Paper: http://hw.oeaw.ac.at/0xc1aa500e%200x00324afb.pdf Abstract: Navigation instructions in pre- and on-trip routing services are usually based on street names and types, distances, and turn directions. However, in digital street graphs it is common that street names for separately mapped pedestrian and cycle links are missing. This leads to unsatisfactory instructions containing “unknown road” records. Often, these unnamed links run parallel to a named road, and it would be beneficial to use this information to generate instructions similar to “follow the sidewalk along Street A”, whereby “Street A” has to be determined by an algorithm. This paper introduces the Unnamed Link Naming Problem (ULNP) and presents a new approach to automatically extract suitable names to describe separately mapped pedestrian and cycle links. The approach has been tested using OpenStreetMap data and manually generated ground truth data for the second district of the city of Vienna, Austria. Results show that our best method achieves 90.7% correct matches in this challenging setting.

1. Improving Navigation
Automated Name Extraction for Separately Mapped Pedestrian and
Cycle Links
Anita Graser, Markus Straub
This work is partially funded by the Austrian BMVIT programme “Mobilität der Zukunft” under grant 844434 “PERRON” as
well as the Vienna Business Agency call “From Science To Products 2013” under the grant for project “sproute”.

2. Outline
1. Problem description
2. Approaches
3. Results
4. Discussion
5. Outlook
2

3. Unnamed Link Naming Problem (ULNP)
finding which named street an unnamed pedestrian or cycle link belongs to
3
Follow the cycle
path along
Burgring

4. Motivation
4

5. Motivation
5

6. Motivation
6

7. Motivation
7

8. Motivation
8

9. Automatic Name Extraction
9

10. Methods
Hausdorff
distance
matching
Median
distance
matching
Composite
matching:
distance and
orientation
10

11. Hausdorff distance matching
Best match = smallest Hausdorff distance
+ check distance tolerance
11
𝑀(𝐴, 𝐵) 𝐻 = 𝐻 𝐴, 𝐵 = 𝑚𝑎𝑥{ ℎ 𝐴, 𝐵 , ℎ 𝐵, 𝐴 }
ℎ 𝐴, 𝐵 = max
𝑎 ∈𝐴
{ min
𝑏 ∈𝐵
{ 𝑑 𝑎, 𝑏 }}

12. Median distance matching
Best match = smallest median distance
+ check distance tolerance
12
𝑀(𝐴, 𝐵) 𝑀 = median
𝑎 ∈𝐴
{ min
𝑏 ∈𝐵
{ 𝑑 𝑎, 𝑏 }} .

13. Composite matching
Best match = best combination of distance and similar orientation
+ check distance and angular tolerance
13
𝑀 𝐴, 𝐵 𝐶 =
median
𝑎 ∈𝐴
{ min
𝑏 ∈𝐵
{ 𝑑 𝑎, 𝑏 }}
𝛿
∗ 𝑤 𝑑 +
𝑜𝑟𝑖𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒(𝐴, 𝐵)
180
∗ 𝑤 𝑜

14. Results
14

15. Evaluation results
16
50
55
60
65
70
75
80
85
90
95
100
5 10 15 20 25 30 35 40
%
Distance tolerance 𝜹
Percentage of correct matches for the three
presented methods with varying parameter settings
Hausdorff dist.
Median dist.
Composite φ = 5°
Composite φ = 10°
Composite φ = 15°
Composite φ = 20°
Composite φ = 25°
Composite φ = 30°
Composite φ = 35°
Composite φ = 40°

16. Hausdorff distance matching
𝜹 = 𝟐𝟎 correct name no match wrong name sum
already named
26
(100%)
26
should be matched
252
(66.7%)
67
(17.7%)
59
(15.6%)
378
should not be
matched
338
(84.5%)
62
(15.5%)
400
17

17. Hausdorff & Median matching
18
Hausdorff distance
matching
Median distance
matching

18. Median distance matching
19
𝜹 = 𝟐𝟎 correct name no match wrong name sum
already named
26
(100%)
26
should be matched
284
(75.1%)
2
(0.5%)
92
(24.3%)
378
should not be
matched
308
(77.0%)
92
(23.0%)
400

19. Median & Composite matching
20
Median distance
matching
Composite
matching

20. Composite matching
21
𝜹 = 𝟐𝟎, 𝛗 = 𝟏𝟓 correct name no match wrong name sum
already named
26
(100%)
26
should be matched
350
(92.6%)
20
(5.3%)
8
(2.1%)
378
should not be
matched
353
(88.3%)
47
(11.8%)
400

21. Results
2208.07.2015
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
100%
Hausdorff
distance
Median distance Composite
Percentage of correct matches
should be matched
should not be matched

22. Composite matching issues
23
Extensions

23. Composite matching issues
24
1:n matches

24. Conclusion
Solving the ULNP
 Composite matching succeeded in matching 90.7% of test links
 better performance expected for rectangular street networks
 at roundabouts, a local relaxation of the angular tolerance might lead to better results.
Potential 2nd application: street graph generalization
 enrich the generalized link with attributes from all matching links
 automatic inference of street cross-section characteristics

25. Future work
 address shortcomings of Composite matching
 identify situations where links have to be split to be able to compute
appropriate matches
 Test transferability to other cities

26. Contact
Anita Graser
anita.graser@ait.ac.at