한국항해항만학회지 (Journal of Navigation and Port Research)

  • 제46권1호
  • /
  • Pages.33-41
  • /
  • 2022
  • /
  • 1598-5725(pISSN)
  • /
  • 2093-8470(eISSN)
한국항해항만학회 (Korean Institute of Navigation and Port Research)
권호 표지
DOI QR코드

DOI QR Code

내부트럭 운영 정보를 이용한 컨테이너 터미널 내 교통 속도예측

Prediction of Traffic Speed in a Container Terminal Using Yard Tractor Operation Data

  • 김태광 (부산대학교 대학원) ;
  • 허경영 (부산대학교 대학원) ;
  • 이훈 ((주)토탈소프트뱅크) ;
  • 류광렬 (부산대학교 정보컴퓨터공학부)
  • Kim, Taekwang (Graduate School of Pusan National University) ;
  • Heo, Gyoungyoung (Graduate School of Pusan National University) ;
  • Lee, Hoon (Research Institute, Total Soft Bank Ltd.) ;
  • Ryu, Kwang Ryel (School of Computer Science and Engineering, Pusan National University)
  • 투고 : 2022.01.27
  • 심사 : 2022.02.28
  • 발행 : 2022.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

컨테이너 터미널의 중요한 운영 목표는 선박에 컨테이너를 싣고 내리는 안벽 크레인(QC: quay crane) 작업의 효율을 극대화하는 것이다. QC 작업 효율의 극대화를 위해서는 장치장과 QC 사이를 오가며 컨테이너를 운반하는 내부트럭(YT: yard tractor)의 운행 지연이 최소화되어야 하는데, 터미널 내부의 교통 정체가 이를 어렵게 하는 경우가 많다. 본 논문에서는 YT와 외부트럭이 혼재하여 다니는 터미널에서YT의 운영 데이터만을 기반으로 터미널 내부 교통 속도를 예측하는 모델을 학습하는 방안을 제안한다. 외부트럭에 대한 교통 데이터는 구할 수 없지만, 대신 YT 운영 데이터에는 가까운 미래의 YT 운행 경로에 관한 정보가 포함되어 있어서 교통 예측에 상당한 도움이 된다. 시뮬레이션 실험 결과 제안 방안으로 학습한 모델이 상당히 정확한 수준으로 교통 속도를 예측할 수 있음을 확인하였다.

An important operational goal of a container terminal is to maximize the efficiency of the operation of quay cranes (QCs) that load and/or unload containers onto and from vessels. While the maximization of the efficiency of the QC operation requires minimizing the delay of yard tractors (YT) that transport containers between the storage yard and QCs, the delay is often inevitable because of traffic congestion. In this paper, we propose a method for learning a model that predicts traffic speed in a terminal using only YT operation data, even though the YT traffic is mixed with that of external trucks. Without any information on external truck traffic, we could still make a reasonable traffic forecast because the YT operation data contains information on the YT routes in the near future. The results of simulation experiments showed that the model learned by the proposed method could predict traffic speed with significant accuracy.

키워드

과제정보

이 과제는 부산대학교 기본연구지원사업(2년)에 의하여 연구되었음.

참고문헌

  1. Dijkstra, E. W.(1959), "A note on two problems in connexion with graphs", Numerische Mathematik 1: pp. 269-271. https://doi.org/10.1007/BF01386390
  2. Gawrilow, E., Kohler, E., Mohring, R. H. and Stenzel, B. (2008), "Dynamic Routing of Automated Guided Vehicles in Real-time", Mathematics - Key Technology for the Future, pp. 165-177.
  3. Guo, S., Lin, Y., Feng, N., Song, C. and Wan, H.(2019), "Attention based spatial-temporal graph convolutional networks for traffic flow forecasting", AAAI-19 33: pp. 922-929.
  4. Jugovic A., Hess, S. and Poletan, T.(2011), "Traffic demand forecasting for port services", Promet -Traffic & Transportation, Vol. 23, No. 1, pp. 59-69. https://doi.org/10.7307/ptt.v23i1.149
  5. Kim, T. and Ryu, K. R.(2020), "Optimization of YT dispatching policy for maximizing quay side productivity in container terminals", Korean Institute of Navigation and Port Research Conference, Vol. 44, No. 3, pp. 227-234.
  6. Liao, B., Zhang, J., Wu, C., McIlwraith, D., Chen, T., Yang, S., Guo, Y. and Wu, F.(2018), "Deep sequence learning with auxiliary information for traffic prediction", SIGKDD, pp. 537-546.
  7. Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y. and Wang, Y. (2017), "Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction", Sensors 17(4): p. 818. https://doi.org/10.3390/s17040818
  8. Nadi, A., Sharma, S., Snelder, M., Bakri, T., Lint, H. van. and Tavasszy, L.(2021), "Short-term prediction of outbound truck traffic from the exchange of information in logistics hubs: A case study for the port of Rotterdam", Trans Res Part C 127 (2021) 103111. https://doi.org/10.1016/j.trc.2021.103111
  9. Tang, J., Gao, F., Liu, F. and Chen, X.(2020), "A denoising scheme-based traffic flow prediction model: combination of ensemble empirical mode decomposition and fuzzy c-means neural network", IEEE Access 8: pp. 11546-11559. https://doi.org/10.1109/access.2020.2964070
  10. Tang, J., Liu, F., Zou, Y., Zhang, W. and Wang, Y.(2017), "An improved fuzzy neural network for traffic speed prediction considering periodic characteristic", TITS 18(9): pp. 2340-2350.
  11. Yuan, H. and Li, G.(2021), "A survey of traffic prediction: from spatio-temporal data to intelligent transportation", Data Science and Engineering (2021) 6: pp. 63-85. https://doi.org/10.1007/s41019-020-00151-z