clusters are for example convinced cyclists or eco-modes oriented pragmatics. The clustered data indicates how to address travelers to measures such as new mobility offers.
There is considerable interest in modeling and forecasting the impacts of autonomous vehicles
on travel behavior and transportation network performance. In an autonomous vehicle future,
individuals may privately own such vehicles, or use mobility-on-demand services provided by
transportation network companies that operate shared autonomous vehicle fleets or adopt a
combination of these two. This paper presents a comprehensive model system of autonomous
vehicle adoption and use. A Generalized Heterogeneous Data Model system is estimated on data
collected as part of the Puget Sound Regional Travel Study. Results show that lifestyle factors
play an important role in shaping autonomous vehicle usage. Younger urban residents who are
more educated and tech-savvy are more likely to be early adopters of autonomous vehicle
technologies, favoring a sharing-based service model over private ownership. Models such as
that presented in this paper can be used to predict adoption of autonomous vehicle technologies,
which will in turn help forecast autonomous vehicle impacts under alternative future scenarios.
Most of previous research that investigates the connections between the travel and satisfaction with travel has focused on the effect of the travel characteristics (e.g. travel mode choice, travel time, level of service, etc.) on satisfaction with travel. Little research has explored the role of the built environment or travel attitudes, two important factors for transport policies. Using data from a recent survey conducted in Xi’an, China, this study aims to quantitatively explore the relative effects of the built environment, travel attitudes, and travel characteristics on commute satisfaction. The data was analyzed using structural equation modeling. The model results suggest that commuting characteristics, including mode choice, congestion, and level of services of transit, all directly influence commute satisfaction. Attitudes have both direct and indirect effects on commute satisfaction, while the built environment only has indirect effects through influencing commuting characteristics.
Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever-evolving transportation system. Anticipating public acceptance and adoption of these technologies is important. A recent internet-based survey polled 347 Austinites to understand their opinions on smart-car technologies and strategies. Results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation (3300) to their current vehicles.
Ordered probit and other model specifications estimate the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding various automation technologies and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios (1, 2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, who live in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.
The importance of measuring the accuracy and consistency of research instruments (especially questionnaires) known as validity and reliability, respectively, have been documented in several studies, but their measure is not commonly carried out among health and social science researchers in developing countries. This has been linked to the dearth of knowledge of these tests. This is a review article which comprehensively explores and describes the validity and reliability of a research instrument (with special reference to questionnaire). It further discusses various forms of validity and reliability tests with concise examples and finally explains various methods of analysing these tests with scientific principles guiding such analysis.
Based on two individual validation studies employing face-to-face interviews and a mail survey, this article investigates factors influencing the probability that respondents truthfully admit to having been convicted of a criminal offense. Overall, 63 percent of the 495 respondents stuck to the truth in the survey settings. The mail survey elicited significantly more truthful responses (67 percent) than the face-to-face survey (58 percent). Female, older, and better-educated respondents confessed their delinquent behavior less often than male, younger, and less-educated ones. Whereas the need for social approval did not show a significant effect on the probability of an honest answer when controlling for other variables, trait desirability did. Regarding the face-to-face survey, the more interviews an interviewer had successfully completed in the ongoing study, the higher the response quality of the interviewees. In the mail mode, a strong negative effect of time to response could be observed: the longer respondents waited before they sent back the questionnaire, the lower the validity of their answers.
We integrate latent attitudes of the individuals into a transport mode choice model through latent variable and latent class models. Psychometric indicators are used to measure these attitudes. The aim of the inclusion of attitudes is to better understand the underlying choice preferences of travelers and therefore increase the forecasting power of the choice model. We first present an integrated choice and latent variable model, where we include attitudes towards public transport and environmental issues, explaining the utility of public transport. Secondly, we present an integrated choice and latent class model, where we identify two segments of individuals having different sensitivities to the attributes of the alternatives, resulting from their individual characteristics. The calibra-tion of these types of advanced models on our sample has demonstrated the importance of attitudinal variables in the characterization of heterogeneity of mode preferences within the population.
