Transportation Mode Annotation of Tourist GPS Trajectories Under Environmental Constraints

Conference paper


Tourist transportation usage analysis provides basic information for tourism policy making. With the technical advances of tracking devices, GPS-equipped smartphones sense the movement of tourists and generate extensive volumes of movement data detailing tourist trajectories. Many researchers study semantic annotation using machine learning. However, it is necessary for machine learning to label the data for training; this requirement is costly. It would be useful for GPS semantic annotation if labelling the substantial amounts of GPS data could be avoided. In this research, we propose a new, simple GPS semantic annotation method using environmental constraints without machine learning. We call this method Segment Expansion with Environmental Constraints (SEEC) and assume a tourist behaviour model in which tourists move by foot and public transportation in touristic destinations that include numerous locations of interest. SEEC inferred the transportation modes of the GPS trajectory data at a 90.4 % accuracy level in the experiment.


GPS Semantic annotation Big data analytics Environmental constraint 


  1. Bolbol, A., Cheng, T., Tsapakis, I., & Haworth, J. (2012). Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification. Computers, Environment and Urban Systems, 36(6), 526–537.CrossRefGoogle Scholar
  2. Guc, B., May, M., Saygin, Y., & Korner, C. (2008). Semantic annotation of GPS trajectories. In 11th AGILE International Conference on Geographic Information Science. Girona, Spain.Google Scholar
  3. Hemminki, S., Nurmi, P., & Tarkoma, S. (2013, November). Accelerometer-based transportation mode detection on smartphones. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. New York, NY: ACM.Google Scholar
  4. Liao, L., Fox, D., & Kautz, H. (2007). Extracting places and activities from GPS traces using hierarchical conditional random fields. International Journal of Robotics Research, 26(1), 119–134.CrossRefGoogle Scholar
  5. Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., & Srivastava, M. (2010). Using mobile phones to determine transportation modes. ACM Transactions on Sensor Networks (TOSN), 6(2), 13.CrossRefGoogle Scholar
  6. Parent, C., Spaccapietra, S., Renso, C., Andrienko, G., Andrienko, N., Bogorny, V., & Yan, Z. (2013). Semantic trajectories modeling and analysis. ACM Computing Surveys (CSUR) 45(4), 42.Google Scholar
  7. Pearce, P. L. (2005). Tourist behaviour: Themes and conceptual schemes. Bristol: Channel View Publications.Google Scholar
  8. Potamias, M., Patroumpas, K., & Sellis, T. (2006). Sampling trajectory streams with spatiotemporal criteria. In Scientific and Statistical Database Management, 2006. 18th International Conference (pp. 275–284). Vienna: IEEE.Google Scholar
  9. Stenneth, L., Wolfson, O., Yu, P. S., & Xu, B. (2011, November 1–4). Transportation mode detection using mobile phones and GIS information. ACM SIGSPATIAL GIS ’11, Chicago, IL.Google Scholar
  10. Stopher, P., Clifford, E, Zhang, J., & FitzGerald, C. (2008). Deducing mode and purpose from GPS data. In The 87th annual meeting of the transportation research board, Washington, DC.Google Scholar
  11. Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., & Aberer, K. (2011, March). SeMiTri: A framework for semantic annotation of heterogeneous trajectories. In Proceedings of the 14th International Conference on Extending Database Technology (pp. 259–270). New York, NY: ACM.Google Scholar
  12. Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K. (2013). Semantic trajectories: Mobility data computation and annotation. ACM Transactions on Intelligent Systems and Technology (TIST) 4(3), 49.Google Scholar
  13. Zheng, Y., Chen, Y., Li, Q., Xie, X., & Ma, W. Y. (2010). Understanding transportation modes based on GPS data for web applications. ACM Transactions on the Web (TWEB) 4(1), 1.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Academic Center for Computing and Media StudiesKyoto UniversityKyotoJapan

Personalised recommendations