Characterizing visitor engagement behavior at large-scale events: Activity sequence clustering and ranking using GPS tracking data

Hoseb Abkarian, Divyakant Tahlyan, Hani Mahmassani*, Karen Smilowitz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This study uses GPS data of 1461 participants at a planned special event organized in Oshkosh, Wisconsin named AirVenture to characterize their spatio-temporal activity participation behavior. The GPS data is used to derive activity sequences for participants and study the attractiveness of various activities at the event site. A validation procedure is proposed using aerial photos, from which crowd density is estimated and compared to heatmaps of GPS data. A machine learning clustering approach is used to group participants into market segments on the basis of their activity sequences. The results show a prevalence of 6 behavioral groups with statistical tests confirming significant differences related to movement and time use. Finally, a multinomial logit model is formulated, demonstrating that age, prior visitation, and attendance plan (daily vs. weekly) affect the typological behavior. The results reveal valuable insights that can help special event organizers with related marketing and planning strategies.

Original languageEnglish (US)
Article number104421
JournalTourism Management
Volume88
DOIs
StatePublished - Feb 2022

Keywords

  • Activity participation behavior
  • Aerial photos
  • GPS data
  • Market segmentation
  • Planned special events
  • Sequence clustering

ASJC Scopus subject areas

  • Development
  • Transportation
  • Tourism, Leisure and Hospitality Management
  • Strategy and Management

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