文中提出了一种基于路段关联度的城市交通流量Apriori-LSTM(Apriori-long short term memory network)预测模型.处理卡口检测器数据,统计交通量并提取车辆轨迹,采用Apriori算法分析预测时段内目标路段与关联路段的时空相关性,计算关联...文中提出了一种基于路段关联度的城市交通流量Apriori-LSTM(Apriori-long short term memory network)预测模型.处理卡口检测器数据,统计交通量并提取车辆轨迹,采用Apriori算法分析预测时段内目标路段与关联路段的时空相关性,计算关联路段支持度;求解预测时段内关联路段到目标路段的流入量,构建LSTM预测的输入矩阵、并采用LSTM预测路段短时流量.采用实例进行验证,对迭代次数、隐藏层神经元个数和步长进行参数灵敏度分析,并与单一的LSTM预测结果进行比较.结果表明:Apriori-LSTM的平均绝对误差降至3.8%,平均绝对百分误差和平均均方误差均有显著降低,均等系数有所提高,模型稳定性更好,达到了更好预测效果.展开更多
In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fu...In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.展开更多
文摘文中提出了一种基于路段关联度的城市交通流量Apriori-LSTM(Apriori-long short term memory network)预测模型.处理卡口检测器数据,统计交通量并提取车辆轨迹,采用Apriori算法分析预测时段内目标路段与关联路段的时空相关性,计算关联路段支持度;求解预测时段内关联路段到目标路段的流入量,构建LSTM预测的输入矩阵、并采用LSTM预测路段短时流量.采用实例进行验证,对迭代次数、隐藏层神经元个数和步长进行参数灵敏度分析,并与单一的LSTM预测结果进行比较.结果表明:Apriori-LSTM的平均绝对误差降至3.8%,平均绝对百分误差和平均均方误差均有显著降低,均等系数有所提高,模型稳定性更好,达到了更好预测效果.
基金National Natural Science Foundation of China(No.61663021)Science and Technology Support Project of Gansu Province(No.1304GKCA023)Scientific Research Project in University of Gansu Province(No.2017A-025)
文摘In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.
