[go: up one dir, main page]
Welcome to the InfoSci Platform
Reference Hub3
Skeleton Network Extraction and Analysis on Bicycle Sharing Networks

Skeleton Network Extraction and Analysis on Bicycle Sharing Networks

Kanokwan Malang, Shuliang Wang, Yuanyuan Lv, Aniwat Phaphuangwittayakul
Copyright: © 2020 |Volume: 16 |Issue: 3 |Pages: 22
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799804994|DOI: 10.4018/IJDWM.2020070108
Cite Article Cite Article

MLA

Malang, Kanokwan, et al. "Skeleton Network Extraction and Analysis on Bicycle Sharing Networks." IJDWM vol.16, no.3 2020: pp.146-167. http://doi.org/10.4018/IJDWM.2020070108

APA

Malang, K., Wang, S., Lv, Y., & Phaphuangwittayakul, A. (2020). Skeleton Network Extraction and Analysis on Bicycle Sharing Networks. International Journal of Data Warehousing and Mining (IJDWM), 16(3), 146-167. http://doi.org/10.4018/IJDWM.2020070108

Chicago

Malang, Kanokwan, et al. "Skeleton Network Extraction and Analysis on Bicycle Sharing Networks," International Journal of Data Warehousing and Mining (IJDWM) 16, no.3: 146-167. http://doi.org/10.4018/IJDWM.2020070108

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.