Journal of the Korean Society of Marine Environment & Safety (해양환경안전학회지)

The Korean Society of Marine Environment and safety (해양환경안전학회)
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A Forecast Method of Marine Traffic Volume through Time Series Analysis

시계열 분석을 통한 해상교통량 예측 방안

  • Received : 2013.08.28
  • Accepted : 2013.12.26
  • Published : 2013.12.31
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Abstract

In this study, time series analysis was tried, which is widely applied to demand forecast of diverse fields such as finance, economy, trade, and so on, different from previous regression analysis. Future marine traffic volume was forecasted on the basis of data of the number of ships entering Incheon port from January 1996 to June 2013, through courses of stationarity verification, model identification, coefficient estimation, and diagnostic checking. As a result of prediction January 2014 to December 2015, February has less traffic volume than other months, but January has more traffic volume than other months. Also, it was found out that Incheon port was more proper to ARIMA model than exponential smoothing method and there was a difference of monthly traffic volume according to seasons. The study has a meaning in that future traffic volume was forecasted per month with time series model. Also, it is judged that forecast of future marine traffic volume through time series model will be the more suitable model than prediction of marine traffic volume with previous regression analysis.

본 연구는 기존의 회귀분석과는 달리 금융, 경제, 무역 등 다양한 분야의 수요 예측에 널리 적용되고 있는 시계열 분석 방법을 시도하였다. 인천항의 1996년 1월부터 2013년 6월까지 입항 척수 자료를 바탕으로 정상성 검증, 모형의 식별, 모수의 추정, 진단 과정을 거쳐 장래 해상교통량을 예측하였다. 2014년 1월부터 2015년 12월까지 예측한 결과 2월달의 교통량이 다른 달 보다 적게 예측된 반면, 1월달의 교통량은 다른 달 보다 많을 것으로 나타났다. 또한 인천항은 지수평활법 보다 ARIMA 모형이 적합하며, 계절에 따라 월별 교통량의 차이를 보이는 것을 알 수 있다. 본 연구는 시계열 분석으로 장래 교통량을 월별로 예측하였다는 점에서 의의가 있다. 또한 기존의 회귀분석으로 예측한 장래 해상교통량보다 시계열 분석으로 예측한 장래 해상교통량이 더 적합한 모형인 것으로 판단된다.

Keywords

References

  1. Box, G. E. P., G. M. Jenkins and G. C. Reinsel(2008), Time Series Analysis: Forecasting and Control, A John Wiley and Sons Inc., pp. 79-86, pp. 103-113.
  2. Fujii, Y., M. Tsutomu and H. Kiyosshi(1981), Marine Traffic Engineering, Haemoondang, pp. 12-13, p. 45.
  3. Kim, J. H., S. G. Gug and M. C. Kim(2006), Estimation on the Future Traffic Volumes and Analysis on Crossing Situation Risk for Gamcheon Harbor, Journal of Korea Navigation and Port Research, Vol. 30, No. 8, pp. 617-622. https://doi.org/10.5394/KINPR.2006.30.8.617
  4. Kim, J. H., S. G. Gug and S. W. Kim(2007), Estimation on the Future Traffic Volumes and Analysis on Information Value of Tidal Current Singnal in Incheon, Journal of Korea Navigation and Port Research, Vol. 31, No. 6, pp. 456-462. https://doi.org/10.5394/KINPR.2007.31.6.455
  5. Kim, J. H.(2007), The Estimation of Future Container Ship Traffic for Three Major Ports in Korea, Journal of Korean Navigation and Port Research, Vol. 31, No. 5, pp. 353-359. https://doi.org/10.5394/KINPR.2007.31.5.353
  6. Kim, J. H.(2008a), The Forecast of the Cargo Transportation and Traffic Volume on Container in Gwangyang Port using Time Series Models, Journal of Navigation and Port Research, Vol. 32, No. 6, pp. 425-431. https://doi.org/10.5394/KINPR.2008.32.6.425
  7. Kim, J. H.(2008b), The Forecast of the Cargo Transportation for the North Port in Busan using Time Series Models, Journal of Korea Port Economic Association, Vol. 24, No. 2, pp. 1-17.
  8. Koo, J. Y.(1997), Evaluation of Traffic Congestion in Channels within Harbour Limit, Journal of Port and Harbor Research, Vol. 11, No. 2, pp. 173-189.
  9. Lee, Y. S. and Y. J. Ahn(2013), A Study on the Standard Ship's Length of Domestic Trade Port, Journal of the Korean Society of Marine Environment & Safety, Vol. 19, No. 2, pp. 164-170. https://doi.org/10.7837/kosomes.2013.19.2.164
  10. Mo, S. W.(2010), Forecasts of the 2011-BDI Using the ARIMA Type Models, Journal of Korea Port Economic Association, Vol. 26, No. 4, pp. 207-218.
  11. Mo, S. W.(2012), The Behavioral Analysis of the Trading Volumes of Gwangyang Port: Comparison with Incheon and Pyeongtaek-Dangjin Port, Journal of Korea Port Economic Association, Vol. 28, No. 3, pp. 111-125.
  12. Mo, S. W.(2013), A Forecast of Shipping Business during the Year of 2013, Journal of Korea Port Economic Association, Vol. 29, No. 1, pp. 67-76.
  13. Park, S. B., J. W. Kim, S. I. Jeon, C. U. Kim, E. J. Choi, C. H. Lee and Y. S. Heo(2012), An Effective Demand Forecasting and Its Applications, Samsung Economic Research Institute, Reserch Report, pp. 24-26.
  14. Stein, R. and P. Shaman(1989), A Fixed Point Characterization for Bias of Auto-Regressive Estimators, The Analysis of Statistics, Vol. 17, No. 3, pp. 1275-1284.
  15. Shin, C. H., J. S. Kang, S. N. Park and J. H. Lee(2008), A Study on the Forecast of Port Traffic using Hybrid ARIMA-Neural Network Model, Journal of Navigation and Port Research, Vol. 32, No. 1, pp. 81-88. https://doi.org/10.5394/KINPR.2008.32.1.081
  16. Shin, C. H. and S. H. Jeong(2011), A Study on Application of ARIMA and Neural Networks for Time Series Forecasting of Port Traffic, Journal of Navigation and Port Research, Vol. 35, No. 1, pp. 83-91. https://doi.org/10.5394/KINPR.2011.35.1.83
  17. Yeo, G. T., H. G. Lee, S. M. Soak and C. Y. Lee(1998), A Simulation Study on the Marine Traffic Congestion in Pusan Port, Journal of Port and Harbor Research, Vol. 9, pp. 1-17.

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