Communications for Statistical Applications and Methods

  • 제19권1호
  • /
  • Pages.183-192
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  • 2012
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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KTX 단기수요 예측을 위한 통행행태 분석

Travel Behavior Analysis for Short-Term KTX Passenger Demand Forecasting

  • Kim, Han-Soo (Korail Research Institute, Korea Railroad Corporation) ;
  • Yun, Dong-Hee (Deajeon-Chungnam HQ, Korea Railroad Corporation) ;
  • Lee, Sung-Duk (Department of Information and Statistics, Chungbuk National University)
  • 투고 : 20110600
  • 심사 : 20111100
  • 발행 : 2012.01.30
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초록

본연구는 KTX의 단기수요예측 방향을 설정하기 위한 통행행태 분석이 목적이다. 분석결과는 첫째, 이상치 판단기준은 통행량 표준편차의 2배가 적정한 것으로 판단된다. 둘째, ANOVA 분석을 이용하여 요일별 통행량의 동질여부를 분석한 결과 주중(월~목)과 주말(금~일)로 구분되었다. 셋째, 통행빈도, 통행량균, 통행거리를 이용하여 철도역간 O/D에 대해 군집분석을 시행하였다.

This study analyzes the travel behavior for short-term demand forecasting model of KTX. This research suggests the following. First, the outlier criteria is considered to appropriate twice the standard deviation of the traffic. Second, the result of a homogeneity test using ANOVA analysis has been divided into weekdays(Mon Thu and weekends(Fri Sun). Third, a cluster analysis for O/D pairs using trip frequency, traffic averages and th distance between stations was performed.

키워드

참고문헌

  1. 국토해양부 (2010). <국가철도망구축계획 수립 연구>, 국토해양부.
  2. 권세혁 (2008). <다변량데이터 분석과 활용>, 자유아카데미.
  3. 정강모, 김명근 (2007). , 교우사.
  4. 최태성, 김성호 (2004). 철도여객수요예측을 위한 Holt-Winters모형의 초기값 설정방법 비교, <한국철도학회논문집>, 7, 9-13.
  5. 한국철도공사 연구원 (2010). , 한국철도공사.
  6. Kanafani, A. (1983). Transportation Demand Analysis, McGraw-Hill, New York.
  7. MVA Consultancy (2007). Rail Passenger Demand Forecasting Research.
  8. Rousseeuw, P. J. (1986). A Visual Display for Hierarchical Classification, In Data Analysis and Informatics 4, edited by E. Diday, Y. Escoufier, L. Lebart, J. Pages, Y. Schektman, and R. Tomassone, North- Holland, Amsterdam, 743-748.
  9. Sarle, W. S. (1983). Cubic Clustering Criterion, SAS Technical Report A-108, Cary, NC: SAS Institute Inc.
  10. Tsai, T., Lee, C. and Wei, C. (2005). Design of dynamic neural networks to forecast short-term railway passenger demand, Journal of the Eastern Asia Society for Transportation Studies, 6, 1651-1666.
  11. Wardman, M., Lythgoe, W. and Whelan, G. (2007). Rail passenger demand forecasting: Cross-sectional models revisited, Research in Transportation Economics, 20, 119-152. https://doi.org/10.1016/S0739-8859(07)20005-8

피인용 문헌

  1. Forecasting Passenger Transport Demand Using Seasonal ARIMA Model - Focused on Joongang Line vol.17, pp.4, 2014, https://doi.org/10.7782/JKSR.2014.17.4.307
  2. Analyzing Effects of the Ticket Release Time on Train Reservation Time: Focusing on KTX Gyeongbu-line vol.16, pp.1, 2017, https://doi.org/10.12815/kits.2017.16.1.38