This paper presents a general methodology and framework for including latent variables—in particular, attitudes and perceptions—in choice models. This is something that has long been deemed necessary by behavioral researchers, but is often either ignored in statistical models, introduced in less than optimal ways (e.g., sequential estimation of a latent variable model then a choice model, which produces inconsistent estimates), or introduced for a narrowly defined model structure. The paper is focused on the use of psychometric data to explicitly model attitudes and perceptions and their influences on choices. The methodology requires the estimation of an integrated multi-equation model consisting of a discrete choice model and the latent variable model's structural and measurement equations. The integrated model is estimated simultaneously using a maximum likelihood estimator, in which the likelihood function includes complex multi-dimensional integrals. The methodology is applicable to any situation in which one is modeling choice behavior (with any type and combination of choice data) where (1) there are important latent variables that are hypothesized to influence the choice and (2) there exist indicators (e.g., responses to survey questions) for the latent variables. Three applications of the methodology provide examples and demonstrate the flexibility of the approach, the resulting gain in explanatory power, and the improved specification of discrete choice models.
If previous research studied acceptability of partially or highly automated driving, few of them focused on fully automated driving (FAD), including the ability to master longitudinal control, lateral control and maneuvers. The present study analyzes a priori acceptability, attitudes, personality traits and intention to use a fully automated vehicle.
421 French drivers (153 males, M = 40.2 years, age range 19–73) answered an online questionnaire.
68.1% Of the sample a priori accepted FAD. Predictors of intention to use a fully automated car (R2 = .671) were mainly attitudes, contextual acceptability and interest in impaired driving (i.e. the two components of FAD acceptability), followed by driving related sensation seeking, finally gender.
FAD preferred use cases were on highways, in traffic congestion and for automatic parking. Furthermore, some drivers reported interest in impaired driving misuses, despite awareness of their responsibility for both the vehicle and the driving. These results are discussed regarding previous knowledge about acceptability of advanced driving assistance systems and consequences for the use of fully automated cars.
This article provides an historical account of how fee -charging Victorian schools have marketed themselves over the past 150 years( via the use of advertisements, brochures, and prospectuses) in order to promote those aspects of schooling believed to be of most importance to potential customers (parents). While some of the features - most notably the success of pupils at examinations held at the completion of schooling - have always been emphasized in promotional material, others deemed worthy of attention in the nineteenth century, have now been replaced by quite features in contemporary advertisements.
Attitudes and attitude change remain core topics of contemporary social psychology. This selective review emphasizes work published from 2005 to 2009. It addresses constructionist and stable-entity conceptualizations of attitude, the distinction between implicit and explicit measures of attitude, and implications of the foregoing for attitude change. Associative and propositional processes in attitude change are considered at a general level and in relation to evaluative conditioning. The role of bodily states and physical perceptions in attitude change is reviewed. This is followed by an integrative perspective on processing models of persuasion and the consideration of meta-cognitions in persuasion. Finally, effects of attitudes on information processing, social memory, and behavior are highlighted. Core themes cutting across the areas reviewed are attempts at integrative theorizing bringing together formerly disparate phenomena and viewpoints.
This review of 122 research reports (184 independent samples, 14,900 subjects) found average r = .274 for prediction of behavioral, judgment, and physiological measures by Implicit Association Test (IAT) measures. Parallel explicit (i.e., self-report) measures, available in 156 of these samples (13,068 subjects), also predicted effectively (average r = .361), but with much greater variability of effect size. Predictive validity of self-report was impaired for socially sensitive topics, for which impression management may distort self-report responses. For 32 samples with criterion measures involving Black-White interracial behavior, predictive validity of IAT measures significantly exceeded that of self-report measures. Both IAT and self-report measures displayed incremental validity, with each measure predicting criterion variance beyond that predicted by the other. The more highly IAT and self-report measures were intercorrelated, the greater was the predictive validity of each.
