Title :
Mining related information of traffic flows on lanes by k-medoids
Author :
Ting Zhang ; Yingjie Xia ; Qianqian Zhu ; Yuncai Liu ; Jianhui Shen
Author_Institution :
Hangzhou Inst. of Service Eng., Hangzhou Normal Univ., Hangzhou, China
Abstract :
Nowadays, processing traffic flows has become an important part in intelligent transportation system (ITS). Prediction and estimation of flows, as a main application in this field, has gradually developed. Moreover, there exist some inherent relationships among various traffic flows, and the mining of related information can provide a platform for traffic flow prediction and estimation, and it can supply some guidance to layout traffic sensors. This paper presents a method of cluster by k-medoids to mine related information of traffic flows from spatial dimension. From spatial dimension, road lanes are clustered by k-medoids to constitute a table of related information. In order to make the mining of related information of flows more accurate, degree of saturation is also used to cluster related information. The results indicate that cluster through combination of flow and degree of saturation has a higher efficiency, and cluster by k-medoids outperforms that by k-means in all experiments.
Keywords :
data mining; intelligent transportation systems; pattern clustering; road traffic; ITS; information clustering; intelligent transportation system; k-medoids; road lanes; traffic flow estimation; traffic flow information mining; traffic flow prediction; traffic sensors; Accuracy; Clustering algorithms; Data mining; Prediction algorithms; Roads; Standards; Vectors; k-medoids; related information; road lanes; traffic flows;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2014 11th International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4799-5147-5
DOI :
10.1109/FSKD.2014.6980866
