A method to automatically identify road centerlines from georeferenced smartphone data

1. A method to automatically identify road centerlines fromgeoreferenced smartphone data XIV Brazilian Symposium on GeoInformatics (GEOINFO 2013) George H. R. Costa, Fabiano Baldo {dcc6ghrc, baldo}@joinville.udesc.br 25/11/2013

2. Agenda • Introduction • Objective • Related work • Proposed method • Tests and Results • Conclusion and Future work

3. Introduction • Digital road maps have gained fundamental role in population’s daily life • Navigation systems etc. • It is essential that maps reflect reality as well as possible • Generated from accurate data; • Periodic updates. • Possible source of data: GPS traces

4. Introduction • By combining many traces it is possible to generate maps • Example: OpenStreetMap • Users use uploaded traces to create/update maps • However, all map editing is done manually • Automatic solutions would be more effective • Could allow maps to be updated faster • Feasible: [Brüntrup et.al. 2005] and [Cao and Krumm 2009] also support this idea

5. Challenges • How to obtain the data needed to generate maps? • Smartphones • Contain many sensors, including a GPS receiver • Represent half of the Brazilian cellphone market [GFK 2013] Source: Garmin

6. Challenges • To create road maps it is necessary to find the roads’ centerlines • How to analyze the traces to identify road centerlines? • Approximated result • Evolutive algorithm Source: author

7. Objective • Therefore, the objective of this work is to: Propose a method to identify road centerlines using an evolutive algorithm in order to generate and update road maps

8. Related work • Characteristics gathered from other works: • Independence from initial maps [Brüntrup et.al. 2005; Cao and Krumm2009; Jang et.al. 2010] • Usage of heuristics to remove noise from the traces [Brüntrup et.al. 2005; Cao and Krumm2009;Zhang et.al. 2010; Niu et.al. 2011] • Characteristic introduced by this work: • Traces’ date of recording is taken into account to generate up-to-date maps

9. Data source Source: author

10. Preprocessing • Reduces noise; saves all traces to database Source: author

11. Road centerlines • Query database to get all traces ordered by date and accuracy • Most recent first • Most accurate first Source: author

12. Road centerlines • For each point k of each trace j (): • Identify nearby points • All points that intersect a buffer around Source: author

13. Road centerlines • Points with a direction of movement different than are discarded • set • How to analyze the set to find the road centerline? Source: author

14. Road centerlines • It is assumed that it is only possible to find an approximated solution • Road centerline = weighted combination between: • Date of recording; • Accuracy; • Distance from a candidate solution to all points selected (set). • Chosen algorithm: evolutive algorithm

15. Road centerlines • : candidate solution • : set of selected points • : influence of time (date of recording) • : influence of accuracy • : influence of distance

16. Road centerlines • , , : Multiply the value of the corresponding influence to prioritize desired characteristics

17. Road centerlines • Recent traces: weight closer to 1 • Older traces: weight closer to 0 Influence of Time

18. Road centerlines Influence of Accuracy Weight Accuracy

19. Road centerlines Influence of Distance Weight Distance

20. Road centerlines Closer to highest concentration of points: smallest overall distance Closer to points high better accuracy Source: author

21. Road centerlines • Evolutive algorithm • 60 generations • 20 candidate solutions per generation • Elitism: 2 best candidate solutions are preserved to the next generation Evolutive algorithm loop: Source: author

22. Road centerlines • Evolutive algorithm finds centerline close to • Next step: repeat process for • If has already been used, skip to the next point Source: author

23. Results • Implemented in Python • DB: PostgreSQL + PostGIS • Data collected between 27/01/2013 e 15/06/2013 • 4237 traces • 966698 points

24. Results • Tests: comparison between • Proposed method’s results • Satellite images • Google Earth • Executed on places with complex road structures

25. Tests (1) Satellite image Roads intersect Source: Google Earth / author

26. Tests (1) Points collected (filtered) Source: Google Earth / author

27. Tests (1) Final result Direction of movement differentiates traces Way centerline It is possible to improve filtering... Source: Google Earth / author

28. Tests (2) Satellite image Roads with same direction of movement Roads with different direction of movement Source: Google Earth / author

29. Tests (2) Points collected (filtered) Source: Google Earth / author

30. Tests (2) Final result It is possible to improve filtering... Direction of movement differentiates traces It is possible to improve parameters... Source: Google Earth / author

31. Results • Small difference between the satellite images and the method’s results • Average distance (100 points): 2.95 meters • Cannot affirm which one is more accurate • Certain questions cannot be controlled • Ex.: satellite images might be somewhat out of position

32. Conclusion • Different from similar methods because: • Takes into consideration the influence of the traces’ date of recording; • Collects data using smartphones; • Finds centerlines using evolutive algorithm. • Tests showed little difference to satellite images • It is still possible to optimize parameters to achieve better results

33. Future work • Improve collected traces’ reliability • Ex.: Kalman Filter • Different update policies for each region • Downtown: more data, only accept better accuracy • Rural areas: less data, accept older data • Mining more information • Traffic lights • Pot holes

34. Bibliografia • Brüntrup, R. et. al. (2005) “Incremental map generation with GPS traces”. In: Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems. • Cao, L. e Krumm, J. (2009) “From GPS traces to a routable road map”. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, EUA: ACM Press. • Garmin (2010) “Garmin-Asus smartphones reach new markets”. <http://garmin.blogs.com/ my_weblog/2010/09/garmin-asus-around-the-globe.html> (accessed on Nov 22). • GFK (2013) “GfKTEMAX BRASIL T2 2013: Crescimento no mercado com forte influência de materiais de escritório e periféricos”. <http://www.gfk.com/br/news-and-events/press-room/press-releases/Paginas/TEMAX-BRASIL-T2-2013.aspx> (accessed on Nov 18). • Jang, S., Kim, T. e Lee, E. (2010) “Map Generation System with Lightweight GPS Trace Data”. In: International Conference on Advanced Communication Technology. • Niu, Z., Li, S. e Pousaeid, N. (2011) “Road extraction using smart phones GPS”. In: Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications. New York, EUA: ACM Press. • Zhang, L., Thiemann, F., Sester, M. (2010) “Integration of GPS traces with road map”. In: Proceedings of the 2nd International Workshop On Computational Transportation Science. San Jose, EUA. ACM Press.

35. A method to automatically identify road centerlines fromgeoreferenced smartphone data XIV Brazilian Symposium on GeoInformatics (GEOINFO 2013) George H. R. Costa, Fabiano Baldo {dcc6ghrc, baldo}@joinville.udesc.br 25/11/2013