2018, Vol.13, No.3

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

pISSN : 1975-7700 / eISSN : 2734-0570
https://journal.kci.go.kr/kkits

This study deals with the analysis of traffic congestion and the peak hour identification by using Kalman Filter and Ensemble Model. There are different types of traffic congestion, Roadway Traffic congestion, Airways Traffic congestion, Network Traffic congestion, and so on. This study focuses on Roadway Traffic congestion. The peak hour identification is essential to prevent roadway traffic congestion. In roadway traffic congestion, there are two categories in traffic data, namely Roadside Equipment (RSE) data and Video Detection System (VDS) data. Both data were collected from RSE devices and VDS devices, which are located in roadways signals, toll plaza, private sectors, and etc. In traffic data, it may contain error values. So, this paper applies the Kalman Filter for the purpose of removing the error values or inaccurate values and providing the cleaned Traffic data. The suggested study also uses the Ensemble Model to average the traffic data at corresponding hours easily to analyse the traffic data. To identify peak hour, it defines four different models by considering numbers, average times, and average speeds of vehicles. With the suggested method, the perfect peak hour in the traffic data can be easily and exactly obtained. In the tests and results, this paper showed the detailed process of peak hour identification in traffic congestion.

This study is about the elderly people's use and experience of Virtual Reality (VR) in order to enhance their quality of life. Leisure is main activity of the elderly. As a result, the quality and diversity of leisure affects the quality of the elderly people's life. VR services including video contents could provide them with advanced and interesting experiences. Across the world, population aging becomes one of the most important problems in each society. The social and economic burden of aging is serious challenge to sustainability welfare of the world, including S. Korea. The authors used virtual reality devices to conduct experiments on 99 aged 50 to 90 (men's average age : 75 and women's average age : 70.8). Through the survey, major variants were measured such as system quality, present sense, enjoyment, intent to reuse, and awareness of advanced technology. Multiple regression analyses with the presence as a dependent variable resulted in a sense of closeness (t = 5.381, p <. 01) and a sense of clarity (t = 4.494, p <. 01). The presence of an independent variant had significant effects on pleasure (t = 4.312, p <. 01) and significant effects on the intent to reuse (t = 3.323,).Therefore, it can be interpreted that the higher the sense of existence, the more pleasure and the intent to reuse it. As a result, the possibility of using VR devices and content to enjoy leisure activities can be found in this study even if the elderly group is unfamiliar with IT devices.

Self-directed career development is motivated to control over their jobs and careers. This enables employees change their tasks and alter boundaries of their jobs to design their work with they are satisfied. Thus, the objective of this study is to understand self-directed career development for IT workers. First, this study investigated the elements of self-directed career development for IT workers such as job autonomy and career growth opportunities. Job autonomy is defined as opportunities to change work according to needs, capabilities, and context. Career growth opportunities is defined as progression through a sequence of jobs. Second, this study examined the relationships between two elements of self-directed career development and learning goal orientation. Learning goal orientation is defined as the degree to strive to understand something new or to increase level of competence in a given task. Finally, this study examined the relationships between learning goal orientation and organizational commitment. The data used to test research hypothesis were 213 samples. The results showed that both job autonomy and career growth opportunities affect learning goal orientation positively. Also we found that learning goal orientation has significantly positive impact on organizational commitment. This study contribute implications for researchers by developing and testing a systematic model of understanding self-directed career development for IT workers.