A space-time varying graph for modelling places and events in a network

ABSTRACT

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

Acknowledgments

We would like to thank the anonymous reviewers for their valuable comments. We would also like to thank the team of the People in Motion Lab at the University of New Brunswick for their constructive advice throughout our research work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the NSERC/Cisco Industrial Research Chair (Canada-2015/2021) and TETFund Nigerian Scholarship.

Notes on contributors

Ikechukwu Maduako

Ikechukwu Maduako is a PhD candidate in People-in-Motion Lab, Geodesy and Geomatics Engineering, University of New Brunswick. He works at the intersection of Advanced Geospatial Analytics, Machine learning and Graph Analytics.

Monica Wachowicz

Monica Wachowicz is a professor in Data Science, and the NSERC/Cisco Industrial Research Chair in Big Data Analytics. She is also the Director of the People in Motion Laboratory, a center of expertise in the application of Internet of Things (IoT) to smart cities.