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计算机系统应用英文版:2021,30(1):243-249
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基于SSA-LightGBM的交通流量调查数据趋势预测
(长安大学 信息工程学院, 西安 710064)
Traffic Volume Survey Data Trend Prediction Based on SSA-LightGBM
(School of Information Engineering, Chang’an University, Xi’an 710064, China)
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Received:June 02, 2020    Revised:June 30, 2020
中文摘要: 为了解决传统模型和机器学习模型对周期时间序列预测效果欠佳的问题, 本文以韩城高速公路交通流量调查数据为数据集, 提出了SSA-LightGBM交通流量调查数据预测模型. 对韩城高速数据进行当量计算, 然后对当量数据进行奇异谱分解得到周期项和随机项, 对周期项进行信号重建, 利用LightGBM预测随机项, 最后将预测随机项与周期延拓信号进行叠加得到最终的高速当量预测数据. 同时与XGBoost和LightGBM预测结果作对比, SSA-LightGBM预测结果与真实值最为贴近, 且MAERMSER2均优于XGBoost和LightGBM模型. 该结果对我国高速公路交通调查数据未来的变化趋势预测研究具有很好的指导意义, 可以为我国高速公路的整修和养护提供很好的参考价值.
Abstract:In order to solve the problem that the traditional model and the machine learning model have poor performance in predicting the periodic time series, this study proposes the SSA-LightGBM prediction model which takes the Hancheng expressway traffic volume survey data as the data set. First, the Passenger Car Unit (PCU) of Hancheng expressway data is calculated. Second, the singular spectral analysis is applied on the PCU data to obtain periodic and random terms, the periodic term is reconstructed, and then the LightGBM is used to predict the random term. Finally, the predicted random term and the periodic extension signal are superimposed to obtain the prediction data of final expressway PCU. At the same time, compared with the prediction results of XGBoost and LightGBM model, the prediction results of SSA-LightGBM are closest to the true values, and MAE, RMSE and R2 are better. This result has a good guiding significance for the research of the forecast on future change trend of our country’s expressway volume survey data, and provides a good reference value for the renovation and maintenance of our country’s expressways.
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基金项目:陕西省交通运输厅交通科研项目(18-22R); 国家重点研发计划(2018YFB1600202); 高新技术研究培育项目(300102240201)
引用文本:
徐磊,孙朝云,李伟,杨荣新.基于SSA-LightGBM的交通流量调查数据趋势预测.计算机系统应用,2021,30(1):243-249
XU Lei,SUN Zhao-Yun,LI Wei,YANG Rong-Xin.Traffic Volume Survey Data Trend Prediction Based on SSA-LightGBM.COMPUTER SYSTEMS APPLICATIONS,2021,30(1):243-249