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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Agouris P, Stefanidis A, Gyftakis S (2001) Differential snakes for change detection in road segments. Photogramm Eng Remote Sens 67(12):1391–1399
Andrášik R, Bíl M, Janoška Z, Valentová V (2013) Identification of curves and straight sections on road network from digital vector data. Trans Transp Sci 6(2):73–80
Bíl M, Andrášik R, Janoška Z (2013) Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation. Accid Anal Prev 55:265–273
Bogenreif C, Souleyrette RR, Hans Z (2012) Identifying and measuring horizontal curves and related effects on highway safety. J Transp Saf Secur 4(3):179–192
Boucheham B, Ferdi Y, Batouche MCh (2006) Recursive versus sequential multiple error measures reduction: a curve simplification approach to ECG data compression. Comput Methods Programs Biomed 81:162–173
Busch A, Willrich F (2002) Quality management of ATKIS data. In: OEEPE/ISPRS joint workshop on spatial data quality management, 21–22 March 2002, Istanbul
Camacho-Torregrosa FJ, Pérez-Zuriaga AM, Campoy-Ungría JM, García-García A (2013) New geometric design consistency model based on operating speed profiles for road safety evaluation. Accid Anal Prev 61:33–42
Castro M, Iglesias L, Rodríguez-Solano R, Sánchez JA (2006) Geometric modelling of highways using global positioning system (GPS) data and spline approximation. Transp Res Part C 14:233–243
Chung K, Jang K, Madanat S, Washington S (2011) Proactive detection of high collision concentration locations on highways. Transp Res Part A 45:927–934
Clayton VH (1985) Cartographic generalization: a review of feature simplification and systematic point elimination algorithms. In: NOAA Technical Report NOS 112, Rockville, Maryland
Corder GW, Foreman DI (2014) Nonparametric statistics: a step-by-step approach, 2nd edn. Wiley, ISBN 1118840313
Douglas DH, Peucker TK (1973) Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Can Cartogr 10(2):112–122
Eidenbenz Ch, Käser Ch, Baltsavias E (2000) ATOMI—automated reconstruction of topographic objects from aerial images using vectorized map information. Int Arch Photogramm Remote Sens 33(B3):462–471
Elvik R (2008) A survey of operational definitions of hazardous road locations in some European countries. Accid Anal Prev 40:1830–1835
ESRI (2013) ArcGIS Desktop: Release 10.2 Redlands. Environmental Systems Research Institute, CA
Findley DJ, Hummer JE, Rasdorf W, Zegeer ChV, Fowler TJ (2012a) Modeling the impact of spatial relationships on horizontal curve safety. Accid Anal Prev 45:296–304
Findley DJ, Zegeer ChV, Sundstrom CA, Hummer JE, Rasdorf W, Fowler TJ (2012b) Finding and measuring horizontal curves in a large highway network: a GIS approach. Public Works Manag Policy 17(2):189–211
Fuks DB, Tabachnikov S (2007) Mathematical omnibus: thirty lectures on classic mathematics. American Mathematical Society, USA
Gander W, Golub GH, Strebel R (1994) Least-squares fitting of circles and ellipses. BIT Numer Math 34(4):558–578
Hauer E (1999) Safety and the choice of degree of curve. Transp Res Rec J Transp Res Board 1665(1):22–27
Hummer JE, Rasdorf W, Findley DJ, Zegeer ChV, Sundstrom CA (2010) Curve collisions: road and collision characteristics and countermeasures. J Transp Saf Secur 2:203–220
Imran M, Hassan Y, Patterson D (2006) GPS–GIS-based procedure for tracking vehicle path on horizontal alignments. Comput Aided Civil Infrastruct Eng 21:383–394
Kanevski M, Pozdnoukhov A, Timonin V (2009) Machine learning for spatial environmental data: theory, applications and software. EPFL Press, Lausanne
Mena JB (2003) State of the art on automatic road extraction for GIS update: a novel classification. Pattern Recogn Lett 24:3037–3058
Pal NR, Bezdek JC, Hathaway RJ (1996) Sequential competitive learning and the fuzzy c-means clustering algorithms. Neural Netw 9(5):787–796
Park W, Yu K (2011) Hybrid line simplification for cartographic generalization. Pattern Recogn Lett 32:1267–1273
Pérez-Zuriaga AMD, Camacho-Torregrosa FJ, García A (2013) Tangent-to-curve transition on two-lane rural roads based on continuous speed profiles. J Transp Eng 139(11):1048–1057
Rasdorf W, Findley DJ, Zegeer ChV, Sundstrom CA, Hummer JE (2012) Evaluation of GIS applications for horizontal curve data collection. J Comput Civil Eng 26(2):191–203
R Development Core Team (2008) R: a language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org
Scilab Enterprises (2012) Scilab: free and open source software for numerical computation (Version 5.5.1) [Software]. http://www.scilab.org
Stewart J (2011) Calculus. Brooks/Cole Publishing Company, Cengage Learning
Straub B-M, Wiedemann Ch, Heipke Ch (2000) Towards the automatic interpretation of images for GIS update. Int Arch Photogramm Remote Sens 33(B2):525–532
Tan P-N, Steinbach M, Kumar V (2005) Introduction to data mining. Addison-Wesley, USA
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.
About this article
Cite this article
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
- Circular curves
- Automatic geometry identification
- Discriminant analysis
- Classification tree