Users’ preferences towards innovative and conventional public transport

Site, Paolo Delle; Filippi, F.; Giustiniani, Gabriele

doi:10.1016/j.sbspro.2011.08.099

Cited by 23 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to safety, vehicle and service attributes have been considered important determinants of AVA. Site et al (2011) revealed that on-board comfort (i.e., whether passengers were sitting or standing) was related to the use of automated vehicles. In our interview study , the majority of respondents' quotes captured service quality aspects (e.g., availability, flexibility and convenience), hinting that service quality is an important determinant of AVA.…”

Section: Domain-specific System Evaluation: Performance and Effort Exmentioning

confidence: 99%

A multi-level model on automated vehicle acceptance (MAVA): a review-based study

Nordhoff

Kyriakidis

Arem

et al. 2019

Theoretical Issues in Ergonomics Science

136

View full text Add to dashboard Cite

Automated vehicle acceptance (AVA) is a necessary condition for the realisation of higher-level objectives such as improvements in road safety, reductions in traffic congestion and environmental pollution. On the basis of a systematic literature review of 124 empirical studies, the present study proposes MAVA, a multi-level model to predict AVA. It incorporates a process-oriented view on AVA, considering acceptance as the result of a four-stage decision-making process that ranges from the exposure of the individual to automated vehicles (AVs) in Stage 1, the formation of favourable or unfavourable attitudes towards AVs in Stage 2, making the decision to adopt or reject AVs in Stage 3, to the implementation of AVs into practice in Stage 4. MAVA incorporates 28 acceptance factors that represent seven main acceptance classes. The acceptance factors are located at two levels, i.e., micro and meso. Factors at the micro-level constitute individual difference factors (i.e., sociodemographics, personality and travel behaviour). The meso-level captures the exposure of individuals to AVs, instrumental domain-specific, symbolic-affective and moral-normative factors of AVA. The literature review revealed that 6% of the studies investigated the exposure of individuals to AVs (i.e., knowledge and experience). 22% of the studies investigated domain-specific factors (i.e., performance and effort expectancy, safety, facilitating conditions, and service and vehicle characteristics), 4% symbolic-affective factors (i.e., hedonic motivation and social influence), and 12% moral-normative factors (i.e., perceived benefits and risks). Factors related to a person's socio-demographic profile, travel behaviour and personality were investigated by 28%, 15% and 14% of the studies, respectively. We recommend that future studies empirically verify MAVA using longitudinal or experimental studies.

show abstract

Section: Domain-specific System Evaluation: Performance and Effort Exmentioning

confidence: 99%

A multi-level model on automated vehicle acceptance (MAVA): a review-based study

Nordhoff

Kyriakidis

Arem

et al. 2019

Theoretical Issues in Ergonomics Science

136

View full text Add to dashboard Cite

show abstract

“…In a series of focus groups conducted by [22], females were more open to fully driverless cars than men, as they said they would have more time to take care of their children in the back seat. Several studies have shown that individuals exhibit greater intention to use driverless cars with increasing age [19,[23][24][25], with some authors suggesting that age differences in intentions to use may be based on different perceptions of when and where automation might be useful. This has recently been shown in a large-scale consumer survey study by [26], who report that younger respondents were initially more willing to use a self-driving car compared to older respondents.…”

Section: Influence Of Age and Gender On Intention To Use Cacsmentioning

confidence: 99%

Drivers’ Intentions to Use Different Functionalities of Conditionally Automated Cars: A Survey Study of 18,631 Drivers from 17 Countries

Louw

Madigan

Lee

et al. 2021

IJERPH

View full text Add to dashboard Cite

A number of studies have investigated the acceptance of conditionally automated cars (CACs). However, in the future, CACs will comprise of several separate Automated Driving Functions (ADFs), which will allow the vehicle to operate in different Operational Design Domains (ODDs). Driving in different environments places differing demands on drivers. Yet, little research has focused on drivers’ intention to use different functions, and how this may vary by their age, gender, country of residence, and previous experience with Advanced Driving Assistance Systems (ADAS). Data from an online survey of 18,631 car drivers from 17 countries (8 European) was used in this study to investigate intention to use an ADF in one of four different ODDs: Motorways, Traffic Jams, Urban Roads, and Parking. Intention to use was high across all ADFs, but significantly higher for Parking than all others. Overall, intention to use was highest amongst respondents who were younger (<39), male, and had previous experience with ADAS. However, these trends varied widely across countries, and for the different ADFs. Respondents from countries with the lowest Gross Domestic Product (GDP) and highest road death rates had the highest intention to use all ADFs, while the opposite was found for countries with high GDP and low road death rates. These results suggest that development and deployment strategies for CACs may need to be tailored to different markets, to ensure uptake and safe use.

show abstract

“…Therefore, for the estimation procedure, a Bayesian estimation method was applied to lessen any computational complexity and local maximization problems arising in the classical method [25][26][27][28][29][30][31].…”

Section: The Multiple Discrete-continuous Extreme Value (Mdcev) Modelmentioning

confidence: 99%

Can Next-Generation Vehicles Sustainably Survive in the Automobile Market? Evidence from Ex-Ante Market Simulation and Segmentation

et al. 2018

View full text Add to dashboard Cite

Introduced autonomous and connected vehicles equipped with emerging technologies are expected to change the automotive market. In this study, using stated preference (SP) data collected from choice experiments conducted in Korea with a mixed multiple discrete-continuous extreme value model (MDCEV), we analyzed how the advent of next-generation of vehicles with advanced vehicle technologies would affect consumer vehicle choices and usage patterns. Additionally, ex-ante market simulations and market segmentation analyses were conducted to provide specific management strategies for next-generation vehicles. The results showed that consumer preference structures of conventional and alternative fuel types primarily differed depending on whether they were drivers or non-drivers. Additionally, although the introduction of electric vehicles to the automobile market is expected to negatively affect the choice probability and mileage of other vehicles, it could have a positive influence on the probability of purchasing an existing conventional vehicle if advanced vehicle technologies are available.

show abstract