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计算机系统应用:2020,29(3):132-139
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基于Seq2Seq模型的港口进出口货物量预测
(1.北京交通大学 计算机与信息技术学院 交通数据分析与挖掘北京市重点实验室, 北京 100044;2.天津市商务局 口岸平台处, 天津 300040;3.北京交通大学 综合交通运输大数据应用技术交通运输行业重点实验室, 北京 100044)
Prediction on Import and Export Goods Volume of Ports Based on Seq2Sseq Model
(1.Beijing Key Laboratory of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;2.Tianjin Port Platform Office, Commission of Commerce, Tianjin 300040, China;3.Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)
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投稿时间:2019-07-17    修订日期:2019-08-22
中文摘要: 港口进出口货物吞吐量是反映港口业务状况的重要指标,其准确预测将给港口经营管理人员进行决策提供重要的依据.利用机器翻译领域的Seq2Seq模型,对影响港口进出货物量的多种因素进行建模.Seq2Seq模型可以反映进出口货物量在时间维度上的变化规律,并且可以刻画天气、节假日等外部因素的影响,从而进行精准预测.Seq2Seq模型包含两个由循环神经网络(LSTM)组成的编码器和解码器,能够捕捉长短期时间范围内集装箱变化趋势,可以根据历史进出口货物量预测未来一段时间的货物量信息.在真实的天津港进出口集装箱数据集上进行了实验,结果表明Seq2Seq模型的深度学习预测方法效果优于传统的时间序列模型以及其他现有的机器学习预测模型.
Abstract:The port amount of import and export goods can reflect the congestion of port flow, whose accurate prediction would provide suggestions for port management to make reasonable decisions. In this study, the Seq2Seq model in the field of machine translation is used to model various factors that affect the amount of goods inflow and outflow from the port. An Seq2Seq model can reflect the change of the amount of import and export goods in the time dimension and describe the influence of external factors such as weather and holidays, so as to make accurate predictions. An Seq2Seq model consists of two LSTM, respectively acting as an encoder and a decoder. It can capture the changing trend of containers in the short and long term and predict the amount of goods in the future based on historical import and export volume. Experiments were carried out on a real-world dataset of import and export containers in Tianjin Port. The experimental result reveals that the deep learning prediction model based on Seq2Seq is more effective and efficient than traditional time series model as well as other existing machine learning prediction models.
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基金项目:中央高校基本科研业务费(2019JBM024)
引用文本:
王涛,张伟,贾宇欣,林友芳,万怀宇.基于Seq2Seq模型的港口进出口货物量预测.计算机系统应用,2020,29(3):132-139
WANG Tao,ZHANG Wei,JIA Yu-Xin,LIN You-Fang,WAN Huai-Yu.Prediction on Import and Export Goods Volume of Ports Based on Seq2Sseq Model.COMPUTER SYSTEMS APPLICATIONS,2020,29(3):132-139

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