This research delves into the transformative potential of self-driving vehicles by investigating their impact on passengers’ happiness. As autonomous transportation technology rapidly evolves, understanding the user experience within these vehicles becomes essential. To investigate the happiness of the self-driving vehicles’ passengers, which is defined as positive emotions and cognitive well-being during self-driving rides, real-traffic test rides were conducted among 31 participants, companied with two before-ride and after-ride surveys. The study employs a comprehensive approach, combining self-reported survey data and biometric measurements, which includes the participants’ heart rate and the eye movement, to investigate passengers' happiness. The results present an overall positive emotions and positive attitudes towards the self-driving vehicle. Moreover, the findings present a notable shift in passengers' attitudes, with originally neutral sentiments transitioning to positive perceptions following the test ride. Participants exhibited various activities during the ride, enhanced comfort with the concept, and an improved satisfaction with self-driving technology. Remarkably, statistical trends suggest that self-driving vehicles hold the potential to alleviate stress and optimize time management, positively impacting passengers' overall well-being. Furthermore, biometric data of the participants presented participants’ different physical reaction on different traffic scenarios and indicated the happiness and well-being of the participant. The research emphasizes the broad implications of self-driving technology on individual happiness, concerning both emotions and attitudes, extending beyond functional enhancements to encompass passenger happiness and societal integration.

This is a research project for course CIE5090-09. The research aims to investigate the impact of the light-mopeds on the cyclists’ perceived safety. An online questionnaire is delivered to collect the participants’ feelings on the perceived safety of the given scenarios, and the reasons why they are feeling unsafe, as well as their intended reaction to the light-mopeds. The results shows that the participants would in general feel unsafe when they are in the conflicting directions with the light-mopeds. The leading reason for them to feel unsafe is the turning and speeding of the light-mopeds. Moreover, the results shows that most of the participants would take evasive actions, for instance to give their way or slow down, when feeling unsafe interacting with the light-mopeds.

E-bikes have gained global popularity due to their environmentally friendly and sustainable attributes, as well as their ability to provide fast speeds and power assistance. However, the increasing popularity of e-bikes has introduced conflicts and crashes with other road users, especially the interaction with conventional bikes, as e-bikes and bikes share the same cycling infrastructure. To study the perceived risk of interaction with e-bikes, this research delves into the various factors, in terms of traffic environment, bicycle type, and individual factors.
This research has used the video-based survey method to measure the perceived risk of cyclists and presented traffic environment and bicycle types in hypothetical traffic scenarios with pre-recorded videos. A questionnaire is designed to investigate participants` perceived risk of hypothetical bicycle interactions and their personal information on demographics, cycling experience, competence of riding skills, cycling behaviors, and expectancy on e-bikes. Moreover, the perceived risk is measured by two items separately: the rate of perceived risk and the likelihood of being involved in a crash. After recruitment and data collection, the effects of various factors have been estimated by the random-effects ordered logit model.
Results implied that compared to interaction between conventional bikes, riding on an e-bike when encountering a conventional bike decreased the perceived risk. Moreover, riding at peak hours and having a conflict with left-turning cyclists was found to increase the perceived risk, while riding at a large intersection was found to decrease the perceived risk. In terms of individual factors, the experience of bicycle crashes and the preference to use an e-bike were found positively related to the perceived risk. While the competence of cycling skills and age were found negatively associated with perceived risk. Additionally, it was found that traffic environment factors were more prominent in predicting the rate of perceived risk, while individual factors had a stronger influence on predicting the likelihood of being involved in a crash.
These findings provide insights into the influence of e-bikes in shared cycling spaces, underscore the importance of traffic management and safety measures in crowded bicycle traffic, and emphasize the significance of intervention and educational initiatives for cyclists to decrease the perceived risk and enhance cycling safety.