DOI QR코드

DOI QR Code

Analysis of Global Shipping Market Status and Forecasting the Container Freight Volume of Busan New port using Time-series Model

글로벌 해운시장 현황 분석 및 시계열 모형을 이용한 부산 신항 컨테이너 물동량 예측에 관한 연구

  • JO, Jun-Ho (BigData Specialist Dept., Namseoul University) ;
  • Byon, Je-Seop (BigData Specialist Dept., Namseoul University) ;
  • Kim, Hee-Cheul (Department of Industrial & Management Engineering, Namseoul University)
  • Received : 2017.07.11
  • Accepted : 2017.08.28
  • Published : 2017.08.30

Abstract

In this paper, we analyze the trends of the international shipping market and the domestic and foreign factors of the crisis of the domestic shipping market, and identify the characteristics of the recovery of the Busan New Port trade volume which has decreased since the crisis of the domestic shipping market We quantitatively analyzed the future volume of Busan New Port and analyzed the trends of the prediction and recovery trends. As a result of analyzing Busan New Port container cargo volume by using big data analysis tool R, the variation of Busan New Cargo container cargo volume was estimated by ARIMA model (1,0,1) (1,0,1)[12] Estimation error, AICc and BIC were the most optimal ARIMA models. Therefore, we estimated the estimated value of Busan New Port trade for 36 months by using ARIMA (1, 0, 1)[12], which is the optimal model of Busan New Port trade, and estimated 13,157,184 TEU, 13,418,123 TEU, 13,539,884 TEU, and 4,526,406 TEU, respectively, indicating that it increased by about 2%, 2%, and 1%.

본 논문에서는 최근 국제 해운시장의 동향과 국내 해운시장의 위기설에 대한 국내외적 요인을 정성적으로 파악하고, 국내 해운시장의 위기 이후 감소한 부산 신항의 물동량이 다시 회복세를 보일 수 있는 특성요인을 파악하고자 부산 신항의 향후 물동량에 대해 정량적으로 분석하여 사전적 예측추이의 파악과 회복세 추이를 분석하였다. 빅데이터 분석 툴인 R을 활용하여 부산 신항 컨테이너 물동량을 분석한 결과, 부산 신항 컨테이너 물동량의 변동은 승법계절 ARIMA 모델 (1,0,1)(1,0,1)[12]로 추정하였을 때, 추정오차와 AICc, BIC기준으로 가장 최적의 ARIMA모형인 것으로 나타났다. 따라서 부산 신항 물동량 추정의 최적의 모델인 ARIMA (1,0,1)(1,0,1)[12]에 의해 향후 36개월간의 부산 신항 물동량을 추정치를 예측한 결과, 13,157,184 TEU, 13,418,123 TEU, 13,539,884 TEU, 4,526,406 TEU 등으로 약 2%, 2%, 1%정도 증가하는 것으로 나타났다.

Keywords

References

  1. Jun-mo Jeon, "Causes and future prospects of domestic shipping industry crisis", IBK industry bank Economy Research, pp.4-17, 2016
  2. Ghae-Deug Yi, "Forecasting the Container Throughput of the Busan Port using a Seasonal Multiplicative ARIMA Model", Journal of Korea Port Economics Association, Vol. 29, No. 3, pp.2, 2013
  3. http://www.thescoop.co.kr/news/articleView.html?idxno=22673
  4. http://www.breaknews.com/sub_read.html?uid=417312
  5. Hang-Jin Yang, Bong-Gyu Jang, Du-sik Jeong, "A development Strategy for Hub-port in Korea" Journal of Korea Port Economics Association Vol. 21, No. 1, pp.24, 2005
  6. Ghae-Deug Yi, "Forecasting the Cargo Transportation for the North Port in Busan, uing Time Series Modelsl", Journal of Korea Port Economics Association Vol. 24, No. 2, 2008
  7. Chang-Beom Kim, "Forecasting the Seaborne Trade Volume using Intervention Multiplicative Seasonal ARIMA and Artificial Neural Network Model", Journal of Korea Port Economics Association, Vol. 31, No. 1, pp.5-20, 2015
  8. https://www.ksg.co.kr/news/news_print.jsp?bbsID=news&bbsCategory=KSG&pNum=105662
  9. A Study on the Global Shipping Market Conditions and Prediction of Major Shipper's Default Rate, The Korean Assciation of Shipping and Logistics, pp.7-10, 2017
  10. http://en.sse.net.cn/indices/ccfinew.jsp
  11. http://blog.naver.com/PostView.nhn?blogId=jsrsabre&logNo=220701031466
  12. No-gyeong Gwak, "Current status and prospect of shipping industry I.National and Oceanic Container Shipping Crisis and Competitiveness-Focusing on comparison with major global shipping companies", Special Report of NICE Investors Service1, pp.2-27, 2016
  13. http://news20.busan.com/controller/newsController.jsp?newsId=20160201000161
  14. http://www.pncport.com/html/03/0202.php
  15. http://m.mt.co.kr/renew/view.html?no=2017022815202872750&MVJ
  16. http://www.bpa-net.com/
  17. http://www.dodomira.com/2016/04/21/arima_in_r/
  18. https://datascienceschool.net/view-notebook/e4b52228ac5749418d51409fdc4f9cef/
  19. https://m.blog.naver.com/PostView.nhn?blogId=risk_girl&logNo=220834418182&proxyReferer=https%3A%2F%2Fwww.google.co.kr%2F
  20. Jong-San Choi, "Evaluation of Estimation and Forecast Accuracy on Retail Meat Prices by Seasonal Time-Series Models", The korean of food preservation Vol. 33, No. 1, pp.10-13, 2016

Cited by

  1. LSTM을 활용한 부산항 컨테이너 물동량 예측 vol.36, pp.2, 2020, https://doi.org/10.38121/kpea.2020.06.36.2.53