Evaluation of Automatic Prediction of Small Horizontal Curve Attributes of Mountain Roads in GIS Environments

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dc.authorid0000-0003-2605-734X
dc.authorid0000-0001-6558-9029
dc.authorid0000-0003-3349-517X
dc.contributor.authorGulci, Sercan
dc.contributor.authorAcar, Hafiz Hulusi
dc.contributor.authorAkay, Abdullah E.
dc.contributor.authorGulci, Nese
dc.date.accessioned2026-02-12T21:05:07Z
dc.date.available2026-02-12T21:05:07Z
dc.date.issued2022
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractRoad curve attributes can be determined by using Geographic Information System (GIS) to be used in road vehicle traffic safety and planning studies. This study involves analyzing the GIS-based estimation accuracy in the length, radius and the number of small horizontal road curves on a two-lane rural road and a forest road. The prediction success of horizontal curve attributes was investigated using digitized raw and generalized/simplified road segments. Two different roads were examined, involving 20 test groups and two control groups, using 22 datasets obtained from digitized and surveyed roads based on satellite imagery, GIS estimates, and field measurements. Confusion matrix tables were also used to evaluate the prediction accuracy of horizontal curve geometry. F-score, Mathews Correlation Coefficient, Bookmaker Informedness and Balanced Accuracy were used to investigate the performance of test groups. The Kruskal-Wallis test was used to analyze the statistical relationships between the data. Compared to the Bezier generalization algorithm, the Douglas-Peucker algorithm showed the most accurate horizontal curve predictions at generalization tolerances of 0.8 m and 1 m. The results show that the generalization tolerance level contributes to the prediction accuracy of the number, curve radius, and length of the horizontal curves, which vary with the tolerance value. Thus, this study underlined the importance of calculating generalizations and tolerances following a manual road digitization.
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK) [120O954]
dc.description.sponsorshipThis study is a part of a project funded by The Scientific and Technological Research Council of Turkiye (TUBITAK) with the project number 120O954. The authors would like to thank CDV-Transport Research Centre for providing the non-commercial use of ROCA extension. The authors also wish to thank anonymous reviewers and editors for their insightful suggestions and comments, which led to an improved manuscript.
dc.identifier.doi10.3390/ijgi11110560
dc.identifier.issn2220-9964
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85141665458
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/ijgi11110560
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6803
dc.identifier.volume11
dc.identifier.wosWOS:000883490200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofIsprs International Journal of Geo-Information
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260212
dc.subjectspatial data
dc.subjectdata quality
dc.subjectfield measurement
dc.subjectcurve geometry
dc.subjecttransportation
dc.subjectline generalization
dc.subjectlow-cost
dc.titleEvaluation of Automatic Prediction of Small Horizontal Curve Attributes of Mountain Roads in GIS Environments
dc.typeArticle

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