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Efficient road geometry identification from digital vector data

Author

Listed:
  • Richard Andrášik

    (CDV Transport Research Centre)

  • Michal Bíl

    (CDV Transport Research Centre)

Abstract

A new method for the automatic identification of road geometry from digital vector data is presented. The method is capable of efficiently identifying circular curves with their radii and tangents (straight sections). The average error of identification ranged from 0.01 to 1.30 % for precisely drawn data and 4.81 % in the case of actual road data with noise in the location of vertices. The results demonstrate that the proposed method is faster and more precise than commonly used techniques. This approach can be used by road administrators to complete their databases with information concerning the geometry of roads. It can also be utilized by transport engineers or traffic safety analysts to investigate the possible dependence of traffic accidents on road geometries. The method presented is applicable as well to railroads and rivers or other line features.

Suggested Citation

  • Richard Andrášik & Michal Bíl, 2016. "Efficient road geometry identification from digital vector data," Journal of Geographical Systems, Springer, vol. 18(3), pages 249-264, July.
    • Handle: RePEc:kap:jgeosy:v:18:y:2016:i:3:d:10.1007_s10109-016-0230-1
    DOI: 10.1007/s10109-016-0230-1
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    References listed on IDEAS

    as
    1. Chung, Koohong & Jang, Kitae & Madanat, Samer & Washington, Simon, 2011. "Proactive detection of high collision concentration locations on highways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(9), pages 927-934, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Circular curves; Tangents; Automatic geometry identification; Curvature; Discriminant analysis; Classification tree; Roads; Database; GIS;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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