基于时空分析的复杂交通流数据挖掘算法

A Stream Data Mining Algorithm for Complex Spatial-Temporal Traffic Flow Data Analysis

为了建立一种易于计算机实现的线性算法来进行交通流数据挖掘,同时建立更加精确的路段交通流模型,通过分析复杂交通数据所特有的流特征和时空特征提出了一种新的交通流数据挖掘算法。首先采用时空滑动窗口数据模型降低了算法的时空复杂度,并实现了动态挖掘;通过对数据流进行聚类分析发现彼此间相似的数据流,并按时段分簇;对每一簇通过主成分分析法剔除非关键变量,最后使用分时段多元线性回归方程构建兴趣模式的表达式,该算法为动态算法,交通实测数据实验证明模型的拟合精度较高,拟合值与真值的平均绝对误差值控制在9秒以内,平均相对误差值控制在5%以内,综合各个时段来看,预测的准确度都在90%以上。

In order to establish a linear traffic flow data mining algorithm,which is easy to be implemented,and build a more precise dynamical model of traffic flow on segment,a new traffic flow data mining algorithm was proposed by exploring the streaming features and the spatial-temporal features of the traffic flow data.Spatial-temporal sliding window was applied to reduce the complexity of the algorithm both on spatial and temporal factors.Clusters with similar characteristics were partitioned,in which the PCA method was used to exclude those uncritical variables.The final patterns of interesting was expressed by the multi-variable linear regression equation in different time periods.The experimental results showed that the new algorithm is extremely efficient,reliable and accurate.The established model is dynamic in essence.The experimental results showed that the fitting accuracy is higher and the mean absolute error between fitted and standard value is less than 9 seconds,the mean relative error is less than 5%,the model has a high degree of accuracy above 90%.