Citation

Košanin I, Gnjatović M, Maček N, Joksimović D. Axioms (Basel) 2023; 12(6): e509.

Copyright

(Copyright © 2023, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/axioms12060509

PMID

unavailable

Abstract

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.

Language: en

Keywords

clustering; critical road segments; knee detection; open data; traffic accident