四川大学学报：工程科学版2011，Vol.43Issue(5)：153-158,6.
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
摘要
Abstract
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%.关键词
流数据挖掘/时空分析/交通流模型Key words
stream data mining/spatial-temporal analysis/traffic flow model分类
信息技术与安全科学引用本文复制引用
王涛,王俊峰,罗积玉,兰时勇..基于时空分析的复杂交通流数据挖掘算法[J].四川大学学报：工程科学版,2011,43(5):153-158,6.基金项目
国家高技术研究发展计划资助项目（2008AA01Z208 （）
2009AA01Z405） （）
国家自然科学基金资助项目 （）
四川省应用基础研究资助项目 （）
