A structure-oriented matching approach for the integration of different road networks

1. A structure-oriented matching approach for the integration of different road networks M. Zhang & L. Meng meng.zhang@bv.tum.de www.cato-tum.de Department of Cartography, TU Munich Moscow, Russia 4~10 August , 2007

2. 1. Background • What is matching? The process aims at establishing logical connections between corresponding objects in two comparable datasets is termed as data matching. • Why matching? • To increase the applicability of existing data • To evaluate and improving the data quality • To easily maintain and update databases in MRDB • To provide navigation solutions for LBS • …

3. 1. Background • Problem in the matching • One of the datasets contains little or no meaningful semantic information at all, such as street name. • The context conditions are too complicated (like around highways). • In some sense a street network can be regarded as one unit constituted by various road structures. Since the various road structures take on quite different geometrical or topological characteristics from each other, it is hardly possible to match all of them efficiently by using the same criteria or methods.

4. 1. Background • Solution • Keeping in mind these unfavourable conditions, we proposed a structure-oriented approach for the matching between different street networks, which can be characterized by three processes: a) Structure recognition; b) Process modeling; c) Process exectuion.

5. 2. Structure Recognition Structure recognition aims atclassifying and identifying typical object clusters, i.e. road structures, based on their spatial and/or semantic characteristics.Different structure categories will necessitate different matching methods.

6. 2. Structure Recognition 2.1 Recognition of roundabouts 2.2 Recognition of the dual-carriageways 2.3 Recognition of navigation stubbles 2.4 Recognition of the narrow passages 2.5 Recognition of the slip roads around cloverleaf junctions 2.6 Normal single carriageways

7. 3. Process modeling Structure recognition is followed by process modeling, which assigns different matching algorithms as well as the necessary criteria to differentiate structure categories. Buffer Growing (BG) along with the necessary parameters is an efficient algorithm for the general task of line matching. Hereby it is employed to as the basic matching algorithm in our proposed matching approach

8. 3. Process modeling 4.1 Matching of single carriageways, incl. slip roads, narrow passages, navigation stubbles and normal single carriageways Step1: Instantiation of the reference polyline

9. s 3. Process modeling Step 2: Identification of possible matching candidates Step 3: Exclusion of incorrect candidates α

10. 3. Process modeling Step 4: Exactness inspection of the matching candidates

11. 3. Process modeling 4.2 Matching of the roundabouts The roundabouts reveal quite similar matching process to that of the single carriageways illustrated. The unique major difference occurs on the definition of the criteria for the purpose of excluding the incorrect matching suggestions as well as selecting the best matching candidate. In the process of matching corresponding roundabouts between different datasets, the criteria are related to three geometrical characteristics: • Characteristic 1: Area • Characteristic 2: Position • Characteristic 3: Shape

12. The first polyline The second polyline 3. Process modeling 4.2 Matching of the dual carriageways Step 1: Preprocessing of the reference parallel roads • Proofreading the orientation of the reference parallel roads • Sequencing the reference parallel roads according to their relative position (ABDCA is in clockwise order).

13. 3. Process modeling Step 2: Line matching of each road of the matching reference The improved promising matching candidates of A→B :J→Kand M→N. The improved promising matching candidates of C→D: J→Kand M→N.

14. d 3. Process modeling Step 3: Step Selection of the optimal combination A The combination is confirmed as the ultimate matching result

15. 4. Process execution

16. 5. Case Study • Test data • Basis DLM, which is from German mapping agencies and captured through map digitization in combination of semiautomatic object extraction from imagery data. • Tele Atlas: which is one of the most important data suppliers for car navigation systems. • Main Task • Enriching Basis DLM with the routing-relevant information from Tele Atlas • Problem • Absence of some important road attributes in Basis DLM, such as street name, etc.

17. 5. Case Study • Matching performance Black lines: Basis DLM Red lines: TeleAtlas Green Arrows: links Successful matching cases

18. 5. Case Study • Matching performance 55 m Black lines: Basis DLM Red lines: TeleAtlas Green Arrows: links Successful matching cases

19. 5. Case Study • Matching performance Black lines: Basis DLM Red lines: TeleAtlas Green Arrows: links Matching chaos caused by topological inconsistency

20. 5. Case Study According to the relationship to the roads, the routing relevant information can be classified into three groups: • Transferring of the routing relevant information (1) The routing attributes of the road lines, e.g. street width, direction restriction and speed limitation. (2) The routing information at the road intersections, e.g. turning restrictions and signpost information. (3) Points of interests (POIs), also called “Service”, are a series of point representations bound to the road lines, such as hotel, gas station, restaurant, showplace, beauty spot etc.

21. 6. Conclusion • This paper proposes a structure-oriented matching approach, which touches upon not only common street data but also the challenge cases of looping crosses, parallel roads, short stubbles, narrow passages, slip roads around cloverleaf junctions etc. • This matching approach reveals high matching rate and accuracy andcan be applied for transfer of the information bound to one of the road-networks to another. • We believe that a perfect matching approach is possible only if the context information can be holistically considered.

22. B B A A C C D D 7. Future work • A new matching algorithm based on Delimited Stroke (DS) is being researched by us. • Comparing with the „Buffer Growing“, the matching algorithm based on DS shows us more positive performance: • Ten times faster (2 minute pro 10,000 objects) • More accurate (around 99.5%)