A Novel Approach of Selecting Arterial Road Network for Route Planning Purpose

Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

The most of existing algorithms for road network selection are proposed for the visualization purpose. Hence, the connectivity of road network for route planning has rarely been considered in the previous works. In this chapter, we propose a novel method of road selection, whereby decisive paths that distinguish the suboptimal route from the optimal one can be identified and added to the high-layer network which is formed mainly by the connectivity of the crucial cities. This benefits the improvement of vertical partitioning and finally the construction of a high-layer road network that allows the optimal route planning. A case study in Bavaria State, Germany, reveals the feasibility of the proposed approach.

Keywords

Generalization Road network Selection Route-planning 

References

  1. Anders KH (2006) Grid typification. In: Riedl A, Kainz W, Elmes GA (eds) Progress in spatial data handling. 12th international symposium on spatial data handling (SDH), Vienna, 10–14 JulyGoogle Scholar
  2. Chaudhry O, Mackaness WA (2005) Rural and urban road network generalization deriving 1:250,000 from 1:1250. In: International cartographic conference. Coruna, pp 9–16Google Scholar
  3. Chen J, Hu YG, Li ZL, Zhao RL, Meng LQ (2009) Selective omission of road features based on mesh density for automatic map generalization. Int J Geogr Inf Sci 23(8):1013–1032CrossRefGoogle Scholar
  4. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1(1):269–271CrossRefGoogle Scholar
  5. Edwardes A, Mackaness W (2000) Modelling knowledge for automated generalization of categorical maps—a constraint based approach. In: Atkinson P, Martin D (eds) GIS and GeoComputation (innovations in GIS 7). Taylor & Francis, LondonGoogle Scholar
  6. Gulgen F, Gokgoz T (2008) Selection of roads for cartographic generalization. In: The international archives of the photogrammetry, remote sensing and spatial information sciences, vol XXXVII, Part B4Google Scholar
  7. Heinzle F, Anders KH (2007) Characterising space via pattern recognition techniques: identifying patterns in road networks. In: Ruas A, Sarjakoski T, Mackaness WA (eds) Generalisation of geographic information: cartographic modelling and applications. Elsevier Ltd, OxfordGoogle Scholar
  8. Heinzle F, Sester M, Anders KH (2005) Graph-based approach for recognition of patterns and implicit information in road networks. In: Proceedings of 22nd international cartographic conference, La Coruña, pp 9–16Google Scholar
  9. Heinzle F, Anders KH, Sester M (2006) Pattern recognition in road networks on the example of circular road detection. In: Proceedings of the 4th international conference GIScience, MünsterGoogle Scholar
  10. Hu YG, Chen J, Li ZL, Zhao RL (2007) Selection of streets based on mesh density for digital map generalization. In: Proceedings of the 4th international conference on image and graphicsGoogle Scholar
  11. Kulik L, Duckham M, Egenhofer M (2005) Ontology-driven map generalization. J Vis Lang Comput 16(3):245–267CrossRefGoogle Scholar
  12. Li ZL, Choi YH (2002) Topographic map generalization: association of road elimination with thematic attributes. Cartogr J 39(2):153–166CrossRefGoogle Scholar
  13. Liu XJ, Zhan BJ, Ai TH (2009) Road selection based on Voronoi diagrams and ‘Strokes’ in map generalization. Int J Appl Earth Obs GeoinfGoogle Scholar
  14. Mackaness WA (1995) A constraint based approach to human computer interaction in automated cartography. In: Proceeding ICA/ACI, vol 2, Barcelona, pp 1423–1433Google Scholar
  15. Mackaness W (2007) Understanding geographic space. In: Mackaness W, Raus A, Sarjakoski T (eds) The generalization of geographic information: models and applications. Elsevier, AmsterdamGoogle Scholar
  16. Mackaness WA, Beard MK (1993) Use of graph theory to support map generalisation. Cartogr Geogr Inf Syst 20:210–221CrossRefGoogle Scholar
  17. Peng W, Muller JC (1996) A dynamic decision tree structure supporting urban road network automated generalisation. Cartogr J 33(1):5–10CrossRefGoogle Scholar
  18. Sester M (1995) Lernen struktureller Modelle für die Bildanalyse. Deutsche Geodätische Kommission, Reihe C, Nr. 441, München 1995, p 118Google Scholar
  19. Sinha G, Flewelling D (2002) A framework for multicriteria line generalization to support scientific and engineering modeling. In: Egenhofer MJ, Mark DM (eds) GIScience 2002 abstracts, pp 173–175Google Scholar
  20. Thom S (2005) A strategy for collapsing OS integrated transport network dual carriageways. In: Proceedings of the 8th ICA workshop on generalization and multiple representation, La CoruñaGoogle Scholar
  21. Thom S (2006) Conflict identification and representation for roads based on a skeleton. In: Proceeding of the 12th international symposium on spatial data handling 12, Vienna, pp 659–680Google Scholar
  22. Thomson RC, Brooks R (2000) Efficient generalisation and abstraction of network data using perceptual grouping. In: Proceedings of the 5th international conference on GeoComputation, ChathamGoogle Scholar
  23. Thomson R, Richardson D (1999) The “good continuation” principle of perceptual organization applied to the generalisation of road networks. In: Proceedings of the 19th ICC, ICA, OttawaGoogle Scholar
  24. Tian J (2008) Progressive representation and generalization of street network vector data. ISPRS Congress, BeijingGoogle Scholar
  25. Touya G (2007) A road network selection process based on data enrichment and structure detection. in: Proceedings of the 10th ICA workshop on generalization and multiple representation, MoscowGoogle Scholar
  26. Wertheimer M (1938). Laws of organization in perceptual forms. Untersuchungen zur Lehre von Der Gestalt II, in Psycologische Forschung 4:301–350Google Scholar
  27. Zhang Q (2004) Modelling structure and patterns in road network generalization. In: proceedings of ICA workshop on generalisation and multiple representation, Leicester, 20–21 Aug 2004Google Scholar
  28. Zhang X, Ai TH, Jantien S (2008) The evaluation of spatial distribution density in map generalization. ISPRS Congress, BeijingGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of GIScienceUniversity of HeidelbergHeidelbergGermany
  2. 2.Kotei Navigation Co. LdtWuhanChina
  3. 3.College of Survying and GeoInformaticsTongji UniversityShanghaiChina

Personalised recommendations