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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

Hidden Markov Map Matching Through Noise and Sparseness

Paul Newson and John Krumm

Microsoft Research

ACM SIGSPATIAL ’09

November 6th, 2009

agenda
Agenda
  • Rules of the game
  • Using a Hidden Markov Model (HMM)
  • Robustness to Noise and Sparseness
  • Shared Data for Comparison
rules of the game
Rules of the Game
  • Some Applications:
  • Route compression
  • Navigation systems
  • Traffic Probes
map matching is trivial
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

three insights
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
three insights three choices
Three Insights, Three Choices

Match Candidate Probabilities

Route Transition Probabilities

“Junk” Points

three insights three choices1
Three Insights, Three Choices

Match Candidate Probabilities

Route Transition Probabilities

“Junk Points”

match candidate limitation
Match Candidate Limitation
  • Don’t consider roads “unreasonably” far from GPS point
route candidate limitation
Route Candidate Limitation
  • Route Distance Limit
  • Absolute Speed Limit
  • Relative Speed Limit
slide66
30 second sample period

90 second sample period

slide67
30 second sample period

90 second sample period

slide68
30 second sample period

90 second sample period

slide71
30 seconds, no added noise

30 seconds, 30 meters noise

slide72
30 seconds, no added noise

30 seconds, 30 meters noise

slide73
30 seconds, no added noise

30 seconds, 30 meters noise

slide74
30 seconds, no added noise

30 seconds, 30 meters noise

slide75
30 seconds, no added noise

30 seconds, 30 meters noise

data http research microsoft com en us um people jckrumm mapmatchingdata data htm
Data!http://research.microsoft.com/en-us/um/people/jckrumm/MapMatchingData/data.htmData!http://research.microsoft.com/en-us/um/people/jckrumm/MapMatchingData/data.htm
conclusions
Conclusions
  • Map Matching is Not (Always) Trivial
  • HMM Map Matcher works “perfectly” up to 30 second sample period
  • HMM Map Matcher is reasonably good up to 90 second sample period
  • Try our data!
questions hidden markov map matching through noise and sparseness

Questions?Hidden Markov Map Matching Through Noise and Sparseness

Paul Newson and John Krumm

Microsoft Research

ACM SIGSPATIAL ’09

November 6th, 2009