Skeleton Network Extraction and Analysis on Bicycle Sharing Networks

Abstract

Skeleton network extraction has been adopted unevenly in transportation networks whose nodes are always represented as spatial units. In this article, the TPks skeleton network extraction method is proposed and applied to bicycle sharing networks. The method aims to reduce the network size while preserving key topologies and spatial features. The authors quantified the importance of nodes by an improved topology potential algorithm. The spatial clustering allows to detect high traffic concentrations and allocate the nodes of each cluster according to their spatial distribution. Then, the skeleton network is constructed by aggregating the most important indicated skeleton nodes. The authors examine the skeleton network characteristics and different spatial information using the original networks as a benchmark. The results show that the skeleton networks can preserve the topological and spatial information similar to the original networks while reducing their size and complexity.

Motif-h: a novel functional backbone extraction for directed networks

“…Grady et al have introduced the robust link salience method to extract network skeletons of generic statistical properties based on the shortest path tree [20]. Yuan et al have proposed the TP ks index to identify the important nodes in the network and achieved the success in the analysis of the bicycle sharing networks [30,37]. Zhang and Zhu have proposed the new measure, i.e., the strong ties, as skeletons of weighted social networks [56].…”