Clustering of Road Traffic Accidents as a Gestalt Problem

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.

A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas

“…1. The clustering algorithm [19] is applied to data on traffic accidents that occurred in municipality m i over period P 1 . 2.…”

“…Clustering Approach [1] traffic load analysis improved k-means clustering algorithm [2] traffic congestion analysis self-organizing maps neural network [3] traffic state classification k-medoids algorithm [4] road network level identification k-means algorithm [5] traffic congestion analysis grey relational clustering model [6] traffic accidents and pattern extraction ROCK algorithm [7] traffic accident pattern identification COOLCAT algorithm [8] traffic accident factor analysis k-means algorithm [9] road traffic accident modeling a comparative study of machine learning classifiers [10] traffic accident black spots identification HDBSCAN algorithm [11] traffic congestion analysis k-means algorithm [12] driving behavior risk analysis k-means algorithm [13] optimal path routing a modified K-medoids algorithm [14] analysis of pedestrian crash fatalities and severe injuries KDE method [15] traffic-management system DBSCAN agorithm [16] severity of traffic accident analysis DBSCAN algorithm [17] highway safety assessment k-means algorithm [18] pedestrian crash severity analysis KDE method [19] detection of road segments of spatially prolonged and high traffic accident risk a clustering algorithm based on the Gestalt principle of proximity…”

“…It introduces a parameter-free approach to clustering critical traffic road segments in urban areas, i.e., road segments of spatially prolonged and high traffic accident risk. With this respect, we build on and extend the specific approach introduced in [19]. Two traffic accidents are considered related (i.e., as belonging to the same cluster) if the spatial distance between them is less than or equal to a predefined threshold value τ, i.e., a i ∼ a j ⇔ d(a i , a j ) ≤ τ .…”

“…The clustering algorithm introduced in [19] is designed to automatically detect and select critical road segments, intended for application in circumstances of limited human or technical resources for traffic monitoring and management. In line with this, we introduce an integrated measure for traffic accident clustering that promotes the stability of clustering results and inter-period accident spatial collocation index, and penalize the size of selected clusters, i.e., for given city M, threshold value τ j and periods P 1 and P 2 , the integrated measure is defined as…”

“…For given city M and periods P 1 and P 2 , the integrated measure for traffic accident clustering introduced in the previous section can be considered a function with one input parameter-the threshold value, i.e., η(τ). This reduction allows for applying the clustering algorithm introduced in [19] repetitively on traffic accidents occurred in city M over periods P 1 and P 2 by varying its input threshold value τ. This section introduces an algorithm for the selection of an operational threshold value based on the integrated measure defined in Equation (13).…”