Travel Diaries Analysis by Sequential Rule Mining

Huy Quan Vu, Gang Li, Chun Hung Roberts Law, Yanchun Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

30 Citations (Scopus)


Because of the inefficiency in analyzing the comprehensive travel data, tourism managers are facing the challenge of gaining insights into travelers’ behavior and preferences. In most cases, existing techniques are incapable of capturing the sequential patterns hidden in travel data. To address these issues, this article proposes to analyze the travelers’ behavior through geotagged photos and sequential rule mining. Travel diaries, constructed from the photo sequences, can capture comprehensive travel information, and then sequential patterns can be discovered to infer the potential destinations. The effectiveness of the proposed framework is demonstrated in a case study of Australian outbound tourism, using a data set of more than 890,000 photos from 3,623 travelers. The introduced framework has the potential to benefit tourism researchers and practitioners from capturing and understanding the behaviors and preferences of travelers. The findings can support destination-marketing organizations (DMOs) in promoting appropriate destinations to prospective travelers.
Original languageEnglish
Pages (from-to)399-413
Number of pages15
JournalJournal of Travel Research
Issue number3
Publication statusPublished - 1 Mar 2018


  • data mining
  • Flickr
  • geotagged photo
  • sequential rule mining
  • travel diary

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Transportation
  • Tourism, Leisure and Hospitality Management

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