An Interactive-Voting Based
Map Matching Algorithm
Jing Yuan1, Yu Zheng2, Chengyang Zhang3, Xing Xie2 and Guangzhong Sun1
1University
of Science and Technology of China
2Microsoft Research Asia
3University of North Texas
Outline
•
•
•
•
•
•
Introduction
Our Contributions
Related Work
Interactive-Voting Algorithm
Evaluation
Conclusion and Future Work
Introduction
• Popular GPS-enabled devices enable us to collect
large amount of GPS trajectory data
Introduction
• These data are often not precise
– Measurement error: caused by limitation of
devices
– Sampling error: uncertainty introduced by
sampling
– It is desirable to match GPS points with road
segments on the map
Introduction
• In practice there exists large amount of lowsampling-rate GPS trajectories
1~2 minutes
8%
2~6 minutes
86%
0~1 minutes
34%
2~20 minutes
58%
6~20 minutes
14%
Distribution of sampling intervals of Beijing taxi dataset
Outline
•
•
•
•
•
•
Introduction
Our Contributions
Related Work
Interactive-Voting Algorithm
Evaluation
Conclusion and Future Work
Our Contributions
• We study the interactive influence of the GPS points
and propose a novel voting-based IVMM algorithm
• Extensive experiments are conducted on real datasets
• The evaluation results demonstrate the effectiveness
and efficiency of our approach for map-matching of
low-sampling rate GPS trajectories
Outline
•
•
•
•
•
•
Introduction
Our Contributions
Related Work
Interactive-Voting Algorithm
Evaluation
Conclusion and Future Work
Related Work
• Information utilized in the input data
– Geometric, topological, probabilistic, …
– Usually performs poor for low-sampling rate
trajectories
• Range of sampling points considered
– Incremental/Local algorithms
– Global algorithms
A screen shot of ST-Matching result (green
pushpins are the matched points of the red trace)
Related Work
• Sampling density of the tracking data
– Dense-sampling-rate approach
– Low-sampling-rate approach
A screen shot of ST-Matching result
(green pushpins are the matched points of the red trace)
Related Work
• Problem with ST-Matching
– The similarity function only considers two
adjacent candidate points
– The influence of points is not weighted
– The mutual influence is not considered
Outline
•
•
•
•
•
•
Introduction
Our Contributions
Related Work
Interactive-Voting Algorithm
Evaluation
Conclusion and Future Work
Problem Definition
• Given a low-sampling rate GPS trajectory T and a
road network G(V,E), find the path P from G that
matches T with its real path.
Key Insights
• Position context
influence
• Mutual influence
• Weighted
influence
f
a
b
c
d
e
System Overview
I. Candidates Preparation
Raw GPS data
Road
Network
Range Query
Candidate Road
Segments / Points
II. Position Context Analysis
III. Mutual Influence Modeling
IV. Interactive Voting
Spatial Analysis
Static Score Matrix Building
Find Sequence
Temporal Analysis
Weighted Influence
Modeling
Parallel Voting
Weighted Score Matrix
Matched Road
Segments
Candidate Graph
Step 1: Candidate Preparation
• Candidate Road Segments (CRS)
• Candidate Points (CP)
c
1
2
c
e12
1
3
e11
e12 c 2
1
e32
2
2
e
c
p1
e13
3
1
c
2
2
𝑐𝑖2
r
𝑝𝑖
𝑒𝑖1
𝑒𝑖3
𝑐𝑖1
𝑐𝑖3
e31
p3
p2
c11
𝑒𝑖2
1
4
e
c 32
c 14
c 42
p4
c 43
e42
e43
p1's candidates
c 11
• Candidate Graph G’=(V’,E’)
p2's candidates
c
1
2
p3's candidates
c
1
3
c
c 14
c 42
c 12
3
1
p4's candidates
c 22
c 32
c 43
Step 2: Position Context Analysis
• Spatial Analysis
– Measure the similarity between the candidate paths
with the shortest path of two adjacent
candidate
points
d


V cit1  cis 
p1's candidates


2
Ft cit1  ci 
c1
c

k
1' .v  v
(
e
 i 1, t  ( i , s ) ) 1
u 1 2u
3
c
c
 u 1(e .v) 
k
'
u
2
 u 1v
k
2
 i 1, t   ( i , s )
c 22
3
1



c 32

.
wi 1,t  (i , s )
p3's candidates p4's candidates
p2's candidates

c 11s
i 1i
F cit1  cis  Fs cit1  cis  Ft cit1  cis

c 14
c
2
4
c 43
Step 2: Position Context Analysis
• Spatial Analysis


V cit1  cis 





F cit1  cis  Fs cit1  cis  Ft cit1  cis

di 1i
wi 1,t  (i , s )
.
Step 2: Position Context Analysis
• Temporal Analysis
– Considers the speed constraints of the road segment


