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Geo-temporal tracking and analysis of tourist movement

Author

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  • O’Connor, A.
  • Zerger, A.
  • Itami, B.

Abstract

Agent-based simulation is a decision support tool that has recently been used by park managers to better plan the development of tourist infrastructure such as paths, buildings and viewing platforms and to understand the relationships between visitor flows and acceptable levels of crowding. The objective in these studies is to simulate the current and projected movement of individuals in relationship to pedestrian infrastructure or management actions to determine the impact on visitor experience and infrastructure capacity. A number of projects have been undertaken in Australia and the United States using agent-based models. However, research has shown that there is a need for new methods to collect calibration and validation data in order to validate spatial/temporal simulation models. This paper presents the results of experiments designed to track humans at the Twelve Apostles National Park, Victoria, Australia over 3 days in an effort to develop typologies of trip itineraries. The research used running race timing equipment known as the Alge timing system to monitor the movement of people in the park along a constrained network. A series of receivers recorded the precise positions of 900 individuals who were wearing unobtrusive ankle transmitters. This paper examines the technical aspects of tracking humans using race-timing technology and proposes some methods to analyse this data to determine whether typologies of tourist behaviour do indeed exist. The research also comments on the utility of the data to address the requirements of the recreational behaviour simulator, an agent-based modeling framework which has been used extensively for national park management.

Suggested Citation

  • O’Connor, A. & Zerger, A. & Itami, B., 2005. "Geo-temporal tracking and analysis of tourist movement," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 69(1), pages 135-150.
  • Handle: RePEc:eee:matcom:v:69:y:2005:i:1:p:135-150
    DOI: 10.1016/j.matcom.2005.02.036
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    Citations

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    Cited by:

    1. David Zejda & Josef Zelenka, 2019. "The Concept of Comprehensive Tracking Software to Support Sustainable Tourism in Protected Areas," Sustainability, MDPI, vol. 11(15), pages 1-18, July.
    2. East, Duncan & Osborne, Patrick & Kemp, Simon & Woodfine, Tim, 2017. "Combining GPS & survey data improves understanding of visitor behaviour," Tourism Management, Elsevier, vol. 61(C), pages 307-320.
    3. Ana Muñoz-Mazón & Laura Fuentes-Moraleda & Angela Chantre-Astaiza & Marlon-Felipe Burbano-Fernandez, 2019. "The Study of Tourist Movements in Tourist Historic Cities: A Comparative Analysis of the Applicability of Four Different Tools," Sustainability, MDPI, vol. 11(19), pages 1-26, September.
    4. Smallwood, Claire B. & Beckley, Lynnath E. & Moore, Susan A., 2012. "An analysis of visitor movement patterns using travel networks in a large marine park, north-western Australia," Tourism Management, Elsevier, vol. 33(3), pages 517-528.
    5. Du, Siyuan & Guo, Chunxiang & Jin, Maozhu, 2016. "Agent-based simulation on tourists’ congestion control during peak travel period using Logit model," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 187-194.
    6. Lorenzo Masiero & Judit Zoltan, 2012. "Tourists intra-destination visits and transportation mode : a bivariate model," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 1205, USI Università della Svizzera italiana.
    7. Angela Chantre-Astaiza & Laura Fuentes-Moraleda & Ana Muñoz-Mazón & Gustavo Ramirez-Gonzalez, 2019. "Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability," Sustainability, MDPI, vol. 11(17), pages 1-32, August.
    8. Anne Hardy & Sarah Hyslop & Kate Booth & Brady Robards & Jagannath Aryal & Ulrike Gretzel & Richard Eccleston, 2017. "Tracking tourists’ travel with smartphone-based GPS technology: a methodological discussion," Information Technology & Tourism, Springer, vol. 17(3), pages 255-274, September.
    9. Tipakornkiat, Chalat & Limanond, Thirayoot & Kim, Hyunmyung, 2012. "Determining an influencing area affecting walking speed on footpath: A case study of a footpath in CBD Bangkok, Thailand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5453-5464.
    10. Danalet, Antonin & Tinguely, Loïc & Lapparent, Matthieu de & Bierlaire, Michel, 2016. "Location choice with longitudinal WiFi data," Journal of choice modelling, Elsevier, vol. 18(C), pages 1-17.
    11. Xia, Jianhong (Cecilia) & Zeephongsekul, Panlop & Arrowsmith, Colin, 2009. "Modelling spatio-temporal movement of tourists using finite Markov chains," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1544-1553.
    12. Wolf, Isabelle D. & Hagenloh, Gerald & Croft, David B., 2012. "Visitor monitoring along roads and hiking trails: How to determine usage levels in tourist sites," Tourism Management, Elsevier, vol. 33(1), pages 16-28.
    13. Endah Saptutyningsih & Agus Tri Basuki, 2012. "Hedonic valuation of marginal willingness to pay for air quality improvement," Economic Journal of Emerging Markets, Universitas Islam Indonesia, vol. 4(2), pages 163-172, April.
    14. Zheng, Weimin & Huang, Xiaoting & Li, Yuan, 2017. "Understanding the tourist mobility using GPS: Where is the next place?," Tourism Management, Elsevier, vol. 59(C), pages 267-280.

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