# Automatic and Accurate Conflation of Different Road-Network Vector Data towards Multi-Modal Navigation

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Strategy

#### 3.1. Road-Network Matching between Participating Datasets

#### 3.2. Identification of the PWs-Tbc in ATKIS

#### 3.3. Transformation of PWs-tbc to Eliminate Geometric Inconsistency

_{2}′ → p

_{1}′ from ATKIS should be connected to the street P

_{1}→ P

_{2}in NAVTEQ, but in fact they are detached here; and instead of intersecting to each other, the road p

_{3}′ → p

_{4}′ lies apart from road P

_{3}→ P

_{4}. Such a case requires an adaptive transformation to harmonize the shape and location of the PWs-tbc to the road network of NAVTEQ. This transformation process can be characterized by two steps:

#### **Step 1:** **Establishment of the control point pairs**

#### **Step 2:** **Alignment based on control point pairs**

**(a) Turning points which are duplicated to the fromPoints of CPPs**

**(b) Road crossings or dead ends which are not duplicated to the fromPoint of any CPP**

_{2}′ and p

_{4}′ in Figure 3, the local transformation is applied, which employs space partition of the whole conflation area into much smaller regions and therefore can better handle the local distortions in each region.

_{1}p

_{2}…p

_{10}p

_{1}in Figure 5, its new position ${P}_{0}\text{'}={\left({X}_{0}\text{'},{Y}_{0}\text{'}\right)}^{\mathrm{T}}$in the conflated dataset can be calculated by Equation (1).

- m—number of the neighbors of p
_{i}; - n—number of the CPPs;
- α, β—two experimental coefficients larger than 0.

_{0}’ in Figure 3). For such cases, the proposed local transformation model will build up a well-defined buffer around the given point; then all the CPPs that fall inside this buffer will be taken into account for the transformation; however, if there is no CPP falling inside, the point will keep its initial position after the data conflation.

**(c) Other Turning points of the PWs-tbc.**

_{1}p

_{2}…p

_{n-1}p

_{n}is restricted by p

_{1}and p

_{n}, where ${P}_{1}={\left({x}_{1},{y}_{1}\right)}^{\mathrm{T}}$ is a turning point in Group (a) with the transformation $\u25b3{T}_{p1}={\left(\u25b3{x}_{1},\text{}\u25b3{y}_{1}\right)}^{\mathrm{T}}$ and ${P}_{n}={\left({x}_{\mathrm{n}},{y}_{\mathrm{n}}\right)}^{\mathrm{T}}$ is a road crossing in Group (b) with the transformation $\u25b3{T}_{pn}={\left(\u25b3{x}_{n},\text{}\u25b3{y}_{n}\right)}^{\mathrm{T}}$. Then, the transformation of the turning point ${p}_{i}\text{}\left(2\le i\le n-1\right)$ can be calculated by Equation (2), where $\u25b3{T}_{pi}$ represents the transformation of the turning point ${p}_{i}$ and γ is an experimental coefficient between (0,1).

#### 3.4. Remodelling of the Conflated Dataset

#### 3.4.1. Creating New Intersections (Nodes)

_{1}′ → p

_{2}′ in Figure 6. The condition, however, becomes more complicated when the PW-tbc touches one road object from road network of NAVTEQ and the touching point is neither from-node nor to-node of this object. In such cases, the conflated road network requires new intersections (nodes) to rearrange the topologies of the conflated road network. For example in Figure 6, the conflation of the PW-tbc p

_{1}′ → p

_{3}′ necessitates a new intersection p

_{3}′ to split the object P

_{5}→ P

_{6}into two parts, which reserves the connectivity between the PWs-tbc and the road network from NAVTEQ.

