This study explored travelers’ behavioral intention to adopt ride-hailing services. With regard to perceived value, several factors related to perceived benefit and perceived sacrifice were considered. Moreover, subjective norm and perceived policy support were further introduced into the concept model. After the construction of the concept model, an empirical analysis was put forward to test the hypotheses proposed. In addition, the effect of sociodemographic factors and usage frequency was further investigated. The empirical analysis was based on a survey that put forward in Nanjing, China. The results demonstrate that perceived value is positively related to behavioral intention. And factors of perceived benefit are related to perceived value positively, while factors of perceived sacrifice have a negative effect on perceived value.

The star tracker is the most accurate attitude measurement device among different types of attitude measurement devices. It is based on captured star images to extract star centroids and then determine attitude. However, images taken by the star tracker under dynamic conditions are often blurred, which limits its dynamic performance. In this paper, we first classify and model motions of the star tracker under dynamic conditions, including rotations and angular vibrations. Then, a motion kernel consisting of three individual descriptors (trajectory, intensity, and point spread function) is proposed to model the motion blurring process and simulate blurred images. Experimental results show that our approach can model a wide variety of blurs including uniform and non-uniform blurs, and it can provide extremely realistic blurred images. These findings are crucial to the subsequent centroid extraction of stars and the dynamic performance of the star tracker.

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