With the continuous development of automatic driving technology and advancements in related experimental research, the probability of traffic accidents caused by human factors has been greatly reduced. However, people are still cautious about the safety of automated driving technology. The purpose of this study was to investigate users’ perceived safety indicators and the psychological factors of their perceived safety judgment of self-driving buses. In this study, a structural model of the factors that influence self-driving buses, including behavioral intention of technology acceptance, trust theory, perceived risk, and perceived safety, was developed based on the technology acceptance model (TAM). Subsequently, a relevant survey of 215 respondents was conducted and analyzed using the partial least squares method. The results indicated that trust, perceived usefulness, and perceived ease of use were important factors for judging the perceived safety of self-driving buses. The structural model developed in this study can quantify and analyze user data to filter out the factors that influence the perceived safety of self-driving buses, which is conducive to improving people’s trust and acceptance of self-driving buses.
As a new mode of public transportation, self-driving buses offer numerous benefits, including increased traffic safety, reduced energy consumption, optimized road-resource ratios, and improved traffic accessibility. However, there is still a need to fully understand the public’s perception of self-driving buses before they are widely used. As a result, we investigated whether individual differences (including demographic and personality traits) influence the acceptance of self-driving buses in Nanjing, China. A questionnaire was given to 453 people in Nanjing, and the sample data were analyzed using a one-way analysis of variance (ANOVA). According to the findings, gender, age, educational background, income level, frequency of use, and personality traits all had a significant impact on the acceptance of self-driving buses. This study’s findings provide empirical data to help guide future research on self-driving buses, as well as a theoretical foundation for self-driving-bus development and design.
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