#### 3.4.2. Decomposition and Transferring of Semantic Information

_{5}→ P

_{6}from the initial road network of NAVTEQ has been split into two objects P

_{5}→ p

_{3}′ and p

_{3}′ → P

_{6}in the conflated dataset. In this case, the initial attribute of the object should be first decomposed and then transferred to the split parts. The non-spatial attributes of the original object, such as street name, Functional Road Class, Form of Way, etc. can be directly assigned to the newly generated objects, whereas the spatial attributes, such as the street length and travel time, should be fairly assigned to the new ones by means of interpolation.

#### 3.4.3. Entity ID Issues

_{1}′ → p

_{2}′ and p

_{1}′ → p

_{3}′ in Figure 6, we should assign new object IDs for them. Meanwhile, the node ID of P

_{4}in NAVTEQ is transferred to the to-node of the object p

_{1}′ → p

_{2}′ (viz. p

_{2}′); while new node IDs are required by the nodes which are either initial from the ATKIS (e.g., p

_{1}′ in Figure 6) or newly created intersections (e.g., p

_{3}′ in Figure 6).

_{4}→ P

_{5}in Figure 6, we will keep all the IDs for road object, from-node and to-node. However, if a road object from the reference dataset of NAVTEQ is divided into different parts after the data conflation (e.g., P

_{5}→ P

_{6}in Figure 6), then each part (see P

_{5}→ p

_{3}’ and p

_{3}’ → P

_{6}in Figure 6) has to be assigned a new object ID since it acts as an individual road object in the conflated dataset.

#### 3.5. Error Detection and Correction

#### **Category 1**: Duplicated conflated pedestrian ways.

#### **Category 2:** Partial duplications.

#### **Category 3:** Conflated pedestrian ways that are possibly wrong.

#### **Category 4:** Reliable conflated pedestrian ways.

## 4. Discussion of Conflation Results

^{2}, it has taken only 39 s in a normal personal computer (Intel Core i7 2.80 GHz).

