A Clustering Refinement Approach for Revealing Urban Spatial Structure from Smart Card Data
| Autor: | Liyang Tang, Yang Zhao, Kwok Leung Tsui, Yuxin He, Liwei Pan |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Applied Sciences, Vol 10, Iss 16, p 5606 (2020) |
| Druh dokumentu: | article |
| ISSN: | 10165606 2076-3417 |
| DOI: | 10.3390/app10165606 |
| Popis: | Facilitated by rapid development of the data-intensive techniques together with communication and sensing technology, we can take advantage of smart card data collected through Automatic Fare Collection (AFC) systems to establish connections between public transit and urban spatial structure. In this paper, with a case study on Shenzhen metro system in China, we investigate the agglomeration pattern of passenger flow among subway stations. Specifically, leveraging inbound and outbound passenger flows at subway stations, we propose a clustering refinement approach based on cluster member stability among multiple clusterings produced by isomorphic or heterogeneous clusterers. Furthermore, we validate and elaborate five clusters of subway stations in terms of regional functionality and urban planning by comparing station clusters with reference to government planning policies and regulations of Shenzhen city. Additionally, outlier stations with ambiguous functionalities are detected using proposed clustering refinement framework. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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