Comparison of three algorithms for real-time pedestrian state estimation-supporting a monitoring dashboard for large-scale events

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientific

3 Citations (Scopus)

Abstract

Technical advancements allow for the development of crowd monitoring and management support systems to ensure the safety of pedestrians during large-scale events. This way, pedestrian behaviour can be monitored during events, and potential dangerous situations can be identified in a timely manner. A real-time prototype Crowd Monitoring Dashboard has been developed for a large nautical event in Amsterdam in 2015. Three main functions of such a system have been identified: the real-time data collection, the traffic engineering functions and the visualization. This paper focuses on three state estimation algorithms in the second component - the traffic engineering functions. These algorithms are presented and cross compared in terms of performance, data usage and practical applications, based on the empirical data collected from this large-scale event. The outcome can shed useful light on the feasibility of the data sources and estimation methods for future real-time applications on crowd monitoring and management for large-scale events.
Original languageEnglish
Title of host publication19th International Conference on Intelligent Transportation Systems (ITSC)
PublisherIEEE
Pages2601-2606
Number of pages6
ISBN (Electronic)978-1-5090-1889-5
ISBN (Print)978-1-5090-1890-1
DOIs
Publication statusPublished - 1 Nov 2016
EventIEEE ITSC 2013, The 16th international IEEE annual conference on intelligent transportation systems , The Hague, The Netherlands - Danvers, The Hague, Netherlands
Duration: 6 Oct 20139 Oct 2013
Conference number: 16

Conference

ConferenceIEEE ITSC 2013, The 16th international IEEE annual conference on intelligent transportation systems , The Hague, The Netherlands
Abbreviated titleITSC 2013
CountryNetherlands
CityThe Hague
Period6/10/139/10/13

Keywords

  • Large-scale events
  • Traffic state estimation
  • Pedestrian flow
  • Monitoring

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