## 5. Conclusions

^{2}and more than 15,388,000 ATKIS objects and 6,690,000 NAVTEQ objects. As a result, the NAVTEQ road-network has been enriched by the appended pedestrian ways from ATKIS and thus gained the necessary capabilities for the development of multi-modal navigation services which have already become basic functionalities in some open platforms (e.g., Google Maps). The same method is now being tested with the data from many other European counties, such as Austria, Switzerland, France, Belgium, The Netherlands, Luxembourg, Denmark, Poland, Czech Republic, etc.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Saalfeld, A. Conflation: Automated map compilation. Int. J. Geogr. Inf. Syst.
**1988**, 2, 217–228. [Google Scholar] [CrossRef] - Chen, C.-C. Automatically and Accurately Conflating Road Vector Data, Street Maps and Orthoimagery. Ph.D. Thesis, University of Southern California, Los Angeles, CA, USA, 2005. [Google Scholar]
- Lozano, A.; Storchi, G. Shortest viable hyperpath in multimodal networks. Transp. Res. B Methodol.
**2002**, 36, 853–874. [Google Scholar] [CrossRef] - Liu, L.; Meng, L. Algorithms of multi-modal route planning based on the concept of switch point. Photogramm. Fernerkund. Geoinform.
**2009**, 5, 431–444. [Google Scholar] [CrossRef] - Ruiz, J.J.; Ariza, F.J.; Ureña, M.A.; Blázquez, E.B. Digital map conflation: a review of the process and a proposal for classification. Int. J. Geogr. Inf. Sci.
**2011**, 25, 1439–1466. [Google Scholar] [CrossRef] - Gabay, Y.; Doytsher, Y. Automatic adjustment of line maps. In Proceedings of the GIS/LIS’94 Annual Convention, Phoenix, Arizona, USA, 25–27 October 1994; pp. 333–341.
- Gabay, Y.; Doytsher, Y. Automatic feature correction in merging of line maps. In Proceedings of the 1995 ACSM-ASPRS Annual Convention 2, Charlotte, North Carolina, 27 February–2 March 1995; pp. 404–410.
- Walter, V.; Fritsch, D. Matching spatial data sets: A statistical approach. Int. J. Geogr. Inf. Sci.
**1999**, 13, 445–473. [Google Scholar] [CrossRef] - Kang, H. Spatial Data Integration: A Case Study of Map Conflation with Census Bureau and Local Government Data; The Ohio State University: Columbus, OH, USA, 2001. [Google Scholar]
- Zhang, M.; Meng, L. An iterative road-matching approach for the integration of postal data. Comput. Environ. Urban Syst.
**2007**, 31, 598–616. [Google Scholar] [CrossRef] - Zhang, Q.; Griffiths, S.; Wollersheim, M.; Tighe, M.L.; Xu, C. Conflation of national bridge inventory database with tiger based road vectors. In Proceedings of the XXII ISPRS Congress: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, 25 August–1 September 2012.
- He, D. A Study on Theory and method of spatial vector data conflation. Res. J. Appl. Sci. Eng. Technol.
**2013**, 5, 563–567. [Google Scholar] - Zhang, M.; Yao, W.; Meng, L. Enrichment of topographic road database for the purpose of routing and navigation. Int. J. Digit. Earth
**2014**, 7, 411–431. [Google Scholar] [CrossRef] - Casado, M.L. Some basic mathematical constraints for the geometric conflation problem. In Proceedings of the 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Lisbon, Portugal, 5–7 July 2006.
- Parent, C.; Spaccapietra, S. Database Integration: The Key to Data Interoperability, Advances in Object-Oriented Data Modeling; Papazoglou, M.P., Spaccapietra, S., Tari, Z., Eds.; The MIT Press: Cambridge, MA, USA, 2000. [Google Scholar]
- Li, L.; Goodchild, M.F. An optimisation model for linear feature matching in geographical data conflation. Int. J. Image Data Fusion
**2011**, 2, 309–328. [Google Scholar] [CrossRef] - Volz, S. An iterative approach for matching multiple representations of street data. In Proceedings of the ISPRS Workshop on Multiple Representation and Interoperability of Spatial Data, Hannover, Germany, 22–24 February 2006.
- Yang, B.; Zhang, Y.; Luan, X. A probabilistic relaxation approach for matching road networks. Int. J. Geogr. Inf. Sci.
**2013**, 27, 319–338. [Google Scholar] [CrossRef] - Zhang, M.; Meng, L. Delimited stroke oriented algorithm—Working principle and implementation for the matching of road networks. J. Geogr. Inf. Sci.
**2008**, 14, 44–53. [Google Scholar] [CrossRef] - Zhang, M.; Meng, L.; Bobrich, J. A road-network matching approach guided by “structure”. Ann. Geogr. Inf. Syst.
**2010**, 16, 165–176. [Google Scholar] [CrossRef] - Chen, J.; Hu, Y.; Li, Z.; Zhao, R.; Meng, L. Selective omission of road features based on mesh density for automatic map generalization. Int. J. Geogr. Inf. Sci.
**2009**, 23, 1013–1032. [Google Scholar] [CrossRef]

**Figure 2.**Identified matching pairs with different matching relationships: (

**a**) m:n matching; (

**b**) Equivalent matching (parallel lines to single line); (

**c**) Equivalent matching (polygon to point). Black lines: road network 1; red lines: road network 2; green arrows: linkages.

**Figure 3.**Matching between NAVTEQ and ATKIS. Orange lines: NAVTEQ; grey lines: ATKIS; dashed lines: pedestrian ways to be conflated (PWs-tbc); green arrows: linkages.

**Figure 4.**Space partition based on meshes: (

**a**) initial meshes based on NAVTEQ; and (

**b**) distorted meshes that fit the geometries of ATKIS. Orange solid lines: initial NAVTEQ; grey dash lines: distorted NAVTEQ; green arrows: linkages.

