Exploring lifestyle patterns from GPS trajectory data: embedding spatio-temporal context information via geohash and POI
Author:
Funder
Korea Agency for Infrastructure Technology Advancement
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s41324-024-00597-7.pdf
Reference39 articles.
1. Won, M., Choi, J., Lee, H., & Kim, J. (2021). Development of an algorithm to analyze home-based work trips using mobile phone data. Journal of Korean Society of Transportation, 39(3), 383–398. https://doi.org/10.7470/jkst.2021.39.3.383
2. Cho, N., & Kang, Y. (2019). Identifying staying places with global positioning system movement data using 3D density-based spatial clustering of applications with noise. Sensors and Materials, 31(10), 3273–3287. https://doi.org/10.18494/SAM.2019.2410
3. Kim, H. (2021). Travel mode classification method based on travel track information. Journal of Korea Society of Computer Information, 26(12), 133–142. https://doi.org/10.9708/jksci.2021.26.12.133
4. Lee, G., Maeng, J., & Son, S. (2019). Mobility mode classification method for trajectory data using CNN. Journal of KIIT, 17(12), 13–20. https://doi.org/10.14801/jkiit.2019.17.12.13
5. Jeong, C., Kim, M., Jeong, H., & Chung, S. (2020). GPS-based mobility mode classification and sampling rate optimization. Journal of Information Technology and Applied Engineering, 10(2), 37–47. https://doi.org/10.3390/s22041476
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