基于多变量LSTM模型的青岛港集装箱吞吐量预测被引量：8

Container Throughput Prediction of Qingdao Port Based on Multivariate LSTM Model

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摘要为了更加科学准确地对港口集装箱吞吐量进行预测,以深度学习方法中的长短时记忆网络(LSTM)模型为基础,建立一种多变量输入的LSTM模型。首先使用系统聚类法对青岛港集装箱吞吐量的多种影响因素进行聚类分析,根据普尔逊(Pearson)相关系数计算值选取典型影响因素,其次结合历史集装箱吞吐量数据作为多变量输入到模型中进行预测,并将预测结果与单变量LSTM模型和传统预测模型(ARIMA模型)的预测结果进行比较。结果表明:使用影响因素及历史吞吐量数据作为多变量输入的LSTM模型预测误差减小,平均绝对百分比误差(MAPE)降低到4.170%,均方根误差(RMSE)降低到7.736,预测值更加精确。该模型提高了预测的科学性与准确性,促进深度学习技术在港口集装箱吞吐量预测方面的应用,可为港口的合理决策与规划提供参考。 In order to predict the port container throughput more scientifically and accurately, a multivariate input LSTM model was established based on the long and short-term memory network(LSTM) model in the deep learning method. Firstly, the hierarchical clustering method was used to carry out the cluster analysis of multiple influencing factors of container throughput of Qingdao port. According to the calculated value of Pearson’s correlation coefficient, typical influencing factors were selected. Secondly, combined with the historical container throughput data, it was input into the model as a multivariate for prediction, and the prediction results was compared with those of the univariate LSTM model and the traditional prediction model ARIMA model. The results show that the prediction error of the LSTM model using influencing factors and historical throughput data as multivariate input is reduced, the mean absolute percentage error(MAPE) is reduced to 4.170%, and the root mean square error(RMSE) is reduced to 7.736. Therefore, the predicted value is more accurate. The proposed model improves the scientificity and accuracy of prediction and promotes the application of deep learning technology in port container throughput prediction, which can provide references for reasonable decision-making and planning for ports.

作者王凤武张晓博吉哲王乐 WANG Fengwu;ZHANG Xiaobo;JI Zhe;WANG Le(Navigation College,Dalian Maritime University,Dalian 116026,Liaoning,China)

机构地区大连海事大学航海学院

出处《重庆交通大学学报（自然科学版）》 CAS CSCD 北大核心 2022年第10期54-61,共8页 Journal of Chongqing Jiaotong University(Natural Science)

基金交通运输部北海救助局基金项目(80716025)。

关键词交通运输工程水路运输系统聚类多变量 LSTM模型青岛港集装箱吞吐量预测 traffic and transportation engineering water transportation hierarchical clustering multivariate LSTM model Qingdao port container throughput forecast

分类号 U691.71 [交通运输工程—港口、海岸及近海工程]