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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Spatio-Temporal Clustering of Road Network Data
verfasst von : Tao Cheng, Berk Anbaroglu
Erschienen in: Artificial Intelligence and Computational Intelligence
Verlag: Springer Berlin Heidelberg
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This paper addresses spatio-temporal clustering of network data where the geometry and structure of the network is assumed to be static but heterogeneous due to the density of links varies cross the network. Road network, telecommunication network and internet are of these type networks. The thematic properties associated with the links of the network are dynamic, such as the flow, speed and journey time are varying in the peak and off-peak hours of a day. Analyzing the patterns of network data in space-time can help the understanding of the complexity of the networks Here a spatio-temporal clustering (STC) algorithm is developed to capture such dynamic patterns by fully exploiting the network characteristics in spatial, temporal and thematic domains. The proposed STC algorithm is tested on a part of London’s traffic network to investigate how the clusters overlap on different days.