Conference proceeding
A traffic flow approach to early detection of gathering events
Proceedings of the 24th ACM SIGSPATIAL International Conference on advances in geographic information systems, pp.1-10
SIGSPACIAL '16
10/31/2016
DOI: 10.1145/2996913.2996998
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.
Details
- Title: Subtitle
- A traffic flow approach to early detection of gathering events
- Creators
- Xun ZhouAmin KhezerlouAlex LiuZubair ShafiqFan Zhang
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 24th ACM SIGSPATIAL International Conference on advances in geographic information systems, pp.1-10
- Series
- SIGSPACIAL '16
- DOI
- 10.1145/2996913.2996998
- Publisher
- ACM
- Grant note
- DOI: 10.13039/100000001, name: National Science Foundation, award: 1566386, CNS-1318563, CNS-1524698, CNS-1421407; name: Jiangsu High-level Innovation and Entrepreneurship (Shuangchuang) Program; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 61472184, 61321491
- Language
- English
- Date published
- 10/31/2016
- Academic Unit
- Computer Science; Business Analytics
- Record Identifier
- 9984083215402771
Metrics
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