Origin-Destination Extraction from Large-Scale Route Search Records for Tourism Trend Analysis
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911078135365632 |
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| author | Ge, Hangli Huang, Dizhi Yang, Xiaojie Lin, Lifeng Hatano, Kazuma Kawasaki, Takeshi Koshizuka, Noboru |
| author_facet | Ge, Hangli Huang, Dizhi Yang, Xiaojie Lin, Lifeng Hatano, Kazuma Kawasaki, Takeshi Koshizuka, Noboru |
| contents | This paper presents a novel method for transforming large-scale historical expressway route search records into a three-dimensional (3D) Origin-Destination (OD) map, enabling data compression, efficient spatiotemporal sampling and statistical analysis. The study analyzed over 380 million expressway route search logs to investigate online search behavior related to tourist destinations. Several expressway interchanges (ICs) near popular attractions, such as those associated with spring flower viewing, autumn foliage and winter skiing, are examined and visualized. The results reveal strong correlations between search volume trends and the duration of peak tourism seasons. This approach leverages cyberspace behavioral data as a leading indicator of physical movement, providing a proactive tool for traffic management and tourism planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19544 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Origin-Destination Extraction from Large-Scale Route Search Records for Tourism Trend Analysis Ge, Hangli Huang, Dizhi Yang, Xiaojie Lin, Lifeng Hatano, Kazuma Kawasaki, Takeshi Koshizuka, Noboru Computers and Society This paper presents a novel method for transforming large-scale historical expressway route search records into a three-dimensional (3D) Origin-Destination (OD) map, enabling data compression, efficient spatiotemporal sampling and statistical analysis. The study analyzed over 380 million expressway route search logs to investigate online search behavior related to tourist destinations. Several expressway interchanges (ICs) near popular attractions, such as those associated with spring flower viewing, autumn foliage and winter skiing, are examined and visualized. The results reveal strong correlations between search volume trends and the duration of peak tourism seasons. This approach leverages cyberspace behavioral data as a leading indicator of physical movement, providing a proactive tool for traffic management and tourism planning. |
| title | Origin-Destination Extraction from Large-Scale Route Search Records for Tourism Trend Analysis |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2507.19544 |