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Prediction of Port Container Throughput Based on PSO Optimization BP Neural Network Model

Published: 25 January 2023 Publication History

Abstract

The development of the container transportation industry has made a huge contribution to the development of the regional economy. Container transportation saves cost and time and increases transportation benefits. Container throughput forecasts are very important for logistics companies, shipping companies, port authorities and shipyards. Such forecasts enable shipping companies and port operators to develop appropriate short- to medium-term strategies to remain competitive, while playing a fundamental role in allocating resources to the market. In this paper, particle swarm algorithm (PSO) is used to optimize the BP neural network model, so as to construct the PSO-BP neural network model, write the PSO-BP code and collect the historical throughput data of Shenzhen Port containers, and set the relevant parameters to use the PSO-BP model to Predicting the container throughput of Shenzhen Port, the experimental results show that the accuracy of the PSO-BP neural network model is higher than that of the unoptimized BP neural network model.

References

[1]
Tang, S., Xu, S., & Gao, J. 2019. An optimal model based on multifactors for container throughput forecasting. KSCE Journal of Civil Engineering, 23(9), 4124-4131.
[2]
Li, M. W., Geng, J., Hong, W. C., & Chen, Z. Y. 2017. A novel approach based on the Gauss-vSVR with a new hybrid evolutionary algorithm and input vector decision method for port throughput forecasting. Neural Computing and Applications, 28(1), 621-640.
[3]
Geng, J., Li, M. W., Dong, Z. H., & Liao, Y. S. 2015. Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm. Neurocomputing, 147, 239-250.
[4]
Huang, A., Lai, K., Li, Y., & Wang, S. 2015. Forecasting container throughput of Qingdao port with a hybrid model. Journal of Systems Science and Complexity, 28(1), 105-121.
[5]
Farhan, J., & Ong, G. P. 2018. Forecasting seasonal container throughput at international ports using SARIMA models. Maritime Economics & Logistics, 20(1), 131-148.
[6]
Koyuncu, K., Tavacioğlu, L., Gökmen, N., & Arican, U. Ç. 2021. Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models. Maritime Policy & Management, 48(8), 1096-1108.
[7]
Zhang, Y., Fu, Y., & Li, G. 2020, September. Research on container throughput forecast based on ARIMA-BP neural network. In Journal of Physics: Conference Series (Vol. 1634, No. 1, p. 012024). IOP Publishing.)
[8]
Awah, P. C., Nam, H., & Kim, S. 2021. Short term forecast of container throughput: New variables application for the Port of Douala. Journal of Marine Science and Engineering, 9(7), 720.(
[9]
WU, L. 2021. Research on the Container Throughput Prediction of Xiamen Port Based on BP Neural Network. Journal of Guangzhou Institute of Navigation (01), 23-25.

Cited By

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  • (2023)Prediction method of surface settlement of rectangular pipe jacking tunnel based on improved PSO-BP neural networkScientific Reports10.1038/s41598-023-32189-013:1Online publication date: 4-Apr-2023

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  1. Prediction of Port Container Throughput Based on PSO Optimization BP Neural Network Model

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    ICIBE '22: Proceedings of the 8th International Conference on Industrial and Business Engineering
    September 2022
    552 pages
    ISBN:9781450397582
    DOI:10.1145/3568834
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 25 January 2023

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    Author Tags

    1. BP Neural Network
    2. Container
    3. Container Throughput
    4. PSO-BP Neural Network
    5. Particle Swarm Algorithm

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    • (2023)Prediction method of surface settlement of rectangular pipe jacking tunnel based on improved PSO-BP neural networkScientific Reports10.1038/s41598-023-32189-013:1Online publication date: 4-Apr-2023

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