Highly automated vehicles are expected to increase the safety and quality of road transportation , but because of significant changes in the driver's role, they introduce new human factors challenges and require learning specific skills. Training has been proposed as a potential tactic to alleviate these challenges and improve driver interaction with automated cars. This study evaluated the effects of Simulator Training and Video Training on procedural and higher-order cognitive skills required for Conditionally Automated Driving (CAD), and users' attitudes as well. Fifty-four people participated in the experiment , 18 in Simulator Training, 18 in Video Training, and 18 in Control groups. Takeover Time (TOT), speed, speed variance, Standard Deviation of Lateral Position (SDLP), Takeover Decision Accuracy (TODA), trust, and acceptance data were collected before and after training sessions. The results showed that both training methods improved TOT, speed variance, and SDLP. Moreover, the participants in Simulator Training outper-formed in deciding whether takeover was necessary or not. The results also indicated that self-reported trust was less erratic in Post-Training Driving Assessment in both Simulator and Video Training. These findings imply that training, especially where interactive learning is provided, helped the participants develop a more developed mental model of CAD and better-calibrated trust. Training programs, however, did not create meaningful changes in the number of crashes, speed, nor automation acceptability. Further research is needed to investigate learning transferability to highly automated driving on the real road.
To alleviate climate change and health issues caused by the transportation sector, a number of countries and sub-national regions have enacted policies to promote adoption of plug-in vehicles, including pol- icies encouraging the battery electric vehicle (BEV) adoption. Despite the potential benefits of BEVs to both society and consumers, barriers such as high purchase price and limited driving range impede the widespread adoption of BEVs, as evidenced by the small market share of BEVs at present. There is a need to better understand the process in which people make vehicle decisions, and more importantly the value they are willing to pay for purchasing a vehicle. This study employs a bivariate model, wherein a binary probit model of BEV adoption is simultaneously estimated with a log-linear regression of will- ingness to pay (WTP). The model accounts for the endogeneity of WTP to the equation of BEV adoption and also captures the cross-equation error correlation between the two model components. The esti- mation results using the 2016 California Vehicle Survey determines the associated influence of factors such as socio-economic characteristics, residential energy sources and prices, and travel behavior, as well as two sets of attitudinal/preferential factors including importance of vehicle attributes and BEV concern factors. The sensitivity analysis of the results reveals that BEV owners are more inclined towards adding another BEV to their households than those with other fuel types and it can be inferred that consumers’ familiarity with the benefits and costs of BEVs may lead to its higher adoption rates.
Even as ride-hailing has become ubiquitous in most urban areas, its impacts on individual travel are still unclear. This includes limited knowledge of demand characteristics (especially for pooled rides), travel modes being substituted, types of activities being accessed, as well as possible trip induction effects. The current study contributes to this knowledge gap by investigating ride-hailing experience, frequency, and trip characteristics through two multi-dimensional models estimated using data from the Dallas-Fort Worth Metropolitan Area. Ride-hailing adoption and usage are modeled as functions of unobserved lifestyle stochastic latent constructs, observed transportation-related choices, and sociodemographic variables. The results point to low residential location density and people’s privacy concerns as the main deterrents to pooled ride-hailing adoption, with non-Hispanic Whites being more privacy sensitive than individuals of other ethnicities. Further, our results suggest a need for policies that discourage the substitution of short-distance “walkable” trips by ride-hailing, and a need for low cost and well-integrated multi-modal systems to avoid substitution of transit trips by this mode.
