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

Mahdi, Ali ✉ [Mahdi, Ali (Transportation En...), szerző] Közlekedésüzemi és Közlekedésgazdasági Tanszék (BME / KJK); Esztergár-Kiss, Domokos [Esztergár-Kiss, Domokos (Közlekedéstudomány), szerző] Közlekedésüzemi és Közlekedésgazdasági Tanszék (BME / KJK)

Angol nyelvű Szakcikk (Folyóiratcikk) Tudományos
Konferencia: 4th International Conference on HCI in Mobility, Transport, and Automotive Systems, MobiTAS 2022 2022-06-26 [Online konferencia, Nemzetközi]
  • X. Földtudományok Osztálya: A
  • SJR Scopus - Computer Science (miscellaneous): Q3
Szakterületek:
  • Műszaki és technológiai tudományok
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.
Hivatkozás stílusok: IEEEACMAPAChicagoHarvardCSLMásolásNyomtatás
2025-05-20 09:24