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Hidden Markov Map Matching Through Noise and Sparseness

Hidden Markov Map Matching Through Noise and Sparseness. Paul Newson and John Krumm Microsoft Research ACM SIGSPATIAL ’09 November 6 th , 2009. Agenda. Rules of the game Using a Hidden Markov Model (HMM) Robustness to Noise and Sparseness Shared Data for Comparison. Rules of the Game.

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Hidden Markov Map Matching Through Noise and Sparseness

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  1. Hidden Markov Map Matching Through Noise and Sparseness Paul Newson and John Krumm Microsoft Research ACM SIGSPATIAL ’09 November 6th, 2009

  2. Agenda • Rules of the game • Using a Hidden Markov Model (HMM) • Robustness to Noise and Sparseness • Shared Data for Comparison

  3. Rules of the Game • Some Applications: • Route compression • Navigation systems • Traffic Probes

  4. Map Matching is Trivial! “I am not convinced to which extent the problem of path matching to a map is still relevant with current GPS accuracy” - Anonymous Reviewer 3

  5. Except When It’s Not…

  6. Our Test Route

  7. Three Insights • Correct matches tend to be nearby • Successive correct matches tend to be linked by simple routes • Some points are junk, and the best thing to do is ignore them

  8. Mapping to a Hidden Markov Model (HMM)

  9. Three Insights, Three Choices Match Candidate Probabilities Route Transition Probabilities “Junk” Points

  10. Match Error is Gaussian (sort of)

  11. Route Error is Exponential

  12. Three Insights, Three Choices Match Candidate Probabilities Route Transition Probabilities “Junk Points”

  13. Match Candidate Limitation • Don’t consider roads “unreasonably” far from GPS point

  14. Route Candidate Limitation • Route Distance Limit • Absolute Speed Limit • Relative Speed Limit

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