Results from a recent consumer survey were thoroughly analyzed in relation to willingness to adopt and willingness to pay (WTP) for different autonomous vehicle (AV) features. Four different levels of automation were considered including basic vehicles, adding advanced features, partial automation, and full automation. A structural equations model with latent variables was employed, which simultaneously regressed adoption and WTP levels against a variety of available variables including socioeconomic and demographic attributes, private car usage habits, and attitudinal preferences/personal opinions. To address the endogeneity in personal attitudes, these variables were added to the model as latent factors. Accordingly, the analysis revealed four major latent attitudinal factors, respectively labeled as “joy of driving,”“mode choice reasoning,”“trust,” and “technology savviness.” Model results indicated that those who enjoy driving were the hardest to persuade towards AV adoption or to pay for automated features. On the other hand, technology savvy people showed higher tendency towards AV adoption. When it comes to factors affecting mode choice including travel time, travel cost, and functionality, people are willing to pay more for automated features when they believe that these features and services will provide them better utility, in relation to time and cost savings, convenience, stress reduction, and quality of life, and so forth. Interestingly, individuals with trust concerns showed higher WTP values, which may indicate that the market believes autonomous vehicles will bring more privacy and protection, at least compared with existing shared mobility or public transit options.
With the era of fully automated vehicles (AVs) quickly approaching, ridesharing services could have an important role in increasing vehicle occupancy, reducing vehicle miles traveled, and improving traffic conditions. However, the extent to which these potentials can be achieved depends on consumers’ disposition to sharing rides. From a travel behavior perspective, two essential elements to the adoption of shared rides are individuals’ acceptance of increased travel times associated with pick-up/drop-off of other passengers and their approval of strangers sharing the same vehicle. The current study develops the notion of willingness to share (WTS), which represents the money value attributed by an individual to traveling alone compared to riding with strangers, to investigate the adoption of shared rides. Using a multivariate integrated choice and latent variable approach, we examine current choices and future intentions regarding the use of shared rides and estimate individuals’ WTS as well as their values of travel time for two distinct trip purposes. Results show that users are less sensitive to the presence of strangers when in a commute trip compared to a leisure-activity trip. We also observe that the travel time added to the trip to serve other passengers may be a greater barrier to the use of shared services compared to the presence of a stranger. However, the potential to use travel time productively may help overcome this barrier especially for high-income individuals.
This paper aims to jointly model public interest in private AVs and multiple SAV configurations (carsharing, ridesourcing, ridesharing, and access/egress mode) in daily and commute travels with explicit treatment of the correlations across the (S)AV types. To this end, multivariate ordered outcome models with latent variables are employed, whereby latent attitudes and preferences describing traveler safety concern about AV, green travel pattern, and mobility-on-demand savviness are accounted for using structural and measurement equations. Drawing from a stated preference survey in the State of Washington, important insights are gained into the potential user groups based on the socio-economic, built environment, and daily/commute travel behavior attributes. Key policies are also offered to promote public interest in (S)AVs by scrutinizing the marginal effects of the latent variables.
People’s mindset and attitudes exert a strong influence on how quickly a new technology is adopted, thus also affecting how well the benefits resulting from automated vehicles can be realised. In previous studies on people’s attitudes towards automated vehicles, the number of respondents surveyed has been small, or alternatively survey’s sample has not been representative. By describing the results of a large citizen survey (N = 2036), this study aims to fill this identified research gap and to reveal, whether people are ready for automated vehicles and what concerns people have that hinder the adoption of these vehicles. The study’s results indicate that people’s attitudes towards automated vehicles reflect the general adoption of technology well. We can assume that those who currently view automated vehicles positively are most likely to belong in the group of early adopters. We found that men, highly educated individuals, people living in densely populated area and those living in households without a car had a more positive attitude to automated vehicles than the other respondents did. The results indicate that traffic safety and ethical perspectives have a key role in the acceptance of automated vehicles. Developers of automated vehicles should also take into account the finding that currently most people consider that all automated vehicles must have the option of manual drive.
Integrated choice and latent variable (ICLV) models are increasingly considered in many fields as a means to gain a deeper understanding into the decision process of individuals as well as to potentially improve predictive ability. Literature has shown that the application of ICLV in context of shopping destination choice has not been conducted yet. This study uses data collected from the city of Tehran, Iran on shopping destination choice of 812 individuals. Then these attitudinal questions are used for the development of latent variables regarding the attitude and lifestyle of the participants. By including one latent variable (LV) reflecting the attitudes of clothing and lifestyle’s of grocery travelers the structural model reveals a sample distribution of this LV conditional on fundamental socio-economic characteristics. The results of our latent variable model clearly confirm that personal attitude toward clothing shopping center and lifestyle indeed impact destination choice. The results show that our experiment and the expanded hybrid choice model (HCM) allow appropriately identifying and investigating the effects of mixtures of latent attitudes on the intention shopping destination choice.