V cit1  cis 
• Spatial Temporal Function





F cit1  cis  Fs cit1  cis  Ft cit1  cis

di 1i
wi 1,t  (i , s )
.
Step 3: Mutual Influence Modeling
• Static Score Matrix
– represents the probability of candidate points to be
correct when only considering two consecutive points
– e.g.

1
2
i 1
i 1
n
Wi  diag wi  , wi  ,, wi  , wi  , wi 

2
3
n
W1  w1  , w1  ,, w1 



i  2,3,...n
j
wi   f (dist ( pi , p j ))
2
3
n
Φi  Wi M  diag Φi  , Φi  ,, Φi 

j  1,2,...n
i  1,2,3,...n
Step 3: Mutual Influence Modeling
• Distance Weight Matrix
– a (n-1) dimensional diagonal matrix for each sampling
point
– The value of each element is determined by a distancebased function f
– e.g.
j
wi   f (dist ( pi , p j ))
w1=diag{1/2,1/4,1/8}

2
3
n
Φi  Wi M  diag Φi  , Φi  ,, Φi 

j  1,2,...n
i  1,2,3,...n
Step 3: Mutual Influence Modeling
• Weighted Score Matrix
– probability when remote points are also considered
– e.g.

1
2
i 1
i 1
n
Wi  diag wi  , wi  ,, wi  , wi  , wi 

2
3
n
W1  w1  , w1  ,, w1 



i  2,3,...n
j
wi   f (dist ( pi , p j ))
2
3
n
Φi  Wi M  diag Φi  , Φi  ,, Φi 

j  1,2,...n
i  1,2,3,...n
Step 4: Interactive Voting
• Interactive Voting Scheme
– Each candidate point determines an optimal path
based on weighted score matrix
– Each point on the best path gets a vote from that
candidate point
– The points with most votes are selected
– Can be processed in parallel
Step 4: Interactive Voting
• Find optimal path for one candidate point
– The path with largest weighted score summation
– Dynamic programming
– A value is obtained to break the tie of voting
Step 4: Interactive Voting
• Find Optimal Path
• Voting results
• Matching result
Outline
•
•
•
•
•
•
Introduction
Our Contributions
Related Work
Interactive-Voting Algorithm
Evaluation
Conclusion and Future Work
Evaluation
• Dataset
– Beijing road network
– 26 GPS traces from Geolife System
12
Counts
Counts
10
8
6
4
2
0
8
7
6
5
4
3
2
1
0
0~10 10~20 20~30 30~40 40~50 50~60 60~
0~50
50~100
100~200 200~450
Average Vehicle Speed (km/h)
Number of Sampling Points
• Evaluation approach (Correct Matching Percentage)
CMP =
Correct matched points
× 100%
Number of points to be matched
Evaluation Results
• Visualized results
ST
IVMM
ST
IVMM
Evaluation Results
Correct Matching Percentage (%)
• Accuracy
85
80
75
70
65
ST-Ma tching
60
IVMM(β=7km)
55
50
0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5
Sampling Interval (minute)
Evaluation Results
• Running time
Sampling Interval (minute)
10.5
9.5
8.5
7.5
6.5
5.5
IVMM
4.5
ST-Matching
3.5
2.5
1.5
0.5
0
50
100
Running Time(s)
150
200
Evaluation Results
• Impact of different distance weight functions
Correct matching percentage (%)
72
70
68
IVMM(β=10)
66
IVMM (exponentia l)
64
IVMM (none)
IVMM (linea r)
62
60
2.5
4.5
6.5
8.5
Sampling Interval (minute)
10.5
Outline
•
•
•
•
•
•
Introduction
Our Contributions
Related Work
Interactive-Voting Algorithm
Evaluation
Conclusion and Future Work
Conclusion and Future Work
• Conclusion
– Modeling the mutual influence of the GPS sampling points
– A voting-based approach for map matching low-samplingrate GPS traces
– Evaluation with real world GPS traces
• Future Work
– The mutual influence related with the topology of the road
network
– Combination with other statistical methods, e.g., HMM and
CRF models
Thank You!