Detection of clusters in traffic networks based on spatio‐temporal flow modeling. Issue 2 (27th February 2019)

• Record Type: Journal Article
• Authors: Shi, Yan; Deng, Min; Gong, Jianya; Lu, Chang‐Tien; Yang, Xuexi; Liu, Huimin
• Is Part Of: Transactions in GIS. Volume 23:Issue 2(2019)
• Page Start: 312
• Page End: 333
• Publication Date: 2019-02-27
• Subjects: Geographic information systems -- Periodicals; 910.285
• DOI: 10.1111/tgis.12521
• Languages: English
• ISSNs: 1361-1682

Abstract

Abstract: Spatio‐temporal clustering is a highly active research topic and a challenging issue in spatio‐temporal data mining. Many spatio‐temporal clustering methods have been designed for geo‐referenced time series. Under some special circumstances, such as monitoring traffic flow on roads, existing methods cannot handle the temporally dynamic and spatially heterogeneous correlations among road segments when detecting clusters. Therefore, this article develops a spatio‐temporal flow‐based approach to detect clusters in traffic networks. First, a spatio‐temporal flow process is modeled by combining network topology relations with real‐time traffic status. On this basis, spatio‐temporal neighborhoods are captured by considering traffic time‐series similarity in spatio‐temporal flows. Spatio‐temporal clusters are further formed by successive connection of spatio‐temporal neighbors. Experiments on traffic time series of central London's road network on both weekdays and weekends are performed to demonstrate the effectiveness and practicality of the proposed method.