The well-differentiated impact that parking supply options produce on congestion, pollution and land consumption arouses the interest of policy makers for a better understanding of car user’s behavior when choosing a parking option. Despite the evidence on the advantages of hybrid discrete choice models, most literature on parking choice only involves observable factors while leaving aside issues related to the latent variables. The behavioral hypothesis is that parking choice process depends not only on a set of observable factors but also has to do with individual-specific latent attributes. A hybrid discrete choice model with interactions among attitudes and observable factors, as well as among socioeconomic characteristics and observable factors, was estimated in order to consider individual heterogeneity. The results showed that, in addition to parking fee, search time and access time, a Risk-averse attitude and a Positive car care (maintenance) attitude are determinants for parking choice. The inclusion of these latent attributes and their interactions also resulted in a large improvement in the goodness-of-fit of the model and affected the time valuations.
This paper presents the preliminary results of a recent nationwide survey that focuses on mode choice behavior in view of emerging mobility options such as ride-sourcing and automated vehicles (AV). The survey provides a comprehensive scan of current mode choice patterns and the influencing factors. Then it presents stated preference (SP) choices to understand how travelers measure the trade-offs among different mode alternatives. The choice experiments focused on four potential user markets: drivers who usually drive for daily activities, passengers who depend on other household members or friends, transit users or users who do not have access to a private vehicle on a regular basis, and visitors or who do not have access to a private vehicle occasionally. The results suggest that on-demand services incorporating AV technologies (with lower operating costs) may become a viable option for many travelers. Most drivers and passengers preferred single ride than shared ride regardless of whether it is on a daily or occasional basis. However, for transit users, shared rides showed higher potential than exclusive services, which may indicate that cost is a primary consideration in the mode choice decisions for these users.
Environmental concerns have propelled public and private entities to search for ways to increase usage of public bicycle sharing systems. However, usage rates in many cities remain below expectations. Using the theory of perceived value to motivate our hypotheses,
this paper evaluates the impact of perceived value on consumer adoption intentions, and assesses the moderating effects of social and personal attitudes towards environmental behavior, on perceived value-adoption intention relationships. Our findings suggest that
public bicycle sharing adoption intention is significantly influenced by its perceived functional, conditional, green and social values. Moreover, personal and social attitudes towards ‘‘greenness” and bicycle sharing, respectively, are found to moderate the relationship
between perceived values and adoption intentions. This research contributes to theory-building in bicycle sharing adoption, and informs business and government leaders on how to promote adoption.
In structural equation modeling, application of the root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker–Lewis index (TLI) highly relies on the conventional cutoff values developed under normal-theory maximum likelihood (ML) with continuous data. For ordered categorical data, unweighted least squares (ULS) and diagonally weighted least squares (DWLS) based on polychoric correlation matrices have been recommended in previous studies. Although no clear suggestions exist regarding the application of these fit indices when analyzing ordered categorical variables, practitioners are still tempted to adopt the conventional cutoff rules. The purpose of our research was to answer the question: Given a population polychoric correlation matrix and a hypothesized model, if ML results in a specific RMSEA value (e.g., .08), what is the RMSEA value when ULS or DWLS is applied? CFI and TLI were investigated in the same fashion. Both simulated and empirical polychoric correlation matrices with various degrees of model misspecification were employed to address the above question. The results showed that DWLS and ULS lead to smaller RMSEA and larger CFI and TLI values than does ML for all manipulated conditions, regardless of whether or not the indices are scaled. Applying the conventional cutoffs to DWLS and ULS, therefore, has a pronounced tendency not to discover model–data misfit. Discussions regarding the use of RMSEA, CFI, and TLI for ordered categorical data are given.
