Comparison of Different Road Segmentation Methods

  • Maen Qaseem Ghadi Budapest University of Technology and Economics
  • Árpád Török Budapest University of Technology and Economics
Keywords: K-means clustering, road segmentation, safety performance function, road accidents, homogeneous segment


In road safety, the process of organizing road infrastructure
network data into homogenous entities is called segmentation.
Segmenting a road network is considered the
first and most important step in developing a safety performance
function (SPF). This article aims to study the benefit
of a newly developed network segmentation method which is based on the generation of accident groups applying K-means clustering approach. K-means algorithm has been used to identify the structure of homogeneous accident groups. According to the main assumption of the proposed clustering method, the risk of accidents is strongly influenced by the spatial interdependence and traffic attributes of the accidents. The performance of K-means clustering was compared with four other segmentation methods applying constant average annual daily traffic segments, constant length segments, related curvature characteristics and a multivariable method suggested by the Highway Safety Manual (HSM). The SPF was used to evaluate the performance of the five segmentation methods in predicting accident frequency. K-means clustering-based segmentation method has been proved to be more flexible and accurate than the other models in identifying homogeneous infrastructure segments with similar safety characteristics.

Author Biographiesaaa replica rolex repwatches replica rolex watches for men replica iwc watch

Maen Qaseem Ghadi, Budapest University of Technology and Economics
Engineering-Department of Transport Technology and EconomicsPhD student
Árpád Török, Budapest University of Technology and Economics

Engineering-Department of Transport Technology and Economics



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How to Cite
Ghadi MQ, Török Árpád. Comparison of Different Road Segmentation Methods. Promet [Internet]. 2019Apr.18 [cited 2024Jul.21];31(2):163-72. Available from: