基于改进灰色马尔可夫的港口货物吞吐量预测研究

doi:10.3969/j.issn.1674-0696.2023.09.18

重庆交通大学学报（自然科学版） ›› 2023, Vol. 42 ›› Issue (9): 130-136.DOI: 10.3969/j.issn.1674-0696.2023.09.18

• 交通基础设施工程 • 上一篇

基于改进灰色马尔可夫的港口货物吞吐量预测研究

丁天明，潘宁，杜柏松，艾万政

(浙江海洋大学船舶与海运学院，浙江舟山 316022)

收稿日期:2022-01-28 修回日期:2022-05-13 发布日期:2023-10-16
作者简介:丁天明(1968—)，男，浙江诸暨人，教授，主要从事航海技术及海事安全方面的研究。E-mail：dtm8302@126.com 通信作者：杜柏松(1984—)，男，山东菏泽人，硕士，船长，讲师，主要从事航海安全保障方面的研究。E-mail:dubaisong@zjou.edu.cn
基金资助:
浙江科技厅公益性项目(2017C33173)；浙江省教育厅一般科研项目(Y202148179)

Forecast of Cargo Throughput in Port Based on Improved Grey Markov

DING Tianming, PAN Ning, DU Baisong, AI Wanzheng

(School of Ship and Maritime Transport, Zhejiang Ocean University, Zhoushan 316022, Zhejiang, China)

Received:2022-01-28 Revised:2022-05-13 Published:2023-10-16

摘要/Abstract

摘要： 为了提高港口货物吞吐量的预测精度，以宁波舟山港为例对灰色马尔可夫组合预测模型进行了优化研究。首先，用中国统计年鉴中宁波舟山港货物吞吐量的历年数据建立灰色GM(1,1)模型；其次，对模拟误差值用一阶马尔可夫链进行修正并确定误差的转移状态，建立复合灰色马尔可夫预测模型；最后，用粒子群算法对该复合模型进行迭代寻优并优化改进，使模型能够根据实际情况对每个灰区间分别进行分析计算，并实时动态更新其区间参数；最终，提高改进后的模型误差精度。结果表明，用粒子群算法改进的灰色马尔可夫模型误差均值下降了37%，预测值与实际值的拟合度更高，预测结果更符合实际情况。

关键词: 灰色预测；马尔可夫理论；粒子群算法；货物吞吐量预测

Abstract: In order to improve the prediction accuracy of port cargo throughput, the grey Markov combination prediction model was optimized and studied by taking Ningbo Zhoushan Port as an example. Firstly, the grey GM (1,1) model was established by using the historical data of the cargo throughput of Ningbo Zhoushan Port in the China Statistical Yearbook. Secondly, the simulated error value was corrected with a first-order Markov chain and the transition state of the error was determined, and a composite grey Markov model was established. Finally, the particle swarm optimization algorithm was used to iteratively optimize and improve the composite model, which enables the proposed model to analyze and calculate each gray interval according to the actual situation, dynamically update its interval parameters in real time and ultimately improve the error accuracy of the improved model. The results show that the mean error of the grey Markov model improved by particle swarm optimization has decreased by 37%, and the predicted value has a higher fit with the actual value, making the predicted results more in line with the actual situation.

Key words: grey prediction; Markov theory; particle swarm optimization; cargo throughput forecast

中图分类号:

U691.71

丁天明，潘宁，杜柏松，艾万政. 基于改进灰色马尔可夫的港口货物吞吐量预测研究[J]. 重庆交通大学学报（自然科学版）, 2023, 42(9): 130-136.

DING Tianming, PAN Ning, DU Baisong, AI Wanzheng. Forecast of Cargo Throughput in Port Based on Improved Grey Markov[J]. Journal of Chongqing Jiaotong University(Natural Science), 2023, 42(9): 130-136.

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基于改进灰色马尔可夫的港口货物吞吐量预测研究

Forecast of Cargo Throughput in Port Based on Improved Grey Markov

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