A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas

This paper introduces a parameter-free clustering-based approach to detecting critical traffic road segments in urban areas, i.e., road segments of spatially prolonged and high traffic accident risk. In addition, it proposes a novel domain-specific criterion for evaluating the clustering results, wh...

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Bibliographic Details
Published inAxioms Vol. 12; no. 6; p. 509
Main Authors Košanin, Ivan, Gnjatović, Milan, Maček, Nemanja, Joksimović, Dušan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2023
Subjects
Algorithms
Clustering
Clustering (Computers)
critical road segments
knee detection
Mathematical models
Methods
open data
Prevention
Roads & highways
Segments
Stability analysis
traffic accident
Traffic accidents
Traffic accidents & safety
Urban areas
Serbia
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Summary:This paper introduces a parameter-free clustering-based approach to detecting critical traffic road segments in urban areas, i.e., road segments of spatially prolonged and high traffic accident risk. In addition, it proposes a novel domain-specific criterion for evaluating the clustering results, which promotes the stability of the clustering results through time and inter-period accident spatial collocation, and penalizes the size of the selected clusters. To illustrate the proposed approach, it is applied to data on traffic accidents with injuries or death that occurred in three of the largest cities of Serbia over the three-year period.
ISSN:2075-1680
2075-1680
DOI:10.3390/axioms12060509