| 基于主成分分析的道路交通事故预测 |
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| 引用本文： | 闫建华,宇仁德,汪洋. 基于主成分分析的道路交通事故预测[J]. 交通标准化, 2009, 0(17): 86-90 |
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| 作者姓名： | 闫建华 宇仁德 汪洋 |
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| 作者单位： | 山东理工大学交通与车辆工程学院,山东淄博255049 |
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| 摘 要： | 将主成分分析和BP神经网络相结合的方法用于道路交通事故预测中,对影响道路交通事故的因素进行主成分分析,并将分析结果作为BP神经网络的输入数据,这样不仅可以减少输入变量个数,而且能保留原始变量的主要信息,消除变量之间的相关性。另外,计算结果表明基于主成分分析（PCA）的BP神经网络法优于BP神经网络法。
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| 关 键 词： | 主成分分析 BP神经网络 道路交通事故 预测 |
Traffic Accident Forecast Based on Principal Component Analysis |
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| YAN Jian-hua,YU Ren-de,WANG Yang. Traffic Accident Forecast Based on Principal Component Analysis[J]. Communications Standardization, 2009, 0(17): 86-90 |
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| Authors: | YAN Jian-hua YU Ren-de WANG Yang |
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| Affiliation: | (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China) |
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| Abstract: | A combination approach based on principal component analysis（PCA） and BP ANN is presented for traffic accident forecast. The factors influencing road traffic accidents have been processed by PCA. The results of PCA are input data for BP ANN. It not only reduces the number of input variables, but also reserves the main information of original variables and irrelevance among variables. It proves that BP ANN based on PCA is better than BP ANN. |
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| Keywords: | principal component analysis BP ANN traffic accident forecast |
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