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Mining Large-Scale, Sparse
GPS Traces for Map Inference
Comparison of Approaches
Xuemei Liu, James Biagioni, Jakob Eriksson,
Yin Wang, George Forman, Yanmin Zhu
Mining Large-Scale, Sparse
GPS Traces for Map Inference
Comparison of Approaches
slide 2
Mining Large-Scale, Sparse
GPS Traces for Map Inference
Comparison of Approaches
slide 3
Raw GPS traces
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Inferred road map
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Mining Large-Scale, Sparse
GPS Traces for Map Inference
Comparison of Approaches
slide 6
slide 7interval
Chicago shuttle data, 1 second
Shanghai taxi data, 16/61 second interval
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Mining Large-Scale, Sparse
GPS Traces for Map Inference
Comparison of Approaches
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UIC campus shuttle traces
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2 hours of Shanghai data
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Mining Large-Scale, Sparse
GPS Traces for Map Inference
Comparison of Approaches
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Mining Large-Scale, Sparse
GPS Traces for Map Inference
Comparison of Approaches
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Existing approaches
‣
k-Means clustering
-
‣
Kernel density estimation
-
‣
Edelkamp & Schrödl (2003)
Davies et al. (2006)
Trace merging
-
Liu et al. (2012)
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Why infer maps?
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Road surveys
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Rural/developing areas
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New road construction
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Road closures
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Road closures
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Opportunistic data collection
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Existing approaches
‣
k-Means clustering
-
‣
Kernel density estimation
-
‣
Edelkamp & Schrödl (2003)
Davies et al. (2006)
Trace merging
-
Liu et al. (2012)
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k-Means Clustering
Edelkamp & Schrödl (2003)
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Raw GPS traces
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Drop seeds
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Adjust seeds
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Link seeds
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Kernel Density Estimation
Davies et al. (2006)
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Raw GPS traces
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2-D histogram
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Trajectory density estimate
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Thresholded image
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Map extraction
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Sparse GPS samples
B
A
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Incorrect trajectory
B
A
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Actual trajectory
B
X
A
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Current method
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Current result
+1
+1
+1
+1
+1
+1
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Proposed result
+1
+1
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KDE variants
‣
KDE “lines”
+1
+1
+1
+1
+1
+1
‣
KDE “points”
+1
+1
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Trace Merging
Liu et al. (2012), “TC1”
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Raw GPS traces
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Segment selection
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Clustering
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Map extraction
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Quantitative Evaluation
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Ground truth map
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Inferred map
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Overlaid maps
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Overlaid maps
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True positive length
1m
1m
1m
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1m
1m
True positive length
≤ m?
1m
1m
≤ m?
1m
≤ m?
≤ m?
1m
true positive length = #  m
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1m
≤ m?
Evaluation metrics
||Inf erred|| = total inf erred road length (m)
||Ground T ruth|| = total ground truth road length (m)
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Evaluation metrics
tp = true positive length
tp
tp
precision =
, recall =
||Inf erred||
||Ground T ruth||
precision · recall
F -measure = 2 ·
precision + recall
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Chicago Evaluation
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Chicago raw GPS data
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2-sec sampling interval
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4-sec sampling interval
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8-sec sampling interval
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16-sec sampling interval
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32-sec sampling interval
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64-sec sampling interval
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128-sec sampling interval
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256-sec sampling interval
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Overall comparison
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K-means
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KDE lines
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KDE points
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KDE points
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TC1
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Shanghai Evaluation
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Shanghai raw GPS data
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Precision/recall vs. threshold
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Precision/recall vs. data size
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KDE lines
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KDE points
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TC1
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What have we learned?
KDE points
TC1
vs.
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Tale of the tape
KDE points
vs.
TC1
✗
sparsity
✓
✓
scalability
✗
✓
intersections
✗
✗
centerlines
✓
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Future work
slide 80
Thanks!
Questions?