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.

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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.