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Robust Linear Regression-Based GIS Technique for Modeling the Processing Time at Tourism Destinations

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HCI in Mobility, Transport, and Automotive Systems (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13335))

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Abstract

Tourist attractions are dispersed across wide geographic areas even within the cities making the data collection prohibitively expensive. For these reasons, information on tourism destinations should be collected automatically. Recently, the collection of particular data on destinations has become easier with the growing popularity of location-based applications. Based on Google Popular Time (GPT), behavioral data are collected to investigate the actual tourist behavior. The statistical analysis clearly shows the presence of outliers in the collected data. Consequently, a regression model based robust approach is used to study the tourists’ processing time (i.e., the time spent) at various tourism destinations in Budapest. Such spatial parameters are adopted as car parking, public transport station, and location. The statistical outcomes present that the availability of car parking or public transport stations significantly affects the tourists’ processing time at the tourism destinations. The findings demonstrate the benefit of using GPT and other online resources to analyze and predict individual behavior. Furthermore, current study reveals that location-based services provide a principal option for tourists during their journeys.

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Acknowledgments

The linguistic revision of this article is prepared by Eszter Tóth.

Funding

This research was supported by the János Bolyai Research Fellowship of the Hungarian Academy of Sciences (BO/00090/21/6).

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Authors and Affiliations

  1. Department of Transport Technology and Economics, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, 1111, Hungary

    Ali Mahdi & Domokos Esztergár-Kiss

Authors
  1. Ali Mahdi
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  2. Domokos Esztergár-Kiss
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Corresponding author

Correspondence to Ali Mahdi .

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Editors and Affiliations

  1. Technische Universität Ilmenau, Ilmenau, Germany

    Heidi Krömker

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Mahdi, A., Esztergár-Kiss, D. (2022). Robust Linear Regression-Based GIS Technique for Modeling the Processing Time at Tourism Destinations. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2022. Lecture Notes in Computer Science, vol 13335. Springer, Cham. https://doi.org/10.1007/978-3-031-04987-3_38

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  • DOI: https://doi.org/10.1007/978-3-031-04987-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04986-6

  • Online ISBN: 978-3-031-04987-3

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