Modeling and gender difference analysis of acceptance of autonomous driving technology

In order to deeply analyze the differences in the acceptance of autonomous driving technology among different gender groups,a multiple indicators and multiple causes model was constructed by integrating a technology acceptance model and theory of planned behavior to comprehensively reveal the gender differences in the influence mechanisms of subjective and objective factors.The analysis is based on data collected from Chinese urban residents.Among objective factors,age has a significant negative impact on women's perceived behavior control and a significant positive impact on perceived ease of use.Education has a significant positive impact on men's perceived behavior control,and has a strong positive impact on women's perceived usefulness(PU).For men,income and education are found to have strong positive impacts on perceived behavior control.Among subjective factors,perceived ease of use(PEU)has the greatest influence on women's behavior intention,and it is the only influential factor for women's intention to use autonomous driving technology,with an influence coefficient of 0.72.The influencing path of men's intention to use autonomous driving technology is more complex.It is not only directly affected by the significant and positive joint effects of attitude and PU,but also indirectly affected by perceived behavior controls,subjective norms,and PEU.

Examining unobserved factors associated with red light running in Vietnam:A latent class model analysis

Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid considerable attention to the observable factors,but not to unobservable factors.This study aims to examine the effects of observable and unobservable factors on RLR.This study uses a latent class model(LCM)to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes.This study incorporates psychological determinants into the LCM to account for unobservable factors.The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators.Such a study has not yet been conducted in the existing literature.In addition,a comprehensive comparison of the LCM and a traditional ordered probit model was conducted.Overall,the results suggest that the LCM is superior to the model that does not consider latent classes.Our estimation results are in alignment with previous studies on RLR:males,younger drivers/riders,less educated road users and motorcyclists are more likely to run red lights.An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators,the absence of traffic police,and long waiting times—increase the possibility of violations.Based on these results,we provide suggestions to policymakers and traffic engineers:the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.