NDLI: A traffic flow approach to early detection of gathering events

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A traffic flow approach to early detection of gathering events

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A traffic flow approach to early detection of gathering events

Content Provider	ACM Digital Library
Author	Liu, Alex Khezerlou, Amin Vahedian Zhou, Xun Shafiq, Zubair Zhang, Fan
Abstract	Given a spatial field and the traffic flow between neighboring locations, the early detection of gathering events (edge) problem aims to discover and localize a set of most likely gathering events. It is important for city planners to identify emerging gathering events which might cause public safety or sustainability concerns. However, it is challenging to solve the edge problem due to numerous candidate gathering footprints in a spatial field and the non-trivial task to balance pattern quality and computational efficiency. Prior solutions to model the edge problem lack the ability to describe the dynamic flow of traffic and the potential gathering destinations because they rely on static or undirected footprints. In contrast, in this paper, we model the footprint of a gathering event as a Gathering directed acyclic Graph (G-Graph), where the root of the G-Graph is the potential destination and the directed edges represent the most likely paths traffic takes to move towards the destination. We also proposed an efficient algorithm called SmartEdge to discover the most likely non-overlapping G-Graphs in the given spatial field. Our analysis shows that the proposed G-Graph model and the SmartEdge algorithm have the ability to efficiently and effectively capture important gathering events from real-world human mobility data. Our experimental evaluations show that SmartEdge saves 50% computation time over the baseline algorithm.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450345897
DOI	10.1145/2996913.2996998
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Spatial data mining Gathering event Early detection
Content Type	Text
Resource Type	Article

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