PSO 및 Dual stage attention 기반 항만 컨테이너 물동량 예측 Framework 개발: 부산항 중심으로
Port Container Volume Prediction Framework with PSO and Dual-stage Attention: The Case of Busan Port
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Port logistics is a significant element determining national competitiveness in a country with a high dependence on trade, like South Korea. This study predicted the container throughput of Busan Port, which accounts for 75% of South Korea's total container transport volume, by considering the container volumes of other major ports, weather near the ports, industrial indices, and exchange rates. This study proposes a new prediction framework comprising a dual-attention-based long short-term memory (LSTM) model and Particle Swarm Optimization (PSO). This model ensures higher explainability than traditional models because it can identify which variable is given more attention. This model showed a higher prediction accuracy than the ARIMA and the LSTM models, while the predicted values better reflected the characteristics of the measured values.
Abstract
Port logistics is a significant element determining national competitiveness in a country with a high dependence on trade, like South Korea. This study predicted the container throughput of Busan Port, which accounts for 75% of South Korea's total container transport volume, by considering the container volumes of other major ports, weather near the ports, industrial indices, and exchange rates. This study proposes a new prediction framework comprising a dual-attention-based long short-term memory (LSTM) model and Particle Swarm Optimization (PSO). This model ensures higher explainability than traditional models because it can identify which variable is given more attention. This model showed a higher prediction accuracy than the ARIMA and the LSTM models, while the predicted values better reflected the characteristics of the measured values.
참고문헌 (32)
- 김가현, "타부두 환적 내부도로 활용을 위한 법⋅정책⋅운영적 방안 연구: 부산항 신항을 중심으로", 로지스틱스연구, 31(4), pp.83~99 (2023), 한국로지스틱스학회[DOI]
- 김두환, "LSTM을 활용한 부산항 컨테이너 물동량 예측", 한국항만경제학회지, 36(2), pp.53~62 (2020), 한국항만경제학회[DOI]
- 김종길, "A Study on forecasting container volume of port using SD and ARIMA", 한국항해항만학회지, 35(4), pp.343~349 (2011), 한국항해항만학회
- 민경창, "SARIMA 모형을 이용한 우리나라 항만 컨테이너 물동량 예측", 대한교통학회지, 32(6), pp.600~614 (2014), 대한교통학회[DOI]
- 박성영, "신경망을 이용한 컨테이너 물동량 예측에 관한 연구", 한국항해항만학회지, 26(2), pp.183~188 (2002), 한국항해항만학회
- 손용정, "의사결정나무분석을 이용한 컨테이너 수출입 물동량 예측", 한국항만경제학회지, 28(4), pp.193~207 (2012), 한국항만경제학회
- 신창훈, "하이브리드 ARIMA-신경망 모델을 통한 컨테이너물동량 예측에 관한 연구", 한국항해항만학회지, 32(2), pp.81~88 (2008), 한국항해항만학회
- 양혁준, "글로벌 컨테이너 터미널 운영사의 자본구조 결정요인에 관한 연구", 로지스틱스연구, 28(5), pp.57~67 (2020), 한국로지스틱스학회[DOI]
- 이재득, "승법계절 ARIMA 모형에 의한 부산항 컨테이너 물동량 추정과 예측", 한국항만경제학회지, 29(3), pp.1~23 (2013), 한국항만경제학회
- 진형인, "글로벌 공급 사슬 관리의 항만 거점화와 항만 배후단지 활용 -인천항을 중심으로-", 로지스틱스연구, 19(1), pp.43~74 (2011), 한국로지스틱스학회[DOI]
- Review of Maritime Transport 2020, "UNCTAD", United Nations, ~ (2020)
- Bahdanau, D., "Neural machine translation by jointly learning to align and translate", arXiv preprint arXiv:1409.0473, ~ (2014)
- Banks A, "A review of particle swarm optimization. Part I: background and development", Natural Computing, 6, pp.467~484 (2007)
- Chen, S. H., "Forecasting container throughputs at ports using genetic programming", Expert Systems with Applications, 37(3), pp.2054~2058 (2010)
- Cho, K., "On the properties of neural machine translation : Encoder-decoder approaches", arXiv preprint arXiv:1409.1259, ~ (2014)
- Cho, K., "Learning phrase representations using RNN encoder-decoder for statistical machine translation", arXiv preprint arXiv:1406.1078, ~ (2014)
- De Alwis, S., "Duo attention with deep learning on tomato yield prediction and factor interpretation", PRICAI 2019: Trends in Artificial Intelligence: 16th Pacific Rim International Conference on Artificial Intelligence, pp.704~715 (2019), Springer
- Hochreiter, S., "Long short-term memory", Neural computation, 9(8), pp.1735~1780 (1997)
- Huang, F., "Port container throughput forecast based on ABC optimized BP neural network", IOP Conference Series: Earth and Environmental Science, 571(1), pp.012068~ (2020)
- Huang, J., "Container Throughput Forecasting for International Ports in Taiwan", Journal of Marine Science and Technology, 28(5), pp.456~469 (2020)
- Kim, T. Y., "Artificial neural networks for non-stationary time series", Neurocomputing, 61, pp.439~447 (2004)
- Kunnapapdeelert, S., "Thailand port throughput prediction via particle swarm optimization based neural network", Journal of Applied Engineering Science, 18(3), pp.338~345 (2020)
- Meersman, H., "Port pricing : considerations on economic principles and marginal costs", European Journal of Transport and Infrastructure Research, 3(4), pp.371~386 (2003)
- Narasingam, A., "Data-driven identification of interpretable reduced-order models using sparse regression", Computers & Chemical Engineering, 119, pp.101~111 (2018)
- Peng, J., "Dual-stage attention-based long-short-term memory neural networks for energy demand prediction", Energy and Buildings, 249, pp.111211~ (2021)
- Peng, WY., "A comparison of univariate methods for forecasting container throughput volumes", Mathematical and Computer Modelling, 50(7-8), pp.1045~1057 (2009)
- Qin, Y., "A dual-stage attention-based recurrent neural network for time series prediction", International Joint Conference on Artificial Intelligence, pp.2627~2633 (2017)
- Rebs, T., "System dynamics modeling for sustainable supply chain management : A literature review and systems thinking approach", Journal of cleaner production, 208, pp.1265~1280 (2019)
- Song, J., "Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation", Networks and Spatial Economics, 20, pp.675~702 (2020)
- Yang, C. H., "Forecasting the demand for container throughput using a mixed-precision neural architecture based on CNN-LSTM", Mathematics, 8(10), pp.1784~ (2020)
- Zhang, Y., "Research on Container Throughput Forecast Based on ARIMA-BP Neural Network", Journal of Physics : Conference Series, 1634(1), pp.012024~ (2020)
- Zhang, Y., "Residual dense network for image restoration", IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(7), pp.2480~2495 (2020)