TY  - 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 . .

TY  - JOUR
AU  - Gnjatović, Milan
AU  - Košanin, Ivan
AU  - Maček, Nemanja
AU  - Joksimović, Dušan
PY  - 2022
UR  - http://jakov.kpu.edu.rs/handle/123456789/1515
AB  - This paper introduces and illustrates an approach to automatically detecting and selecting
“critical” road segments, intended for application in circumstances of limited human or technical
resources for traffic monitoring and management. The reported study makes novel contributions
at three levels. At the specification level, it conceptualizes “critical segments” as road segments
of spatially prolonged and high traffic accident risk. At the methodological level, it proposes a
two-stage approach to traffic accident clustering and selection. The first stage is devoted to spatial
clustering of traffic accidents. The second stage is devoted to selection of clusters that are dominant in
terms of number of accidents. At the implementation level, the paper reports on a prototype system
and illustrates its functionality using publicly available real-life data. The presented approach is
psychologically inspired to the extent that it introduces a clustering criterion based on the Gestalt
principle of proximity. Thus, the proposed algorithm is not density-based, as are most other state-of-
the-art clustering algorithms applied in the context of traffic accident analysis, but still keeps their
main advantages: it allows for clusters of arbitrary shapes, does not require an a priori given number
of clusters, and excludes “noisy” observations.
PB  - Basel, Switzerland : MDPI
T2  - Applied Sciences
T1  - Clustering of Road Traffic Accidents as a Gestalt Problem
VL  - 12
IS  - 9
SP  - 4543
DO  - 10.3390/app12094543
ER  - 

@article{
author = "Gnjatović, Milan and Košanin, Ivan and Maček, Nemanja and Joksimović, Dušan",
year = "2022",
abstract = "This paper introduces and illustrates an approach to automatically detecting and selecting
“critical” road segments, intended for application in circumstances of limited human or technical
resources for traffic monitoring and management. The reported study makes novel contributions
at three levels. At the specification level, it conceptualizes “critical segments” as road segments
of spatially prolonged and high traffic accident risk. At the methodological level, it proposes a
two-stage approach to traffic accident clustering and selection. The first stage is devoted to spatial
clustering of traffic accidents. The second stage is devoted to selection of clusters that are dominant in
terms of number of accidents. At the implementation level, the paper reports on a prototype system
and illustrates its functionality using publicly available real-life data. The presented approach is
psychologically inspired to the extent that it introduces a clustering criterion based on the Gestalt
principle of proximity. Thus, the proposed algorithm is not density-based, as are most other state-of-
the-art clustering algorithms applied in the context of traffic accident analysis, but still keeps their
main advantages: it allows for clusters of arbitrary shapes, does not require an a priori given number
of clusters, and excludes “noisy” observations.",
publisher = "Basel, Switzerland : MDPI",
journal = "Applied Sciences",
title = "Clustering of Road Traffic Accidents as a Gestalt Problem",
volume = "12",
number = "9",
pages = "4543",
doi = "10.3390/app12094543"
}

Gnjatović, M., Košanin, I., Maček, N.,& Joksimović, D.. (2022). Clustering of Road Traffic Accidents as a Gestalt Problem. in Applied Sciences
Basel, Switzerland : MDPI., 12(9), 4543.
https://doi.org/10.3390/app12094543

Gnjatović M, Košanin I, Maček N, Joksimović D. Clustering of Road Traffic Accidents as a Gestalt Problem. in Applied Sciences. 2022;12(9):4543.
doi:10.3390/app12094543 .

Gnjatović, Milan, Košanin, Ivan, Maček, Nemanja, Joksimović, Dušan, "Clustering of Road Traffic Accidents as a Gestalt Problem" in Applied Sciences, 12, no. 9 (2022):4543,
https://doi.org/10.3390/app12094543 . .