**Figure 5.**Local distortion map based on mesh partition. Orange solid lines: initial NAVTEQ; grey dash lines: distorted NAVTEQ; green arrows: linkages.

**Figure 6.**Transformation of PWs-tbc from one road network to the other: (

**a**) PWs-tbc before transformation; (

**b**) PWs-tbc after transformation. Orange lines: NAVTEQ; grey lines: ATKIS; dashed lines: conflated pedestrian ways; green arrows: linkages (control point pairs).

**Figure 7.**The process to solve the problems of partial duplications: (

**a**) data matching; (

**b**) data conflation; (

**c**) error correction. Orange lines: NAVTEQ; grey lines: ATKIS; dashed lines: conflated pedestrian ways; dashed lines: conflated pedestrian ways; green arrows: linkages.

**Figure 8.**An example of the conflated road network in a built-up area: (

**a**) a randomly selected area of 10 × 10 km

^{2}in Munich, Germany; (

**b**) partial enlarged view of (a); (

**c**) partial enlarged view of (a). Orange: the initial road network of NAVTEQ; grey: the conflated roads from ATKIS.

**Figure 9.**An example of the conflated road network in a rural area: (

**a**) a randomly selected area of 7 × 7 km

^{2}in Garmisch, Germany; (

**b**) partial enlarged view of (a); (

**c**) partial enlarged view of (a). Orange: the initial road network of NAVTEQ; grey: the conflated roads from ATKIS.

**Figure 10.**An example of the conflated road network in a suburb area: (

**a**) a randomly selected area of 15 × 10 km

^{2}in Hamburg, Germany; (

**b**) partial enlarged view of (a); (

**c**) partial enlarged view of (a). Orange: the initial road network of NAVTEQ; grey: the conflated roads from ATKIS.

**Figure 11.**Examples of “data ambiguity”: (

**a**) an area where the geometric/topologic conditions are very complex; (

**b**) an area where the geometric/topologic conditions are distinct to each other. Red lines: NAVTEQ; grey lines: ATKIS.

Test Area (km^{2}) | Area 1 (100 km^{2}) | Area 2 (49 km^{2}) | Area 3 (150 km^{2}) | Total |
---|---|---|---|---|

NAVTEQ Features (NF) | 14,960 | 2214 | 3111 | 20,285 |

ATKIS Features (AF) | 19,516 | 5196 | 6400 | 31,112 |

Conflated Features (CF) | 5177 | 2246 | 2599 | 10,022 |

Unfavorable Conflated Features (UCF) | 42 | 11 | 12 | 65 |

Computing Time (second) (incl. data reading and writing) | 25 s | 6 s | 8 s | 39 s |

Configuration of the Computer | Intel Core i7 2.80 GHz | |||

Correctness | ||||

Overall Correctness = (AF − UCF)/AF × 100% | 99.78% | 99.79% | 99.81% | 99.79% |

Conflation Correctness = (CF − UCF)/CF × 100% | 99.19% | 99.51% | 99.54% | 99.35% |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhang, M.; Yao, W.; Meng, L.
Automatic and Accurate Conflation of Different Road-Network Vector Data towards Multi-Modal Navigation. *ISPRS Int. J. Geo-Inf.* **2016**, *5*, 68.
https://doi.org/10.3390/ijgi5050068

**AMA Style**

Zhang M, Yao W, Meng L.
Automatic and Accurate Conflation of Different Road-Network Vector Data towards Multi-Modal Navigation. *ISPRS International Journal of Geo-Information*. 2016; 5(5):68.
https://doi.org/10.3390/ijgi5050068

**Chicago/Turabian Style**

Zhang, Meng, Wei Yao, and Liqiu Meng.
2016. "Automatic and Accurate Conflation of Different Road-Network Vector Data towards Multi-Modal Navigation" *ISPRS International Journal of Geo-Information* 5, no. 5: 68.
https://doi.org/10.3390/ijgi5050068