| 王逸文,王维莉,陈怡霏,毛蔚敏,刘贤超.基于奇异谱分析的长时交通流混合预测模型[J].电子测量与仪器学报,2022,36(11):98-106 |
| 基于奇异谱分析的长时交通流混合预测模型 |
| Hybrid prediction model of long-time traffic flowbased on singular spectrum analysis |
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| DOI： |
| 中文关键词: 奇异谱分析 支持向量回归 极限学习机 交通流预测 |
| 英文关键词:singular spectrum analysis support vector regression extreme learning machine traffic flow forecasting |
| 基金项目:国家自然科学基金（71904116）、上海市科技创新行动计划项目（19DZ1209600）资助 |
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| 中文摘要: |
| 长时交通流预测是综合交通运输系统规划的重要组成部分,也是宏观交通流管理政策制定的重要依据。 针对时序预测
中存在较多噪声及单一模型预测效果不稳定等问题,提出了一种基于奇异谱分析(SSA)的混合预测模型,以提高实际应用中交
通流序列预测的精度与效率。 首先将原始数据经过奇异谱分析后重构为趋势项、周期项和残差项,其中趋势项运用支持向量回
归(SVR)进行预测,并引入灰狼优化(GWO)算法对模型参数进行优化,周期项利用带遗忘机制的在线序列极限学习机(FOSELM)预测,最后叠加两部分得到预测结果。 以真实交通流数据开展实验,本文所提出的混合预测模型的平均绝对误差为
215. 15,均方根误差为 278. 51。 整体结果表明,该模型能够解决单一模型预测结果误差波动大、预测效果不稳定等问题;相比经
验模态分解(EMD)以及未经处理的时间序列,各模型对经过奇异谱分析的时间序列的预测误差均有所减小,进一步证实了奇
异谱分析在时间序列分解中的有效性。 |
| 英文摘要: |
| Long-term traffic flow prediction is a vital part of comprehensive transportation system planning, it is also an important basis
for formulating macro traffic flow management policy. In order to improve the accuracy and efficiency of traffic sequence prediction, a
hybrid prediction model based on singular spectrum analysis (SSA) is proposed, in which the problems of noise and unstable prediction
of a single model in time series prediction are well solved. Firstly, raw data are reconstructed into tendency term, periodic term and
residual term using SSA. Specifically, trend term is predicted using support vector regression ( SVR), and grey wolf optimization
(GWO) algorithm is introduced to optimize parameters of the regression model. Moreover, periodic term is predicted using forgetting
online sequential-extreme learning machine ( FOS-ELM). Finally, the predicted results are obtained by superimposing the above
mentioned two parts. The experiment is carried out with real traffic flow data, and the mean absolute error (MAE) and root mean square
error (RMSE) of the proposed hybrid prediction model are 215. 15 and 278. 51, respectively. The overall results show that the proposed
model can solve the problems of large error fluctuation and unstable prediction of the single model. Furthermore, compared with
empirical mode decomposition (EMD) and unprocessed time series, the prediction error of each model to the time series after singular
spectrum analysis is reduced, which further illustrates the effectiveness of SSA in time series decomposition. |
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