A space-time varying graph for modelling places and events in a network
Modelling topological relationships between places and events is challenging especially because these relationships are dynamic, and their evolutionary analysis relies on the explanatory power of representing their interactions across different temporal resolutions. In this paper, we
introduce the Space-Time Varying Graph (STVG) based on the whole graph approach that combines directed and bipartite subgraphs with a time-tree for representing the complex interaction between places and events across time. We demonstrate how the proposed STVG can be exploited to identify
and extract evolutionary patterns of traffic accidents using graph metrics, ad-hoc graph queries and clustering algorithms. The results reveal evolutionary patterns that uncover the places with high incidence of accidents over different time resolutions, reveal the main reasons why the traffic
accidents have occurred, and disclose evolving communities of densely connected traffic accidents over time.
Keywords: Space-time varying graph; graph modelling; real-world networks; traffic accident analytics
Document Type: Research Article
Affiliations: People in Motion Lab, Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, NB, Canada
Publication date: 03 October 2019
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