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A Taxi Trajectory and Social Media Data Management Platform for Tourist Behavior Analysis

Author

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  • Pattama Krataithong

    (ICT Department, School of Engineering & Technology, Asian Institute of Technology, Klong Luang 12120, Pathum Thani, Thailand)

  • Chutiporn Anutariya

    (ICT Department, School of Engineering & Technology, Asian Institute of Technology, Klong Luang 12120, Pathum Thani, Thailand)

  • Marut Buranarach

    (National Electronics and Computer Technology Center, Klong Luang 12120, Pathum Thani, Thailand)

Abstract

Taxis are commonly used by tourists to travel around unfamiliar cities they visit. These taxis today have GPS devices, which can then be used to collect a significant amount of data on the movement of tourists. One problem with this idea, however, is the question of how to extract that movement data from the raw GPS data, which includes a lot of other data, such as vehicle IDs, timestamps, and speeds, etc. The purpose of this research is to propose a data management platform to process heterogeneous data including taxi data, social media data, and place data for tourist behavior analysis. We propose a data pipeline that can be scaled in order to process a significant amount of data regarding taxi trajectory and social media, with two objectives. The first objective is to extract the tourist trajectory data from the raw GPS data and produce a data integration module enriched with a knowledge base of tourist trajectories. This knowledge base is constructed through the extension of semantic trajectory ontology (STO) and mobility behavior ontology (MBO). The second objective is to extract tourist activities/point of interests (POIs) from geo-tagged Twitter data. The results of the data pipeline can readily be used for tourist behavior analysis, such as tourist descriptive analysis, popular tourist destinations/zones, and tourist movement patterns identification. We leverage the study’s results to demonstrate the real-life case study in Bangkok during the Songkran Festival in 2019. Thus, we could precisely identify tourist movement during various periods, determine popular destinations/zones, discover high density density of taxi destination points for a given trajectory type, and display the top ten tourist destinations, as well as prominent tourism keywords or trends at the time. This can provide insight to governments and businesses related to tourism regarding the trajectories and activities of tourists, and it will help predict future tourism trends.

Suggested Citation

  • Pattama Krataithong & Chutiporn Anutariya & Marut Buranarach, 2022. "A Taxi Trajectory and Social Media Data Management Platform for Tourist Behavior Analysis," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:8:p:4677-:d:793370
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    References listed on IDEAS

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    1. Jose J Padilla & Hamdi Kavak & Christopher J Lynch & Ross J Gore & Saikou Y Diallo, 2018. "Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-20, June.
    2. Meixu Chen & Dani Arribas-Bel & Alex Singleton, 2019. "Understanding the dynamics of urban areas of interest through volunteered geographic information," Journal of Geographical Systems, Springer, vol. 21(1), pages 89-109, March.
    3. Bidur Devkota & Hiroyuki Miyazaki & Apichon Witayangkurn & Sohee Minsun Kim, 2019. "Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest," Sustainability, MDPI, vol. 11(17), pages 1-29, August.
    4. Cai, Hua & Zhan, Xiaowei & Zhu, Ji & Jia, Xiaoping & Chiu, Anthony S.F. & Xu, Ming, 2016. "Understanding taxi travel patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 590-597.
    5. Chua, Alvin & Servillo, Loris & Marcheggiani, Ernesto & Moere, Andrew Vande, 2016. "Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy," Tourism Management, Elsevier, vol. 57(C), pages 295-310.
    6. 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.
    7. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
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    Cited by:

    1. Jiusheng Du & Xingwang Liu & Chengyang Meng, 2023. "Road Intersection Extraction Based on Low-Frequency Vehicle Trajectory Data," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    2. Yao Chang & Dongbing Li & Zibibula Simayi & Shengtian Yang & Maliyamuguli Abulimiti & Yiwei Ren, 2022. "Spatial Pattern Analysis of Xinjiang Tourism Resources Based on Electronic Map Points of Interest," IJERPH, MDPI, vol. 19(13), pages 1-18, June.

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