The document describes an interactive-voting based map matching algorithm that improves upon previous ST-matching methods. It introduces mutual and weighted influence between candidate points to better model their reciprocal relationships. The algorithm first generates candidate road segments for each GPS point, then calculates weighted score matrices to determine optimal paths. Points on the best paths vote for their candidate points, and the candidates with most votes form the final matched path. Experiments show the new algorithm achieves higher matching accuracy than ST-matching with only a small increase in runtime.

1. An Interactive-Voting Based
Map Matching Algorithm
1
By:
Jianjun Luo, Sushma Tayanna, Wen Liu,
Yousef Fadila, Zhanfeng Huang

2. Worcester Polytechnic Institute
Agenda
Background
ST-Matching
Interactive-Voting
Experiments & Evaluation
Conclusion

3. Background
Map Matching and Related Work
3

4. Worcester Polytechnic Institute
Background - Motivation
• Vehicle navigation
• Fleet management
• Intelligent transport system
• Other services
Image sources:
http://www.stcl.com/wp-content/uploads/2014/03/fleet_management.jpg
http://www.thetruthaboutcars.com/wp-content/uploads/2012/03/Sanyo-new-car-navigation-system.jpg
https://conceptdraw.com/a862c3/p1/preview/640/pict--vehicular-network-diagram-intelligent-transportation-system

5. Worcester Polytechnic Institute
Background - Motivation
• Low accuracy from positioning errors and sampling errors
• Large quantity of low-sampling-rate GPS tracking data

6. Worcester Polytechnic Institute
Background - Map matching
• To match an original GPS tracking data to a digital map or a
digital road network

7. Worcester Polytechnic Institute
Background - Low Sampling Rate
• Simple solution for high-sampling-rate data
─Weighted distance
• Challenge
Missing details
The matched road segments disconnected

8. Worcester Polytechnic Institute
Approaches - Background
According to the additional information used:
• Geometric
• Topological
• Probabilistic
• Advanced techniques
Figure Cite:
J. S. Greenfeld, “Matching GPS Observations to Locations on a Digital Map”, In proceedings of the 81st Annual
Meeting of the Transportaion Research Board, Wasington D. C, 2002.

9. Worcester Polytechnic Institute
Background - Approaches
According to the range of sampling points:
• Local/incremental
• Global
Figure Cite:
A. Civilis, C. S. Jensen, J. Nenortaite, and S. Pakalnis, “Techniques for Efficient Road-network-based Tracking of
Moving Objects”, IEEE Transactions on Knowledge and Date Engineering, vol. 17(5), pp. 698- 711, 2005.

10. Mathematical Preliminary
and
ST-Matching Algorithm
10

11. Worcester Polytechnic Institute
Mathematical Preliminary
Problem Definition
• GPS trajectory: p1 -> p2 ->...->pn
• Road network: a directed graph G(V, E)
• Path: a set of connected road segments, P: e1 -> e2 -> … -> en
Given the road network G and
a raw GPS trajectory T, find a
path in G which matches T with
its real path

12. Worcester Polytechnic Institute
ST-Matching: System Overview

13. Worcester Polytechnic Institute
Candidates Preparation
• retrieve a set of candidate road
segments (CRS) for each sampling
point by a range query -- Pi: CRSi
• Candidate points (CP): the projection
of the sampling point onto the road
segments or the endpoint
• Rephrase problem: how to choose one
candidate from each set so that
best matches

14. Worcester Polytechnic Institute
Spatial-Temporal Analysis
• Spatial Analysis Assumption: a driver is more likely to choose a shorter route when driving
• Observation Probability: the likelihood that a GPS sampling point matches a candidate point
• Transmission Probability: the likelihood that the “true” path from two sampling points matches the
shortest path from two candidates
• Spatial analysis function: the product of above

15. Worcester Polytechnic Institute
Spatial-Temporal Analysis
• Temporal Analysis Assumption: a driver considers the speed constraints of the road segments
• Temporal analysis function:
• ST function: the product of spatial & temporal analysis function
A candidate path sequence:
The candidate graph

16. Worcester Polytechnic Institute
Result Matching

17. Worcester Polytechnic Institute
Drawbacks on ST-Matching
● Solely with respect to two
adjacent candidate points,
whereas the position of a
sampling point is influenced
by all its neighbouring points
● Uses a simple summation of
all the values in the trajectory
● Doesn’t consider the
reciprocal influence

18. An Improved Version:
Interactive-Voting
18

19. Worcester Polytechnic Institute
Key Insights of Interactive-Voting
• Key insights
─ Mutual influence
─ Weighted influence (based on distance)
a
b c d e
f
Jing Yuan, Yu Zheng, et al. An Interactive-Voting based Map Matching Algorithm. MDM 2010.

