Xuemei Liu, James Biagioni, Jakob Eriksson, Yin Wang, George

Xuemei Liu, James Biagioni, Jakob Eriksson, Yin Wang, George

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
slide 4
Inferred road map
slide 5
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
slide 8
Mining Large-Scale, Sparse
GPS Traces for Map Inference
Comparison of Approaches
slide 9
UIC campus shuttle traces
slide 10
2 hours of Shanghai data
slide 11
Mining Large-Scale, Sparse
GPS Traces for Map Inference
Comparison of Approaches
slide 12
Mining Large-Scale, Sparse
GPS Traces for Map Inference
Comparison of Approaches
slide 13
Existing approaches
‣
k-Means clustering
-
‣
Kernel density estimation
-
‣
Edelkamp & Schrödl (2003)
Davies et al. (2006)
Trace merging
-
Liu et al. (2012)
slide 14
Why infer maps?
slide 15
Road surveys
slide 16
Rural/developing areas
slide 17
New road construction
slide 18
Road closures
slide 19
Road closures
slide 20
Opportunistic data collection
slide 21
Existing approaches
‣
k-Means clustering
-
‣
Kernel density estimation
-
‣
Edelkamp & Schrödl (2003)
Davies et al. (2006)
Trace merging
-
Liu et al. (2012)
slide 22
k-Means Clustering
Edelkamp & Schrödl (2003)
slide 23
Raw GPS traces
slide 24
Drop seeds
slide 25
Adjust seeds
slide 26
Link seeds
slide 27
Kernel Density Estimation
Davies et al. (2006)
slide 28
Raw GPS traces
slide 29
2-D histogram
slide 30
Trajectory density estimate
slide 31
Thresholded image
slide 32
Map extraction
slide 33
Sparse GPS samples
B
A
slide 34
Incorrect trajectory
B
A
slide 35
Actual trajectory
B
X
A
slide 36
Current method
slide 37
Current result
+1
+1
+1
+1
+1
+1
slide 38
Proposed result
+1
+1
slide 39
KDE variants
‣
KDE “lines”
+1
+1
+1
+1
+1
+1
‣
KDE “points”
+1
+1
slide 40
Trace Merging
Liu et al. (2012), “TC1”
slide 41
Raw GPS traces
slide 42
Segment selection
slide 43
Clustering
slide 44
Map extraction
slide 45
Quantitative Evaluation
slide 46
Ground truth map
slide 47
Inferred map
slide 48
Overlaid maps
slide 49
Overlaid maps
slide 50
True positive length
1m
1m
1m
slide 51
1m
1m
True positive length
≤ m?
1m
1m
≤ m?
1m
≤ m?
≤ m?
1m
true positive length = #  m
slide 52
1m
≤ m?
Evaluation metrics
||Inf erred|| = total inf erred road length (m)
||Ground T ruth|| = total ground truth road length (m)
slide 53
Evaluation metrics
tp = true positive length
tp
tp
precision =
, recall =
||Inf erred||
||Ground T ruth||
precision · recall
F -measure = 2 ·
precision + recall
slide 54
Chicago Evaluation
slide 55
Chicago raw GPS data
slide 56
2-sec sampling interval
slide 57
4-sec sampling interval
slide 58
8-sec sampling interval
slide 59
16-sec sampling interval
slide 60
32-sec sampling interval
slide 61
64-sec sampling interval
slide 62
128-sec sampling interval
slide 63
256-sec sampling interval
slide 64
Overall comparison
slide 65
K-means
slide 66
KDE lines
slide 67
KDE points
slide 68
KDE points
slide 69
TC1
slide 70
Shanghai Evaluation
slide 71
Shanghai raw GPS data
slide 72
Precision/recall vs. threshold
slide 73
Precision/recall vs. data size
slide 74
KDE lines
slide 75
KDE points
slide 76
TC1
slide 77
What have we learned?
KDE points
TC1
vs.
slide 78
Tale of the tape
KDE points
vs.
TC1
✗
sparsity
✓
✓
scalability
✗
✓
intersections
✗
✗
centerlines
✓
slide 79
Future work
slide 80
Thanks!
Questions?