利用数据分组技术提高GM(1,1)的准确性及其在日本德岛市机动车量和CO2排放预测中的应用

IF 0.7 4区计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS

Modeling Identification and Control Pub Date : 2017-01-01 DOI:10.2316/P.2017.848-016

Vincent B. Getanda, H. Oya, T. Kubo

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引用次数: 4

摘要

本文研究了提高灰色模型GM(1,1)在交通流和二氧化碳排放预测中的精度问题。为了提高预测精度，我们采用了GM(1,1)的数据分组技术，建立了一个分组GM(1,1) (GGM(1,1))。利用累积发电操作(AGO)和逆累积发电操作(IAGO)技术对日本德岛市11号国道的训练数据进行分析，考察GM(1,1)和GGM(1,1)预测车流量和CO2排放量的准确性。因此，本文为进一步发展和提高GM(1,1)的预测精度做出了贡献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Improving the Accuracy of the GM(1,1) by Data Grouping Technique and Its Application to Forecast Vehicle Volume and CO2 Emission in Tokushima City, Japan

This paper deals with the problem for improving the accuracy of the grey model (GM(1,1)) in traffic flow and CO2 emission prediction. In order to improve the prediction accuracy, we adopt a data grouping technique along with the GM(1,1) and a Grouped GM(1,1) (GGM(1,1)) is established. Moreover, by applying techniques of accumulated generating operation (AGO) and inverse accumulated generating operation (IAGO) on training data collected from national route 11 of Tokushima City, Japan, the accuracy of GM(1,1) and GGM(1,1) in forecasting vehicle volume and CO2 emissions is investigated. Therefore, in this paper we contribute to develop and enhance the GM(1,1)’s accuracy in prediction.

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来源期刊

Modeling Identification and Control 工程技术-计算机：控制论

CiteScore

3.30

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The aim of MIC is to present Nordic research activities in the field of modeling, identification and control to the international scientific community. Historically, the articles published in MIC presented the results of research carried out in Norway, or sponsored primarily by a Norwegian institution. Since 2009 the journal also accepts papers from the other Nordic countries.