Psychological certainty has been the subject of a great deal of research across a number of different literatures. This review focuses on prior and ongoing research on attitude certainty—the subjective sense of confidence of conviction a person has about an attitude—to provide a general overview of the role of certainty in attitudes and persuasion. First, we describe the antecedents, or origins, of attitude certainty, with particular attention to the metacognitive appraisals that drive people's feelings of certainty or uncertainty about their own attitudes. Second, we review the known consequences of attitude certainty, emphasizing the role of certainty in shaping information processing, attitude strength, and attitudinal advocacy. Third, we discuss recent developments that point to an upside for uncertainty in persuasion, whereby uncertainty experienced during message processing can increase message engagement and, thus, enhance message impact. Finally, we highlight several promising directions for future research. Our hope is that this review helps organize classic and contemporary research on attitude certainty and, in so doing, sparks new interest and continuing progress in the years to come.
Attitudes are thought to play an important role in determining people’s travel behavior, although researchers have acknowledged the possibility of a reverse relationship. Given the importance of knowledge about the direction of causation as a basis for policies and programs designed to effect behavioral change, the scarcity of in-depth research on this subject is surprising and problematic. The aim of the present paper is twofold: first to assess the bidirectional relationships between attitudes and behavior (in a transport context) and second to present a new framework to study attitude-behavior (in)consistency over time. To achieve these aims, we use data from a two-wave mobility panel to estimate cross-lagged panel models and latent transition models. Results indicate that use of a mode and the attitude towards using that mode mutually influence each other over time. As expected, we find that people who have dissonant (i.e., non-aligned) attitude-behavior patterns are less stable than those who have consonant patterns. Contrary to conventional wisdom and commonly used model structures, however, the effects of behaviors on attitudes are much larger than vice versa. That is, dissonant travelers are more inclined to adjust their attitudes to align those with their behavior than vice versa. Based on these results, we outline several implications for research and policy.
This study gains insight into individual motivations for choosing to own and use autonomous vehicles and develops a model for autonomous vehicle long-term choice decisions. A stated preference questionnaire is distributed to 721 individuals living across Israel and North America. Based on the characteristics of their current commutes, individuals are presented with various scenarios and asked to choose the car they would use for their commute. A vehicle choice model which includes three options is estimated:
(1) Continue to commute using a regular car that you have in your possession.
(2) Buy and shift to commuting using a privately-owned autonomous vehicle (PAV).
(3) Shift to using a shared-autonomous vehicle (SAV), from a fleet of on-demand cars for your commute.
A factor analysis determined five relevant latent variables describing the individuals’ attitudes: technology interest, environmental concern, enjoy driving, public transit attitude, and pro-AV sentiments. The effects that the characteristics of the individual and the autonomous vehicle have on use and acceptance are quantified through random utility models including logit kernel model taking into account panel effects.Currently, large overall hesitations towards autonomous vehicle adoption exist, with 44% of choice decisions remaining regular vehicles. Early AV adopters will likely be young, students, more educated, and spend more time in vehicles. Even if the SAV service were to be completely free, only 75% of individuals would currently be willing to use SAVs. The study also found various differences regarding the preferences of individuals in Israel and North America, namely that Israelis are overall more likely to shift to autonomous vehicles.
Methods to encourage SAV use include increasing the costs for regular cars as well as educating the public about the benefits of shared autonomous vehicles.
