Clustering of Road Traffic Accidents as a Gestalt Problem
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2022
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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 Gestal...t
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.
Ključne reči:
traffic accident / clustering / spatially prolonged risk / Gestalt / proximity / open dataIzvor:
Applied Sciences, 2022, 12, 9, 4543-Izdavač:
- Basel, Switzerland : MDPI
Institucija/grupa
JakovTY - 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 . .