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基于组合模型的交通流量预测方法
丛新宇
作者单位E-mail
丛新宇 华东理工大学 congcong1986good@126.com 
摘要:
随着智能交通系统的蓬勃发展,交通控制和交通流诱导成为智能交通系统(ITS)研究的热门问题,而实现交通控制诱导的关键问题是实时准确的短时交通流量预测,预测的精度直接影响到交通控制和诱导的效果。为此,提出基于组合模型的交通流量预测方法,该方法将历史趋势模型和多元回归模型加权组合以建立组合预测模型,并利用加权平均的方法,对较精确的预测值赋予较大的权重,从而提高模型预测的精度。最后,通过对2009年上海城市交通流量预测结果的分析,证明该方法可提高预测准确度。
关键词:  交通流量预测  组合预测模型  历史趋势模型  多元回归模型
DOI:
分类号:
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
Traffic Flow Forecasting based on Combination Model
congxinyu
Abstract:
With the vigorous development of Intelligent Transportation systems(ITS),traffic control and traffic guidance have become a research hotspot. Real-time and accurate short-term forecasting of traffic flow is critical to traffic control and guidance, and accuracy directly influences the effect of traffic control and guidance. As for this, a traffic flow forecasting method based on combination model is proposed. In order to improve forecast accuracy, the method combines forecasting models such as historical trend model and multiple regression model to establish combination forecasting model in which a weighted average method is used to give heavier weight to more accurate forecasting results. Through analysis of forecasting results of a scenario of Shanghai’s traffic flow in 2009, it is proved that the method can improve forecast accuracy.
Key words:  Traffic flow forecasting  Combination model  Historical trend model  Multiple regression model

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