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http://dx.doi.org/10.14400/JDC.2021.19.5.093

Social Network Analysis of Long-term Standby Demand for Special Transportation  

Park, So-Yeon (Department of Information and Communication Engineering, Yeungnam University)
Jin, Min-Ha (School of Management/Data Science, Handong Global University)
Kang, Won-Sik (Department of Political Science and Diplomacy, Kyungpook National University)
Park, Dae-Yeong (School of Business, Yeungnam University)
Kim, Keun-Wook (Big Data Center, Daegu Digital Industry Promotion Agency)
Publication Information
Journal of Digital Convergence / v.19, no.5, 2021 , pp. 93-103 More about this Journal
Abstract
The special means of transportation introduced to improve the mobility of the transportation vulnerable met the number of legal standards in 2016, but lack of development in terms of quality, such as the existence of long waiting times. In order to streamline the operation of special means of transportation, long-term standby traffic, which is the top 25% of the wait time, was extracted from the Daegu Metropolitan Government's special transportation history data, and spatial autocorrelation analysis and social network analysis were conducted. As a result of the analysis, the correlation between the average waiting time of special transportation users and the space was high. As a result of the analysis of internal degree centrality, the peak time zone is mainly visited by general hospitals, while the off-peak time zone shows high long-term waiting demand for visits by lawmakers. The analysis of external degree centrality showed that residential-based traffic demand was high in both peak and off-peak hours. The results of this study are considered to contribute to the improvement of the quality of the operation of special transportation means, and the academic implications and limitations of the study are also presented.
Keywords
The Mobility Handicapped; Special Transportation; Long-term Standby Demand; Social Network Analysis; Spatial Autocorrelation Analysis;
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