A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas
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2023
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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.
Ključne reči:
traffic accident / clustering / critical road segments / knee detection / open dataIzvor:
Axioms, 2023, 12, 6, 509-Izdavač:
- Basel, Switzerland : MDPI
Institucija/grupa
JakovTY - JOUR AU - Košanin, Ivan AU - Gnjatović, Milan AU - Maček, Nemanja AU - Joksimović, Dušan PY - 2023 UR - http://jakov.kpu.edu.rs/handle/123456789/1513 AB - 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. PB - Basel, Switzerland : MDPI T2 - Axioms T1 - A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas VL - 12 IS - 6 SP - 509 DO - 10.3390/axioms12060509 ER -
@article{ author = "Košanin, Ivan and Gnjatović, Milan and Maček, Nemanja and Joksimović, Dušan", year = "2023", 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.", publisher = "Basel, Switzerland : MDPI", journal = "Axioms", title = "A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas", volume = "12", number = "6", pages = "509", doi = "10.3390/axioms12060509" }
Košanin, I., Gnjatović, M., Maček, N.,& Joksimović, D.. (2023). A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas. in Axioms Basel, Switzerland : MDPI., 12(6), 509. https://doi.org/10.3390/axioms12060509
Košanin I, Gnjatović M, Maček N, Joksimović D. A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas. in Axioms. 2023;12(6):509. doi:10.3390/axioms12060509 .
Košanin, Ivan, Gnjatović, Milan, Maček, Nemanja, Joksimović, Dušan, "A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas" in Axioms, 12, no. 6 (2023):509, https://doi.org/10.3390/axioms12060509 . .