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Efficient road geometry identification from digital vector data

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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.

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Acknowledgments

This paper was prepared with the help of a project undertaken by Transport R&D Centre (OP R&D for Innovation No. CZ.1.05/2.1.00/03.0064) and project ‘RESILIENCE 2015: Dynamic Resilience Evaluation of Interrelated Critical Infrastructure Subsystems’ (No. VI20152019049), supported by the Ministry of the Interior of the Czech Republic. We would further like to thank our colleagues Martina Bílová and Jiří Sedoník for their help with data and figures preparation, Zuzana Křivánková for her comments and suggestions and Pavel Havránek for expert identification of road geometry. We also greatly appreciate the suggestions and work carried out by the two anonymous reviewers and Antonio Paez.

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  1. Department of Geoinformatics, CDV Transport Research Centre, Líšeňská 33a, 636 00, Brno, Czech Republic

    Richard Andrášik & Michal Bíl

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  1. Richard Andrášik
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  2. Michal Bíl
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Correspondence to Michal Bíl.

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Andrášik, R., Bíl, M. Efficient road geometry identification from digital vector data. J Geogr Syst 18, 249–264 (2016). https://doi.org/10.1007/s10109-016-0230-1

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  • DOI: https://doi.org/10.1007/s10109-016-0230-1

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