In the recent years many developments took place regarding automated vehicles (AVs) technology. It is however unknown to which extent the share of the existing transport modes will change as result of AVs introduction as another public transport option. This study is the first where detailed traveller preferences for AVs are explored and compared to existing modes. Its main objective is to position AVs in the transportation market and understand the sensitivity of travellers towards some of their attributes, focusing particularly on the use of these vehicles as egress mode of train trips. Because fully-automated vehicles are not yet a reality and they entail a potentially high disruptive way on how we use automobiles today, we apply a stated preference experiment where the role of attitudes in perceiving the utility of AVs is particularly explored in addition to the classical instrumental variables and several socio-economic variables. The estimated discrete choice model shows that first class train travellers on average prefer the use of AVs as egress mode, compared to the use of bicycle or bus/tram/metro as egress. We therefore conclude that AVs as last mile transport between the train station and the final destination have most potential for first class train travellers. Results show that in-vehicle time in AVs is experienced more negatively than in-vehicle time in manually driven cars. This suggests that travellers do not perceive the theoretical advantage of being able to perform other tasks during the trip in an automated vehicle, at least not yet. Results also show that travellers’ attitudes regarding trust and sustainability of AVs are playing an important role in AVs attractiveness, which leads to uncertainty on how people will react when AVs are introduced in practice. We therefore state the importance of paying sufficient attention to these psychological factors, next to classic instrumental attributes like travel time and costs, before and during the implementation process of AVs as a public transport alternative. We recommend the extension of this research to revealed preference studies, thereby using the results of field studies.
In confirmatory factor analysis (CFA), the use of maximum likelihood (ML) assumes that the observed indicators follow a continuous and multivariate normal distribution, which is not appropriate for ordinal observed variables. Robust ML (MLR) has been introduced into CFA models when this normality assumption is slightly or moderately violated. Diagonally weighted least squares (WLSMV), on the other hand, is specifically designed for ordinal data. Although WLSMV makes no distributional assumptions about the observed variables, a normal latent distribution underlying each observed categorical variable is instead assumed. A Monte Carlo simulation was carried out to compare the effects of different configurations of latent response distributions, numbers of categories, and sample sizes on model parameter estimates, standard errors, and chi-square test statistics in a correlated two-factor model. The results showed that WLSMV was less biased and more accurate than MLR in estimating the factor loadings across nearly every condition. However, WLSMV yielded moderate overestimation of the interfactor correlations when the sample size was small or/and when the latent distributions were moderately nonnormal. With respect to standard error estimates of the factor loadings and the interfactor correlations, MLR outperformed WLSMV when the latent distributions were nonnormal with a small sample size of N = 200. Finally, the proposed model tended to be over-rejected by chi-square test statistics under both MLR and WLSMV in the condition of small sample size N = 200.
Public traasport and cycling often are combined in one trip. However, this combination has not attracted much research attention. Existing research has identified several hard factors that may explain the combined use of public traasport and bicycle: station accessibility, distance to the station, and bicycle facilities at stations. Even though the effect of attitudes toward mode choice is widely acknowledged, the authors are not aware of any study that analyzes this effect oil the combined use of bicycle and public transport. The effect of attitudes on the decision to commute by both public traasport and bicycle was investigated. Results indicated that public transport-bicycle commuters differed significantly from those who commuted by only car, public transport, or bicycle. Nevertheless, public transport-bicycle commuters shared similarities with public transport commuters (who did not cycle to or from the station) and bicycle commuters. Public transport commuters had a more positive attitude toward car commuting and a less favorable attitude toward cycling, and bicycle commuters had a more positive attitude toward cycling and a less favorable attitude toward public traasport than did public traasport-bicycle commuters. Public transport-bicycle commuters also shared most beliefs about public traasport with public transport commuters and shared beliefs about cycling with bicycle commuters and public traasport commuters but differed on several characteristics. Nevertheless, differences between the groups were significant and indicated that commuters who used both public traasport and a bicycle in one trip were different from single-mode commuters.
This paper applied prospect theory (PT) to describe drivers' route choice behavior under Variable Message Sign (VMS), which presented visual traffic information to assist them to make route choice decisions. A quite rich empirical data from questionnaire and field spot was used to estimate parameters of PT. In order to make the parameters more realistic with drivers' attitudes, they were classified into different types by significant factors influencing their behaviors. Based on the travel time distribution of alternative routes and route choice results from questionnaire, the parameterized value function of each category was figured out, which represented drivers' risk attitudes and choice characteristics. The empirical verification showed that the estimates were acceptable and effective. The result showed drivers' risk attitudes and route choice characteristics could be captured by PT under real-time information shown on VMS. For practical application, once drivers' route choice characteristics and parameters were identified, their route choice behavior under different road conditions could be predicted accurately, which was the basis of traffic guidance measures formulation and implementation for targeted traffic management. Moreover, the heterogeneous risk attitudes among drivers should be considered when releasing traffic information and regulating traffic flow.
