期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2015
卷号：13
期号：2
页码：563-570
DOI：10.12928/telkomnika.v13i2.980
语种：English
出版社：Universitas Ahmad Dahlan
摘要：Floating car data (FCD) is vechiel’s position trace that is comes from Global Positioning System receivers.collected at the discrete time. FCD contains much information of traffic and road-network. But there are different level of trajectory shape damages because of the affection of sample frequency and running speed. So before the FCD mining, the first thing should be done is to restore track to make up for the loss. This paper analyzes the spatial-temporal characteristics of floating car trajectory at intersection, and build an error recognition model and a adjust algorithm. Experiments showed that this method can improve accuracy of trajectory restoration, and set a solid foundation for further data mining.
其他摘要：Floating car data (FCD) is vechiel’s position trace that is comes from Global Positioning System receivers.collected at the discrete time. FCD contains much information of traffic and road-network. But there are different level of trajectory shape damages because of the affection of sample frequency and running speed. So before the FCD mining, the first thing should be done is to restore track to make up for the loss. This paper analyzes the spatial-temporal characteristics of floating car trajectory at intersection, and build an error recognition model and a adjust algorithm. Experiments showed that this method can improve accuracy of trajectory restoration, and set a solid foundation for further data mining.
