hidden markov map matching through noise and sparseness paul newson and john krumm microsoft...
TRANSCRIPT
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Hidden Markov Map Matching Through Noise and Sparseness
Paul Newson and John KrummMicrosoft ResearchACM SIGSPATIAL ’09November 6th, 2009
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Agenda
• Rules of the game• Using a Hidden Markov Model (HMM)• Robustness to Noise and Sparseness• Shared Data for Comparison
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Rules of the GameSome Applications:• Route compression• Navigation systems• Traffic Probes
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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
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Except When It’s Not…
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Our Test Route
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Three Insights
1. Correct matches tend to be nearby
2. Successive correct matches tend to be linked by simple routes
3. Some points are junk, and the best thing to do is ignore them
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Mapping to a Hidden Markov Model (HMM)
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Three Insights, Three Choices
1. Match Candidate Probabilities
2. Route Transition Probabilities
3. “Junk” Points
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Match Error is Gaussian (sort of)
0 2 4 6 8 10 12 14 16 18 200
0.02
0.04
0.06
0.08
0.1
0.12
GPS Difference Probability
Data Histogram Gaussian Distribution
Distance Between GPS and Matched Point (meters)
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Route Error is Exponential
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
1
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Distance Difference Probability
Data Histogram Exponential Distribution
abs(great circle distance - route distance) (meters)
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Three Insights, Three Choices
1. Match Candidate Probabilities
2. Route Transition Probabilities
3. “Junk Points”
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Match Candidate Limitation
• Don’t consider roads “unreasonably” far from GPS point
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Route Candidate Limitation
• Route Distance Limit• Absolute Speed Limit• Relative Speed Limit
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Robustness to Sparse Data
1 2 5 10 20 30 45 60 90 120
180
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0
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1Error vs. Sampling Period
Sampling Period (seconds)
Rout
e M
ismat
ch F
racti
on
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Robustness to Sparse Data
1 2 5 10 20 30 45 60 90 120
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540
600
0
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1Error vs. Sampling Period
Sampling Period (seconds)
Rout
e M
ismat
ch F
racti
on
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30 second sample period 90 second sample period
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30 second sample period 90 second sample period
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30 second sample period 90 second sample period
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Robustness to NoiseAt 30 second sample period
4.07 10 15 20 30 40 50 75 1000
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Accuracy vs. Measurement Noise
Noise Standard Deviation (meters)
Frac
tion
of R
oute
Inco
rrec
t
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30 seconds, no added noise
30 seconds, 30 meters noise
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30 seconds, no added noise 30 seconds, 30 meters noise
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30 seconds, no added noise 30 seconds, 30 meters noise
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30 seconds, no added noise 30 seconds, 30 meters noise
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30 seconds, no added noise
30 seconds, 30 meters noise
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Data!http://research.microsoft.com/en-us/um/people/jckrumm/MapMatchingData/data.htm
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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!
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Questions?Hidden Markov Map Matching Through Noise and Sparseness
Paul Newson and John KrummMicrosoft ResearchACM SIGSPATIAL ’09November 6th, 2009