Understanding mobility based on GPS data

ABSTRACT

Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems. As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information. In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs. The contribution of this work lies in the following two aspects. On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used. On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance. This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement.

REFERENCES

• 1: GPS Track log route exchange forum: http://www.gpsxchange.com
• 2: Mihael Ankerst , Markus M. Breunig , Hans-Peter Kriegel , Jörg Sander, OPTICS: ordering points to identify the clustering structure, Proceedings of the 1999 ACM SIGMOD international conference on Management of data, p.49-60, May 31-June 03, 1999, Philadelphia, Pennsylvania, United States
• 3: Daniel Ashbrook , Thad Starner, Using GPS to learn significant locations and predict movement across multiple users, Personal and Ubiquitous Computing, v.7 n.5, p.275-286, October 2003 [doi>10.1007/s00779-003-0240-0]
• 4: Ermes, M., Parkka, J., Mantyjarvi, J., Korhonen I., Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions, IEEE Transactions on Information Technology in Biomedicine 12, 1(2006), 20--26.
• 5: Krumm, J., Horvitz, E., LOCADIO: Inferring Motion and Location from Wi-Fi Signal Strengths. In Proc. of Mobiquitous 2004, IEEE Press (2004), 4--13.
• 6: Krumm, J., Horvitz, E., Predestination: Inferring Destinations from Partial Trajectories. In Proc. of UBICOMP'06, Springer-Verlag Press(2003), 243--260
• 7: Liao L., Patterson, D. J., Fox, D., Kautz, H., Building Personal Maps from GPS Data. IJCAI MOO05, Springer Press(2005), 249--265
• 8: Liao L., Fox, D., Kautz, H., Learning and Inferring Transportation Routines. In Proc. of AI 2004. AAAI Press (2004), 348--353.
• 9: Parkka, J., Ermes, M., Korpipaa P., Mantyjarvi J., Peltola, J., Activity classification using realistic data from wearable sensors, IEEE Transactions on Information Technology in Biomedicine 10, 1 (2006), 119--128.
• 10: Patterson, D. J., Liao, L., Fox, D., Kautz, H., Inferring High-Level Behavior from Low-Level Sensors. In Proc. of UBICOMP '03, Springer Press (2003), 73--89
• 11: Timothy, S., Varshavsky, A., LaMarca A., Chen M. Y., Choudhury T., Mobility detection using everyday GSM traces. In Proc. Ubicomp 2006, Springer Press (2006).
• 12: Yu Zheng , Like Liu , Longhao Wang , Xing Xie, Learning transportation mode from raw gps data for geographic applications on the web, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367532]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Classifier design and evaluation

Keywords:
GPS, GeoLife, infer transportation mode, machine learning, recognize human behavior