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Automated mobility mode detection based on GPS tracking data

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An approach to detect mobility mode by mining/analyzing the geographic location, duration, speed as well as spatial context information in the data.

Publicada em: Dados e análise
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Automated mobility mode detection based on GPS tracking data

  1. 1. Yongyao Jiang, Yuqi Chen, Jie Tian Clark University
  2. 2. Background  Widespread of GPS-enabled devices  A number of web applications  make sense of GPS tracking data
  3. 3. Why is mobility mode important?  For users,  Reflect on their past events  Share life experience  For researchers,  Understand user’s lifestyle, social phenomena.  compare people’s daily mobility modes, east vs. west coast, the U.S. vs. China
  4. 4. GPS Tracking Data ID Latitude Longitude Time Speed 1 -71.695522 42.346806 07:19:30 5.2km/h 2 -71.694959 42.340857 07:19:40 6.3km/h … … … … … •The location of user is recorded every ten seconds. •speed is estimated by GPS device between every two consecutive points
  5. 5. Sample Result Start time End time Mobility mode 4:00 7:41 Indoor 7:41 8:32 Driving 8:32 8:37 Parking 8:37 11:48 Indoor 11:48 12:53 Driving 12:53 12:57 Parking 12:57 17:23 Indoor 17:23 17:51 Walking
  6. 6. Terminology Based on time duration, Significant activity ≥ 1 min Insignificant activity < 1 min Our goal is to divide a GPS trajectory into multiple significant activities
  7. 7. Terminology  Heading change rate (HCR) Walking Transporting High Low
  8. 8. Methodology Five modes: transporting, walking, indoor, parking and roaming • Indoor point detection • Outdoor point classification • Inference strategy
  9. 9. Indoor Point Detection  Indoor error  For each building polygon encompassing GPS points, the entry point and the exit point are detected. Exit point Entry point
  10. 10. Outdoor point classification  V>7km/h, likely transporting  3.6<V<7km/h, likely walking  V<3.6km/h, likely roaming
  11. 11. Outdoor point classification  Connect points of the same class into segments  If the length of a segment>1 min, significant segment; otherwise, insignificant segment Transporting Walking Indoor Parking Roaming
  12. 12. Inference strategy walking roaming walking walking walking roaming walkingwalkingtransporting transporting walking roaming walking walkingtransporting transportingInsignificant walking transporting Situation 1: Situation 2:
  13. 13. Inference strategy • A linear segment between two “Transporting” segments will be reclassified as “Transporting” • “Linear” is measured by HCR (heading change rate) Transporting Transporting Linear segment
  14. 14. Inference strategy • A clustered segment that starts after “Transporting” or ends before “Transporting” will be classified as “Parking” • “Clustered” is measured by heading change rate.
  15. 15. Experiment Data
  16. 16. Accuracy assessment Mobility Mode Accuracy Transporting 95.30% Walking 91.71% Parking 93.62% Indoor 75.80% Roaming 71.23% Overall accuracy 90.28% By referencing the human interpretation information,
  17. 17. Discussion  Choosing an appropriate temporal resolution  a coarse resolution may fail in the detection of some brief significant activities  a fine resolution may suffer from much of noises
  18. 18. Discussion  Setting an appropriate length for short jumping points  It is relative to the total length of stay in the building and is subject to the GPS error severity.  Reach a balance or set different value for each building Small jumping length Large jumping length
  19. 19. Summary  Effective algorithm to detect mobility mode with overall accuracy of 90%  Reduce efforts spent on human interpretation  Make soft classification based on possibility  Develop a web application to automate the detection and visualize the result
  20. 20. Questions?