| 引用本文: | 邴其春,龚勃文,杨兆升,林赐云,商强.一种组合核相关向量机的短时交通流局域预测方法[J].哈尔滨工业大学学报,2017,49(3):144.DOI:10.11918/j.issn.0367-6234.2017.03.023 |
| BING Qichun,GONG Bowen,YANG Zhaosheng,LIN Ciyun,SHANG Qiang.A short-term traffic flow local prediction method of combined kernel function relevance vector machine[J].Journal of Harbin Institute of Technology,2017,49(3):144.DOI:10.11918/j.issn.0367-6234.2017.03.023 |
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| 摘要: |
| 为有效提高短时交通流预测的精度，提出一种基于组合核相关向量机模型的短时交通流局域预测方法.首先利用C-C方法实现相空间重构，然后根据Hannan-Quinn准则确定邻近点个数，进而构建基于粒子群优化的组合核相关向量机模型，最后采用上海市南北高架快速路的感应线圈实测数据进行实验验证和对比分析.实验结果表明：基于组合核相关向量机模型的短时交通流局域预测方法的预测误差和均等系数均优于对比方法，其中，平均绝对百分比误差比GKF-RVM模型、GKF-SVM模型和加权一阶局域预测模型分别降低了29.2%、47.5%和59.5%，能够进一步提高短时交通流预测的精度.
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| 关键词: 交通工程 相空间重构 C-C方法 组合核 相关向量机模型 短时交通流预测 |
| DOI：10.11918/j.issn.0367-6234.2017.03.023 |
| 分类号:U491 |
| 文献标识码:A |
| 基金项目:“十二五”国家科技支撑计划(2014BAG03B03)； 国家自然科学基金青年基金(8,7) |
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| A short-term traffic flow local prediction method of combined kernel function relevance vector machine |
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BING Qichun1,2,GONG Bowen1,YANG Zhaosheng1,LIN Ciyun1,SHANG Qiang1
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（1.College of Transportation, Jilin University, Changchun 130022, China; 2.School of Automobile and Transportation, Qingdao Technology University, Qingdao 266520, Shandong, China）
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| Abstract: |
| In order to improve the prediction accuracy of short-term traffic flow effectively, a short-term traffic flow local prediction method based on a combined kernel function relevance vector machine (CKF-RVM) model was proposed.Firstly, the C-C method was used to realize phase space reconstruction.Secondly, the number of neighboring points was determined by use of Hannan-Quinn criteria.Then, the CKF-RVM model was constructed based on particle swarm optimization algorithm.Finally, validation and comparative analysis was carried out using inductive loop data measured from the north-south viaduct in Shanghai.The experimental results demonstrate that the prediction error and the equal coefficient of the proposed method are both superior to the contrastive method.The MAPEs of the proposed method are 29.2%, 47.5% and 59.5% lower than GKF-RVM model, GKF-SVM model and weighted first-order local prediction model, which can further improve the prediction accuracy of short-term traffic flow.
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| Key words: traffic engineering phase space reconstruction C-C method combined kernel function relevance vector machine model short-term traffic flow prediction |
