Profiling pedestrian distribution and anomaly detection in a dynamic environment

Minh Tuan Doan, Sutharshan Rajasegarar, Mahsa Salehi, Masud Moshtaghi, Christopher Leckie

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

9 Citations (Scopus)

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 languageEnglish
Title of host publicationCIKM'15 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management
Subtitle of host publicationOctober 19-23, 2015 Melbourne, Australia
EditorsCharu C. Aggarwal, Maarten de Rijke, Ravi Kumar, Vanessa Murdock, Timos Sellis, Jeffrey Xu Yu
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages1827-1830
Number of pages4
ISBN (Electronic)9781450337946
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventACM International Conference on Information and Knowledge Management 2015 - Melbourne, Australia
Duration: 19 Oct 201523 Oct 2015
Conference number: 24th
http://www.cikm-2015.org/
https://dl.acm.org/doi/proceedings/10.1145/2806416

Conference

ConferenceACM International Conference on Information and Knowledge Management 2015
Abbreviated titleCIKM 2015
CountryAustralia
CityMelbourne
Period19/10/1523/10/15
Internet address

Keywords

  • Anomaly detection
  • Application

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