20. Worcester Polytechnic Institute
Interactive-Voting Solution Overview
ST- Matching Part Interactive Voting Part

21. Worcester Polytechnic Institute
Mutual Influence Modeling
𝑴 =
𝟎. 𝟖 𝟎. 𝟔 −∞ −∞ −∞ −∞ −∞
𝟎. 𝟕 𝟎. 𝟓 −∞ −∞ −∞ −∞ −∞
𝟎. 𝟔 𝟎. 𝟒 −∞ −∞ −∞ −∞ −∞
−∞ −∞ 𝟎. 𝟑 𝟎. 𝟕 −∞ −∞ −∞
−∞ −∞ 𝟎. 𝟐 𝟎. 𝟒 −∞ −∞ −∞
−∞ −∞ −∞ −∞ 0.3 0.5 0.4
−∞ −∞ −∞ −∞ 0.6 0.7 0.9
𝜱1 =
0.4 0.3 −∞ −∞ −∞ −∞ −∞
0.35 0.25 −∞ −∞ −∞ −∞ −∞
0.3 0.2 −∞ −∞ −∞ −∞ −∞
−∞ −∞ 0.075 0.175 −∞ −∞ −∞
−∞ −∞ 0.05 0.1 −∞ −∞ −∞
−∞ −∞ −∞ −∞ 0.038 0.063 0.05
−∞ −∞ −∞ −∞ 0.075 0.088 0.113
𝜱2 =
0.4 0.3 −∞ −∞ −∞ −∞ −∞
0.35 0.25 −∞ −∞ −∞ −∞ −∞
0.3 0.2 −∞ −∞ −∞ −∞ −∞
−∞ −∞ 0.15 0.35 −∞ −∞ −∞
−∞ −∞ 0.1 0.2 −∞ −∞ −∞
−∞ −∞ −∞ −∞ 0.08 0.13 0.1
−∞ −∞ −∞ −∞ 0.15 0.18 0.23
𝜱3 =
0.2 0.15 −∞ −∞ −∞ −∞ −∞
0.175 0.125 −∞ −∞ −∞ −∞ −∞
0.15 0.1 −∞ −∞ −∞ −∞ −∞
−∞ −∞ 0.15 0.35 −∞ −∞ −∞
−∞ −∞ 0.1 0.2 −∞ −∞ −∞
−∞ −∞ −∞ −∞ 0.15 0.25 0.2
−∞ −∞ −∞ −∞ 0.3 0.35 0.45
𝜱4 =
0.1 0.075 −∞ −∞ −∞ −∞ −∞
0.088 0.063 −∞ −∞ −∞ −∞ −∞
0.075 0.05 −∞ −∞ −∞ −∞ −∞
−∞ −∞ 0.075 0.175 −∞ −∞ −∞
−∞ −∞ 0.05 0.1 −∞ −∞ −∞
−∞ −∞ −∞ −∞ 0.15 0.25 0.2
−∞ −∞ −∞ −∞ 0.3 0.35 0.45
𝑾 𝟏 =
1/2
1/4
1/8
𝑝2 𝑝 𝑛𝑝3
𝑤𝑖𝑗 = 2
−(𝑑𝑖𝑠𝑡 𝑝𝑖,𝑝 𝑗
p1's candidates p2's candidates p3's candidates p4's candidates
1
1c
2
1c
3
1c
1
2c
2
2c
1
3c
2
3c
1
4c
2
4c
3
4c
𝐹𝑠 𝑐𝑖−1
𝑡
→ 𝑐𝑖
𝑠
= 𝑁 𝑐𝑖
𝑠
∗ 𝑉 𝑐𝑖−1
𝑡
→ 𝑐𝑖
𝑠
0.8
0.4
0.3
0.4
0.7
0.6
0.5
0.6
0.2
0.7
0.8 * ½
𝜱𝒊 = 𝑾𝒊 𝑴

22. Worcester Polytechnic Institute
Interactive-Voting Scheme
 Each candidate point determines an optimal path
based on their own weighted score matrix 𝜱𝒊
 Each point on the best path gets a vote from
that candidate point
p1's candidates p2's candidates p3's candidates p4's candidates
1
1c
2
1c
3
1c
1
2c
2
2c
1
3c
2
3c
1
4c
2
4c
3
4c
+1
+1
+1
+1
+1
+2+1
+1
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
2
1
CP Votes
𝑐1
1
𝑐1
2
𝑐1
3
𝑐2
1
𝑐2
2
𝑐3
1
𝑐3
2
𝑐4
1
𝑐4
2
𝑐4
3

23. Worcester Polytechnic Institute
Select The Global Optimal Path
CP Votes
𝑐1
1
8
𝑐1
2
1
𝑐1
3 1
𝑐2
1
9
𝑐2
2
1
𝑐3
1 1
𝑐3
2 9
𝑐4
1 1
𝑐4
2 2
𝑐4
3 7
The points with the most number of votes are selected
𝑐1
1
 𝑐2
1
 𝑐3
2
 𝑐3
2

24. Interactive-Voting
Experiments & Evaluation
24

25. Evaluation
Road networks
• 58,624 vertices and 130,714 road segments.
• The vertical length : 47.7 km and horizontal length : 52.6 km
Fig: Road network of Beijing

26. Evaluation
GPS Tracking data:
• Real human labeled true path data.
• 26 trajectories with varying number of points and average speed.

27. Evaluation

28. Evaluation
Paramater selection:
• Low Sampling rate- 30 seconds to 10.5 minutes
• IVMM algorithm: k=5 ,maximum number of candidates for each sampling point.
• Radius of range query= 100 meters
• Distance weight function with β as 7 kms.
• Java on Win 7 OS.

29. Evaluation
• Comparison between ST-matching and IVMM algorithm.
• Efficiency: Running time
• Quality: CMP(Correct matching percentage)

30. ST-Matching V.S. IVMM

31. ST-Matching V.S. IVMM

32. CMP

33. Running Time

34. Conclusion
34

35. Strength
1.Provide a developed method for low-sampling-rate GPS data trajectory
drawing
2.Improve from the previous paper, the result is really effective
–Mutual influence (considering a farther distance)
–Weighted influence (based on distance)
3.Runtime: a minimum increase

36. Weakness
1. Space cost is definitely increased, since every candidate needs to compute a
weighted score matrix (ST-Matching only needs one for dynamic programing)
2. Does not suitable for concurrent query processing (ST-Matching does)
3. The comparison of linear and nonlinear distance weight functions is insufficient
4. aj , a parameter for matrix dimension is not pre-defined in the paper
5. Many handcrafted assumptions, maybe machine learning/statistical models can
be introduced

37. Thank You!
37