Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Social Network Analysis of Long-term Standby Demand for Special Transportation

특별교통수단 장기대기수요에 대한 사회 연결망 분석

  • 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)
  • 박소연 (영남대학교 정보통신공학과) ;
  • 진민하 (한동대학교 경영학/데이터사이언스학과) ;
  • 강원식 (경북대학교 정치외교학과) ;
  • 박대영 (영남대학교 경영학과) ;
  • 김건욱 (대구디지털산업진흥원 빅데이터활용센터)
  • Received : 2021.02.26
  • Accepted : 2021.05.20
  • Published : 2021.05.28
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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.

교통약자의 이동편의 증진을 위해 도입된 특별교통수단은 2016년 법적 기준대수 충족 등 양적인 발전을 이루었으나, 대기시간의 50% 이상이 30분을 초과하는 등 질적인 측면에서 발전이 부족한 실정이다. 이에 본 연구에서는 특별교통수단의 운영 효율화를 위하여 대구광역시 특별교통수단 승하차 이력 자료 중 대기시간이 상위 25%에 해당하는 장기대기통행을 추출하여 공간자기상관 분석과 사회 연결망 분석을 수행하였다. 분석 결과 특별교통수단 이용자들의 평균 대기시간과 공간과의 상관관계는 높은 것으로 나타났으며, 일부 도서산간 지역에서 개선지역을 도출하였다. 내향연결 중심성 분석 결과 첨두시간대는 종합병원, 복지관 방문이 주를 이루는 반면, 비첨두시간대는 터미널/역사, 주거지 인근의 의원 방문의 장기대기수요가 높게 나타났다. 외향 연결 중심성 분석은 첨두시간대와 비첨두시간대 모두 주거지 기반의 수요가 높게 나타났다. 본 연구의 결과는 특별교통수단 운영의 질적 개선과 교통약자 이동권 개선에 기여할 것으로 판단되며, 연구의 학술적 함의와 한계점 또한 제시하였다.

Keywords

References

  1. Third transportation convenience improvement plan for the weak. (2016.12). Ministry of Land, Infrastructure and Transport, 2017-90, 1-64.
  2. S. K. An & C. J. Kwak. (2020). Evaluation of Operations Efficiency in Special Transportation Services for the Transportation Vulnerable. Information systems review, 22(1), 307-325.
  3. K. W. Kim, D. S Yun & J. J. Kim. (2020). Travel Demand Analysis of Special Transportation Systems for the Transportation Vulnerable using Big Data: A Case Study of Daegu Metropolitan City. Journal of Daegu Gyeongbuk development institute, 19(2), 43-61.
  4. J. W. Park, J. T. Kim, S. M. Kim & J. Y. Kim. (2020). Effectiveness of decentralized management of special transportation systems for the elderly and disabled - A case study of Namyangju city. Journal of Korean Society of Road Engineers, 22(1), 95-104.
  5. B. H. Lee & H. T. Yang. (2017). Study on analysis of current usage big data of special transport service of person with disabilities focused on Seoul City and Gyeonggi-do. Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, 7(10), 925-934. DOI : 10.14257/ajmahs.2017.10.55
  6. D. S. Yun & G. S. Sin. (2010). Characteristics and Mode Choice Behavior of the Transportation Handicapped: A Case Study of Gyeongsan City. The Korea Spatial Planning Review, 66(-), 25-45 https://doi.org/10.15793/kspr.2010.66..002
  7. J. W. Park, J. T. Kim, S. M. Kim & J. Y. Kim. (2020). Social Network Analysis of Shared Bicycle Usage Pattern Based on Urban Characteristics: A Case Study of Seoul Data. Korean Society of Road Engineers, 22(1), 147-165. DOI : 10.14329/isr.2020.22.1.147
  8. H. Park, S. H. Bae & S. I. Park. (2016). Properties of a Social Network Topology of Livestock Movements to Slaughterhouse in Korea. Journal of Veterinary Clinics, 33(5), 278-285. DOI : 10.17555/jvc.2016.10.33.5.278
  9. T. J. Sin, S. Kim & S. Y. Jung. (202). Effects of the COVID-19 spread on the Northeast Asia Airport Network Centrality: Using Social Network Analysis. Journal of Digital Convergence, 18(5), 179-186. DOI : 10.14400/JDC.2020.18.5.179
  10. S. H. Lee & Y. G. Oh. (2017). Analysis of the Spatio-temporal Migration and Degree Centrality of Migration Network. Journal of the Korean Society of Agricultural Engineers, 59(5), 1-15. DOI : 10.5389/KSAE.2017.59.5.001
  11. Prabhakar, N. & Anbarasi, L.J. (2021). Exploration of the global air transport network using social network analysis. Korean Soc. Netw. Anal. Min, 11(26), 1-12. DOI : 10.1007/s13278-021-00735-1
  12. Ali Lakhania et al. (2019). Disability support services in Queensland, Australia: Identifying service gaps through spatial analysis. Applied Geography, 110(-), 1-27. DOI : 10.1016/j.apgeog.2019.102045
  13. K. K. Ko. (2007). The Review of Studies on Policy Network and the Application of Social Network Analysis. Korean Journal of Public Administration, 45(1), 137-164.
  14. S. K. Kang, H. Yu & Y. J. Lee. (2016). Analyzing Disaster Response Terminologies by Text Mining and Social Network Analysis. Information systems review, 18(1), 141-155. DOI : 10.14329/isr.2016.18.1.141
  15. S. S. Lee. (2012). Network Analysis Methodology. Seoul : nonhyungbook.
  16. H. J. Chun & B. H. Leem. (2014). Face/non-face channel fit comparison of life insurance company and non-life insurance company using social network analysis. Journal of the Korean data & information science society, 25(6), 1207-1219. DOI : 10.7465/jkdi.2014.25.6.1207
  17. C. G. Lee, M. J. Sung & Y. B. Lee. (2011). Discovering Customer Service Cool Trends in e-Commerce: Using Social Network Analysis with NodeXL. Information systems review, 13(1), 75-96.
  18. K. Y. Kwak. (2017). Social Network Analysis. crbook.
  19. J. W. Ko, C. M. Cho, S. H. Kim & W. H. Jung. (2015). A Study of Coastal Passenger Ship Routes through Social Network Analysis Method. Journal of Korean Navigation and Port Research, 39(3), 217-222. DOI : 10.5394/KINPR.2015.39.3.217
  20. M. I. Kim, H. B. Kwak, W. K. Lee, M. S. Won & K. S. Koo. (2011). Study on Regional Spatial Autocorrelation of Forest Fire Occurrence in Korea. Journal of Korean Society for Geospatial Information Science, 19(2), 29-37.
  21. K. W. Kim, S. H. Son, M. Y. Yang & S. H. Lee. (2020). Frequency of Special Transportation Estimation Model Using Deep Learning(Nadri Call). Korean Society of Transportation, 17(2), 43-51.
  22. H. S. Lee. (2020). Everyday life difficulties of persons with disabilities on quality (CQR) research. Journal of Digital Convergence, 12(12), 561-570. DOI : 10.14400/JDC.2014.12.12.561