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

Mahdi, Ali and Esztergár-Kiss, Domokos (2022) Robust Linear Regression-Based GIS Technique for Modeling the Processing Time at Tourism Destinations. In: HCI in Mobility, Transport, and Automotive Systems : 4th International Conference, MobiTAS 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, June 26 – July 1, 2022, Proceedings. Lecture Notes in Computer Science (13335). Springer Nature Limited, Cham, pp. 557-569. ISBN https://doi.org/10.1007/978-3-031-04987-3_38

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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 usingGPT 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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