Abstract
Pedestrians movements have a major impact on the dynamics of cities and provide valuable guidance to city planners. In this paper we model the normal behaviours of pedestrian flows and detect anomalous events from pedestrian counting data of the City of Melbourne. Since the data spans an extended period, and pedestrian activities can change intermittently (e.g., activities in winter vs. summer), we applied an Ensemble Switching Model, which is a dynamic anomaly detection technique that can accommodate systems that switch between different states. The results are compared with those produced by a static clustering model (Hy-CARCE) and also cross-validated with known events. We found that the results from the Ensemble Switching Model are valid and more accurate than HyCARCE.
Original language | English |
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Title of host publication | CIKM'15 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management |
Subtitle of host publication | October 19-23, 2015 Melbourne, Australia |
Editors | Charu C. Aggarwal, Maarten de Rijke, Ravi Kumar, Vanessa Murdock, Timos Sellis, Jeffrey Xu Yu |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 1827-1830 |
Number of pages | 4 |
ISBN (Electronic) | 9781450337946 |
DOIs | |
Publication status | Published - 2015 |
Externally published | Yes |
Event | ACM International Conference on Information and Knowledge Management 2015 - Melbourne, Australia Duration: 19 Oct 2015 → 23 Oct 2015 Conference number: 24th http://www.cikm-2015.org/ https://dl.acm.org/doi/proceedings/10.1145/2806416 |
Conference
Conference | ACM International Conference on Information and Knowledge Management 2015 |
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Abbreviated title | CIKM 2015 |
Country | Australia |
City | Melbourne |
Period | 19/10/15 → 23/10/15 |
Internet address |
Keywords
- Anomaly detection
- Application