1. 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 Mobilitt 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. 2. Outline 1. Problem description 2. Approaches 3. Results 4. Discussion 5. Outlook 2
3. 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. 4. Motivation 4
5. 5. Motivation 5
6. 6. Motivation 6
7. 7. Motivation 7
8. 8. Motivation 8
9. 9. Automatic Name Extraction 9
10. 10. Methods Hausdorff distance matching Median distance matching Composite matching: distance and orientation 10
11. 11. Hausdorff distance matching Best match = smallest Hausdorff distance + check distance tolerance 11 (, ) = , = { , , , } , = max { min { , }}
12. 12. Median distance matching Best match = smallest median distance + check distance tolerance 12 (, ) = median { min { , }} .
13. 13. Composite matching Best match = best combination of distance and similar orientation + check distance and angular tolerance 13 , = median { min { , }} + (, ) 180
14. 14. Results 14
15. 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 Percentage of correct matches for the three presented methods with varying parameter settings Hausdorff dist. Median dist. Composite = 5 Composite = 10 Composite = 15 Composite = 20 Composite = 25 Composite = 30 Composite = 35 Composite = 40
16. 16. Hausdorff distance matching = 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. 17. Hausdorff & Median matching 18 Hausdorff distance matching Median distance matching
18. 18. Median distance matching 19 = 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. 19. Median & Composite matching 20 Median distance matching Composite matching
20. 20. Composite matching 21 = , = 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. 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. 22. Composite matching issues 23 Extensions
23. 23. Composite matching issues 24 1:n matches
24. 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. 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. 26. Contact Anita Graser anita.graser@ait.ac.at