@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"
}