摘要
为建立适合港口典型货种煤炭、油品、集装箱吞吐量的数据模型,实现精准预测港口吞吐量,本研究中结合直接和迭代预测模型的优点,构建了基于支持向量机模型的多步混合预测方法,并通过对大连港66个月吞吐量样本数据进行了实证分析。结果表明:利用建立的多步混合预测模型计算,煤炭、油品、集装箱的预测值与实际值的平均相对误差分别为5.7%、4.2%、2.8%;相关系数分别为95.5%、95.2%、98.2%。研究表明,本研究中构建的多步混合预测模型参数标定准确、预测精度较高,可为港口经营者掌握港口运转状态及经营决策提供技术支持,在港口多货种吞吐量指标预测中具有可行性。
This paper aims to establish an appropriate data model of the typical port cargo,and to make accurate throughput prediction.A multi-step hybrid prediction model is built by combination of the advantages of direct and iterative prediction mode based on support vector machine(SVM),and was used to empirically analyze 66 monthly cargo throughput data of Dalian Port.It was found that the average relative error between predicted by the multi-step hybrid prediction model and actual values was 5.7%for coal,4.2%for oil,and 2.8%for container,with correlation coefficients of 95.5%for coal,95.2%for oil,and 98.2%for container,indicating that the multi-step hybrid prediction model has accurate parameter calibration,and a relatively high level of prediction veracity.The findings indicate that the model is feasible to predict multiple cargo throughputs,which provides a strong technical support for port operators to grasp the overall port operation condition and to make operating decision.
作者
宋长利
靳廉洁
关峰
林木西
SONG Chang-li;JIN Lian-jie;GUAN Feng;LIN Mu-xi(School of Economics,Liaoning University,Shenyang 110036,China;Transport Planning and Research Institute,Ministry of Transport,Beijing 100028,China;School of Transportation Engineering,Shenyang Jianzhu University,Shenyang 110168,China)
出处
《大连海洋大学学报》
CAS
CSCD
北大核心
2019年第5期752-756,共5页
Journal of Dalian Ocean University
基金
沈阳市社科联项目(SYSK2019-07-31)
教育部哲学社会科学发展报告培养项目(11JBGP010)
关键词
港口吞吐量
支持向量机
多步混合预测模型
实证分析
port throughput prediction
support vector machine
multi-step hybrid prediction model
empirical analysis
