基于组合预测方法的城市道路短时交通流预测
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摘要
随着我国城市化进程的加速,市民对出行的需求也日益增加,准确的交通预测模型对于更好地分析路网交通状况,规划交通网络和实现交通优化控制策略都有十分重要的作用。以城市路网短时交通流预测为研究对象,建立了基于ARMA模型和BP神经网络模型的组合预测模型,深入研究了城市路网的划分、路网构建和特征路口交通流预测等内容,形成了一个较为完整的城市路网预测体系,通过实测交通流数据,验证了所述方法的可行性和有效性,为城市路网交通流预测提供了一种解决方向。
With the acceleration of the process of urbanization in our country,people's demand for travel is also increasing day by day.An accurate traffic forecasting model plays an important role in better analyzing the traffic conditions of the road network,planning the traffic network and implementing traffic control strategies.The short-term urban traffic flow forecasting was taken as the research object,a combined forecast model based on ARMA model and BP neural network model was established,and the contents of urban road network division,road network construction and traffic flow prediction at intersection were deeply studied.A more complete urban road network forecasting system was formed,and the feasibility and effectiveness of the method were verified through the measurement of traffic flow data,which provides a solution to the urban road network traffic flow forecasting.
With the acceleration of the process of urbanization in our country,people's demand for travel is also increasing day by day.An accurate traffic forecasting model plays an important role in better analyzing the traffic conditions of the road network,planning the traffic network and implementing traffic control strategies.The short-term urban traffic flow forecasting was taken as the research object,a combined forecast model based on ARMA model and BP neural network model was established,and the contents of urban road network division,road network construction and traffic flow prediction at intersection were deeply studied.A more complete urban road network forecasting system was formed,and the feasibility and effectiveness of the method were verified through the measurement of traffic flow data,which provides a solution to the urban road network traffic flow forecasting.
引文
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[7] 张涛,陈先,谢美萍,等.基于K近邻非参数回归的短时交通流预测方法[J].系统工程理论与实践,2010,30(2):376-384.
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[10] 杨春霞,符义琴,鲍铁男,等.基于相似性的短时交通流预测[J].公路交通科技,2015,32(10):124-128.
[11] 秦大建,李志蜀.基于神经网络的时间序列组合预测模型研究及应用[J].计算机应用,2006,26(b06):129-131.
[12] 谭满春,冯荦斌,徐建闽.基于ARIMA与人工神经网络组合模型的交通流预测[J].中国公路学报,2007,20(4):118-121.
[13] 高勇.城市交通流短时预测方法研究及实现[D].中国科学技术大学,2007.
[14] 孙占全,潘景山,张赞军,等.基于主成分分析与支持向量机结合的交通流预测[J].公路交通科技,2009,26(5):127-131.
[15] 姚智胜,邵春福,熊志华,等.基于主成分分析和支持向量机的道路网短时交通流量预测[J].吉林大学学报(工),2008,38(1):48-52.
[2] Ahmed M S,Cook A R.Analysis of freeway traffic time-series data by using Box-Jenkins techniques[M].1979.
[3] Guo J,Huang W,Williams B M.Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification[J].Transportation Research Part C:Emerging Technologies,2014,43:50-64.
[4] Guo J,Williams B M.Real-Time Short-Term Traffic Speed Level Forecasting and Uncertainty Quantification Using Layered Kalman Filters[J].Transportation Research Record Journal of the Transportation Research Board,2010,2175(2175):28-37.
[5] Ledoux C.An urban traffic flow model integrating neural networks[J].Transportation Research Part C:Emerging Technologies,1997,5(5):287-300.
[6] Pang X,Wang C,Huang G.A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm[J].Journal of Transportation Technologies,2016,6(04):200.
[7] 张涛,陈先,谢美萍,等.基于K近邻非参数回归的短时交通流预测方法[J].系统工程理论与实践,2010,30(2):376-384.
[8] Wang J,Shi Q.Short-term traffic speed forecasting hybrid model based on chaos–wavelet analysis-support vector machine theory[J].Transportation Research Part C:Emerging Technologies,2013,27:219-232.
[9] 傅贵,韩国强,逯峰,等.基于支持向量机回归的短时交通流预测模型[J].华南理工大学学报 (自然科学版),2013,41(9):71-76.
[10] 杨春霞,符义琴,鲍铁男,等.基于相似性的短时交通流预测[J].公路交通科技,2015,32(10):124-128.
[11] 秦大建,李志蜀.基于神经网络的时间序列组合预测模型研究及应用[J].计算机应用,2006,26(b06):129-131.
[12] 谭满春,冯荦斌,徐建闽.基于ARIMA与人工神经网络组合模型的交通流预测[J].中国公路学报,2007,20(4):118-121.
[13] 高勇.城市交通流短时预测方法研究及实现[D].中国科学技术大学,2007.
[14] 孙占全,潘景山,张赞军,等.基于主成分分析与支持向量机结合的交通流预测[J].公路交通科技,2009,26(5):127-131.
[15] 姚智胜,邵春福,熊志华,等.基于主成分分析和支持向量机的道路网短时交通流量预测[J].吉林大学学报(工),2008,38(1):48-52.
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