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A Prediction of Vehicle Possession in Hunan Province Based on Principal Component and BP Neural Network

机译:基于主成分和BP神经网络的湖南省车辆占有预测

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Prediction of car ownership has direct reference significance for the development of urban transportation and construction of urban roads. By analyzing the impact factors of urban auto possession, this paper first analyzes 8 indicators such as urban population, GDP, road passenger traffic and so on determined by some references, then establish BP neural network model to predict the vehicles possession in Hunan Province from 2006 to 2008. The figures of prediction is 989,300, 1,221,800 and 1,370,300 respectively in 2006, 2007 and 2008, which is very close to the real ownership of 946,400,1,217,200 and 1,426,700 respectively. It shows the prediction is very accurate. This suggests that the BP neural network has very strong learning and generalization ability and can be employed in prediction of vehicle possession effectively. The prediction of car ownership, as a foundational work for transportation planning, has direct reference significance on the development of urban traffic, its control and management and construction of urban road, etc. Early in 1940s this research has been started in foreign countries[1]. Many different models of prediction of car ownership have been developed. Many of them are developed mainly based on the factors such as urban economy, population network capacity, the land utilization and parking facilities. In China there are also some researches on this issue. They predicate the car ownership mainly by time series prediction, regression analysis and fractal theory and entropy method [2~6]. However, these methods do not comprehensively describe the complex relationship between car ownership and other factors. The author of this paper chooses some car ownership-related factors and employ principal component method to analyze to obtain the main factors, then tries to find the relationship between BP neural networks and car ownership according to these factors so as to predict the car ownership in Hunan Province form 2006 to 2008, which will be greatly significant to the development of urban transportation, management and construction.
机译:汽车所有权预测对城市交通和城市道路建设的发展具有直接参考意义。通过分析城市自动占有的影响因素,本文首先分析了城市人口,GDP,道路客运等8个指标等,这些指标由某些参考决定,然后建立BP神经网络模型,以预测2006年湖南占有权到2008年。预测图分别于2006年,2007年和2008年分别为989,300,1,221,800和1,370,300,这非常接近946,400,1,217,200和1,426,700的实际所有权。它显示预测非常准确。这表明BP神经网络具有非常强大的学习和泛化能力,并且可以有效地用于预测车辆占有。汽车所有权的预测,作为运输规划的基础工作,对城市交通的发展,城市道路的控制和建设等直接参考意义等。在20世纪40年代,在国外开始了这项研究[1 ]。已经开发出许多不同的汽车所有权预测模型。其中许多是主要基于城市经济,人口网络能力,土地利用和停车设施等因素的因素。在中国还有一些关于这个问题的研究。它们主要通过时间序列预测,回归分析和分形理论和熵方法(2〜6])谓。然而,这些方法无法全面地描述汽车所有权和其他因素之间的复杂关系。本文的作者选择了一些与汽车所有权相关的因素,采用了主要成分方法来分析以获得主要因素,然后试图根据这些因素找到BP神经网络和汽车所有权之间的关系,以预测汽车所有权湖南省2006年至2008年,这将对城市交通,管理和建设的发展大大意义。

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