Outline <ul><li>1. Introduction </li></ul><ul><li>2. Hierarchical activity model </li></ul><ul><li>3. Inference </li></ul>...
1. Introduction <ul><li>Track and predict a user’s location </li></ul><ul><li>Infer a user’s mode and predict when and whe...
2. Hierarchical activity model <ul><li>Locations and transportation modes </li></ul><ul><li>Trip segments </li></ul><ul><l...
two  time slices k − 1  and  k novelty mode next goal and current trip segment GPS sensor measurement
Locations and transportation modes <ul><li>x k  = 〈 l k   ,  v k  ,  c k 〉 </li></ul><ul><li>z k   : GPS sensor measuremen...
Trip segments <ul><li>A trip segment is defined by its start location,  t k .start , end location,  t k .end , and the mod...
Goals & Novelty <ul><li>A goal represents the current target location of the person. </li></ul><ul><li>g k  : goal </li></...
3. Inference <ul><li>Flat model </li></ul><ul><ul><li>Rao–Blackwellized particle filter for estimation in the flat model <...
Rao–Blackwellized particle filter for estimation in the flat model <ul><li>Sampling step </li></ul><ul><li>Kalman filter s...
RBPF algorithm for the flat model Sampling step Kalman filter step Importance weights
RBPF algorithm for the hierarchical model
Detecting novel behavior
4. Learning <ul><li>Finding mode transfer locations </li></ul><ul><ul><li>estimate  the mode transition probabilities usin...
5. Experimental results <ul><li>Activity model learning </li></ul><ul><li>Empirical comparison to other models </li></ul><...
Activity model learning <ul><li>six most common transportation goals  </li></ul><ul><li>frequently used bus stops and park...
Empirical comparison to other models
Error detection
6. Application:  Opportunity Knocks <ul><li>The name of our system is derived from the desire to provide our users with a ...
Opportunity Knocks(1)
Opportunity Knocks(2)
Opportunity Knocks(3)
Opportunity Knocks(4)
Opportunity Knocks(5)
Opportunity Knocks(6)
7. Conclusions <ul><li>provide predictions of movements to distant goals, and support a simple and effective strategy for ...
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Learning And Inferring Transportation Routines

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Learning And Inferring Transportation Routines

  1. 1.
  2. 2.
  3. 3. Outline <ul><li>1. Introduction </li></ul><ul><li>2. Hierarchical activity model </li></ul><ul><li>3. Inference </li></ul><ul><li>4. Learning </li></ul><ul><li>5. Experimental results </li></ul><ul><li>6. Application: Opportunity Knocks </li></ul><ul><li>7. Conclusions </li></ul>
  4. 4. 1. Introduction <ul><li>Track and predict a user’s location </li></ul><ul><li>Infer a user’s mode and predict when and where </li></ul><ul><li>Infer the locations of transportation destinations. </li></ul><ul><li>Indicate he or she has made an error. </li></ul>
  5. 5. 2. Hierarchical activity model <ul><li>Locations and transportation modes </li></ul><ul><li>Trip segments </li></ul><ul><li>Goals </li></ul><ul><li>Novelty </li></ul>
  6. 6. two time slices k − 1 and k novelty mode next goal and current trip segment GPS sensor measurement
  7. 7. Locations and transportation modes <ul><li>x k = 〈 l k , v k , c k 〉 </li></ul><ul><li>z k : GPS sensor measurements </li></ul><ul><ul><li>generated by the person carrying a GPS sensor </li></ul></ul><ul><li>m k : transportation mode </li></ul><ul><ul><li>BUS , FOOT , CAR , and BUILDING . </li></ul></ul><ul><li>The domain of τ k is the set of outgoing neighbors of the current edge; </li></ul><ul><li>θ k is picked from the edges close to z k </li></ul>
  8. 8. Trip segments <ul><li>A trip segment is defined by its start location, t k .start , end location, t k .end , and the mode of transportation, t k .mode . </li></ul><ul><li>: trip switching </li></ul><ul><li>: a counter that measures the time steps until the next transportation mode is entered. </li></ul>
  9. 9. Goals & Novelty <ul><li>A goal represents the current target location of the person. </li></ul><ul><li>g k : goal </li></ul><ul><li>: goal switching node </li></ul><ul><li>n k : indicating whether a user’s behavior is consistent with historical patterns. </li></ul>
  10. 10. 3. Inference <ul><li>Flat model </li></ul><ul><ul><li>Rao–Blackwellized particle filter for estimation in the flat model </li></ul></ul><ul><ul><li>RBPF algorithm for the flat model </li></ul></ul><ul><li>Hierarchical model </li></ul><ul><ul><li>RBPF algorithm for the hierarchical model </li></ul></ul><ul><ul><li>Detecting novel behavior </li></ul></ul>
  11. 11. Rao–Blackwellized particle filter for estimation in the flat model <ul><li>Sampling step </li></ul><ul><li>Kalman filter step </li></ul><ul><li>Importance weights </li></ul>histories location of the person car location
  12. 12.
  13. 13. RBPF algorithm for the flat model Sampling step Kalman filter step Importance weights
  14. 14. RBPF algorithm for the hierarchical model
  15. 15. Detecting novel behavior
  16. 16. 4. Learning <ul><li>Finding mode transfer locations </li></ul><ul><ul><li>estimate the mode transition probabilities using the expectation maximization (EM) algorithm </li></ul></ul><ul><li>Finding goals </li></ul><ul><ul><li>a person typically spends extended periods of time whether indoors or outdoors. </li></ul></ul><ul><li>Estimating transition matrices </li></ul><ul><ul><li>use EM to estimate the transition matrices </li></ul></ul>
  17. 17. 5. Experimental results <ul><li>Activity model learning </li></ul><ul><li>Empirical comparison to other models </li></ul><ul><li>Error detection </li></ul>
  18. 18. Activity model learning <ul><li>six most common transportation goals </li></ul><ul><li>frequently used bus stops and parking lots, </li></ul><ul><li>the common routes using different modes of transportation </li></ul>
  19. 19. Empirical comparison to other models
  20. 20. Error detection
  21. 21. 6. Application: Opportunity Knocks <ul><li>The name of our system is derived from the desire to provide our users with a source of computer generated opportunities </li></ul><ul><li>it plays a sound like a door knocking to get the user’s attention. </li></ul>
  22. 22. Opportunity Knocks(1)
  23. 23. Opportunity Knocks(2)
  24. 24. Opportunity Knocks(3)
  25. 25. Opportunity Knocks(4)
  26. 26. Opportunity Knocks(5)
  27. 27. Opportunity Knocks(6)
  28. 28. 7. Conclusions <ul><li>provide predictions of movements to distant goals, and support a simple and effective strategy for detecting novel events that may indicate user errors </li></ul><ul><li>system limitation : it uses fixed thresholds to extract goals and mode transfer locations. </li></ul>

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