Clustering Algorithms to Analyze the Road Traffic Crashes

Islam, Mahnaz Rafia; Jenny, Israt Jahan; Nayon, Moniruzzaman; Islam, Md. Rajibul; Amiruzzaman, Md; Abdullah-Al-Wadud, M.

doi:10.48550/arxiv.2108.03490

Cited by 1 publication

(2 citation statements)

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“…The k-means algorithm employs accident frequency counts as parameters for the clustering of these locations. However, the studies of [14,16,20] show that density-based clustering algorithms and spatial analyses outperform the k-means algorithm when applied to determine accident hotspot locations. The authors of [21] applied k-means to group the combinations of the four indicators into categories with homogeneous effects on run-off-road injury crashes frequency and severity.…”

Section: The Two-step Cluster Methodsmentioning

confidence: 99%

“…In addition, these both algorithms are unable to process categorical data [7,12]. Moreover, the k-means algorithm takes into account the complete dataset and produces clusters with spherical shapes, which may not be ideal for accurately depicting regions prone to traffic accidents [20]. The authors of [7] applied the k-means method to identify highfrequency accident areas.…”

Section: Literature Reviewmentioning

confidence: 99%

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Applying a two-step cluster algorithm in traffic accident data analysis

Khanh Giang,

Ho Thi Lan,

Van Manh

et al. 2024

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Cluster analysis is often employed as the initial stage in organizing heterogeneous data into homogeneous groups. Choosing an effective clustering approach and an ideal number of clusters in a traffic accident dataset might be complex and challenging. This study aims to evaluate the effectiveness of k-means and two-step cluster methods. Subsequently, the two-step cluster method and GIS are applied to analyze the traffic accident datasets from 2015 to 2017 in Hanoi, Vietnam. First, according to the Silhouette score, the two-step cluster method achieved a higher score of 0.563, while the k-means method scored 0.341. A higher Silhouette score indicates more well-defined clusters. Second, the research suggests combining the two-step cluster method with GIS for analyzing traffic accident datasets. The outcome identifies five typical types of accidents in Hanoi. In addition, the locations of various accident types were visually illustrated on a map, enabling traffic officials to recommend precise and urgent countermeasures. Importantly, the clustering results reveal that the two-step cluster method exhibits a significantly higher rate of homogeneous data in the clusters compared to the k-means method. This study demonstrates that the two-step cluster method is not only more effective than the k-means method in terms of clustering ability but also in data pre-processing. The study's results enable authorities to gain a more detailed understanding of typical traffic accident patterns in Hanoi. Besides, the employed methods could potentially be applied to other regions, providing an additional avenue for analysis

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