In the current paper, we propose a new multinomial probit-based model formulation for integrated choice and latent variable (ICLV) models, which, as we show in the paper, has several important advantages relative to the traditional logit kernel-based ICLV formulation. Combining this MNP-based ICLV model formulation with Bhat’s maximum approximate composite marginal likelihood (MACML) inference approach resolves the specification and estimation challenges that are typically encountered with the traditional ICLV formulation estimated using simulation approaches. Our proposed approach can provide very substantial computational time advantages, because the dimensionality of integration in the log-likelihood function is independent of the number of latent variables. Further, our proposed approach easily accommodates ordinal indicators for the latent variables, as well as combinations of ordinal and continuous response indicators. The approach can be extended in a relatively straightforward fashion to also include nominal indicator variables. A simulation exercise in the virtual context of travel mode choice shows that the MACML inference approach is very effective at recovering parameters. The time for convergence is of the order of 30–80 min for sample sizes ranging from 500 observations to 2000 observations, in contrast to much longer times for convergence experienced in typical ICLV model estimations.
An analysis was carried out using the 1991 wave of the Puget Sound Transportation Panel data set to determine the role played by attitudinal and preference variables in explaining commuter mode-choice behavior. Different modal market segments were compared to determine the extent to which attitudes and preferences differ across mode choices. A factor analysis was performed on the sample to identify a few distinct factors that would summarize the multitude of attitudinal variables present in the data set. Multinomial logit models of mode choice were estimated using different utility specifications. Three types of models were estimated: one that included only demographic variables, another that included only attitudinal factors, and another that included both demographic and attitudinal variables. Likelihood ratio tests were applied to assess the significance of the contribution of different types of variables in explaining mode-choice behavior. Results show that demographic variables and attitudinal variables are extremely important in explaining mode-choice behavior. More noteworthy, however, is the finding that the contribution of attitudinal factors is greater than that of demographic variables, thus emphasizing the need for greater consideration of attitudinal and preference variables in travel-demand-modeling applications.
Two Monte Carlo studies were conducted to examine the sensitivity of goodness of fit indexes to lack of measurement invariance at 3 commonly tested levels: factor loadings, intercepts, and residual variances. Standardized root mean square residual (SRMR) appears to be more sensitive to lack of invariance in factor loadings than in intercepts or residual variances. Comparative fit index (CFI) and root mean square error of approximation (RMSEA) appear to be equally sensitive to all 3 types of lack of invariance. The most intriguing finding is that changes in fit statistics are affected by the interaction between the pattern of invariance and the proportion of invariant items: when the pattern of lack of invariance is uniform, the relation is nonmonotonic, whereas when the pattern of lack of invariance is mixed, the relation is monotonic. Unequal sample sizes affect changes across all 3 levels of invariance: Changes are bigger when sample sizes are equal rather than when they are unequal. Cutoff points for testing invariance at different levels are recommended.
Driver behaviour is often studied by using self-reports and questionnaires. Despite their obvious advantages, questionnaires are vulnerable to socially desirable response tendencies. In this study, the effects of socially desirable responding on self-reports of driving were studied by recording self-reports of driving in both public and private settings. In public settings, 47 applicants for a driving instructor training course completed the driver behaviour questionnaire (DBQ) and balanced inventory for socially desirable responding as a part of the entrance examination. In a private setting, 54 students of that training course completed the same questionnaires anonymously in the classroom. ANOVA results showed a difference between the two settings in six DBQ item scores, such that aberrant behaviours were reported less frequently in public than in private settings. The results indicated that bias caused by socially desirable responding is relatively small in DBQ responses.