摘要: Traffic time series analysis is important because of its use in traffic control and travel time prediction. In this paper, we discuss how to cluster traffic time series that have similar fluctuation patterns. We use simple average... 展开
作者 | Shan Jiang Shuofeng Wang Xin Pei Zhiheng Li Guo Weiwei | ||
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作者单位 | |||
文集名称 | 2015 IEEE 18th International Conference on Intelligent Transportation Systems | ||
出版年 | 2015 | ||
会议名称 | IEEE International Conference on Intelligent Transportation Systems | ||
页码 | 848-853 | 开始页/总页数 | 00000848 / 6 |
会议地点 | Gran Canaria(ES) | 会议年/会议届次 | 2015 / 18th |
关键词 | data analysis pattern clustering principal component analysis road traffic control time series traffic engineering computing PCA clustering algorithm fluctuation pattern fluctuation similarity modeling k-means algorithm principle component analysis ramp entrance residual time series rural area simple average detrending method time series features traffic control traffic flow time series analysis traffic time series clustering travel time prediction urban area Fluctuations Market research Principal component analysis Roads Time series analysis Traffic control Yttrium PCA detrending fluctuation pattern k-means traffic time series | ||
馆藏号 | IEL22230 (7312804) |