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1203

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Published in: Economy & Finance, Education

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Transcript

  • 1. Learning and Inferring Transportation Routines
  • 2. Outline
    • Introduction
    • Hierarchical Activity Model
    • Inference, Learning, and Error Detection
      • Inference
      • Learning
      • Detection of User Errors
    • Experimental Results
  • 3. Introduction
    • A hierarchical Markov model that can learn and infer a user’s daily movements through the community
    • The model allows :
      • Infer the locations of usual goals, such as home or workplace
      • Infer a user’s mode of transportation, such as foot, car, or bus, and predict when and where she will change modes
      • Predict her future movements, both in the short term and in terms of distant goals
      • Infer when a user has broken his ordinary routine in a way that may indicate that he has made an error, such as failing to get off his bus at his usual stop on the way home
      • Robustly track and predict locations even in the presence of total loss of GPS signals and other sources of noise
  • 4. Hierarchical Activity Model
    • Locations and transportation modes
    • Trip segments
    • Goals
  • 5.  
  • 6. Inference
    • GPS-based tracking on street maps
    • Goal and trip segment estimation
  • 7.
    • The factorization (1) separates the state space of our estimation problem into its continuous and discrete parts
    • The left term on the right hand side of (1) , (2) follows by applying Bayes rule and the independences in our estimation problem
  • 8.  
  • 9. Learning
    • Finding goals
    • Finding mode transfer locations
    • Estimating transition matrices
  • 10. Finding mode transfer locations
  • 11. Detection of User Errors
    • The hierarchical tracker has “expensive” particles, each containing much state information, but requires few particles for accurate tracking
    • The flat, untrained tracker needs more particles to maintain tracking, but each particle is cheaper to compute
  • 12. Experimental Results
    • Activity model learning
    • Detection of user errors
  • 13.
    • (a) Probability of the true goal (work place) during an episode from home to work, estimated using the flat and the hierarchical model
    • (b,c) Probability of an activity being normal or abnormal, estimated by the concurrent trackers
    • (b) Normal activity: driving from home to work
    • (c) Abnormal activity: missing to get off the bus at time t2
  • 14.  
  • 15.  
  • 16.  
  • 17. Thank you

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