Traffic accident location study based on AD-DBSCAN Algorithm with Adaptive Parameters

Abstract

Aiming at the shortcomings of the traditional Density-Based Spatial Clustering of Applications with Noise -DBSCAN algorithm such as insignificant clustering effect and the choice of parameter combinations. This paper proposes an AD-DBSCAN algorithm with adaptive parameters, which makes the algorithm more difficult in the selection of the parameters. By establishing a DBSCAN algorithm model to adapt to finding the optimal distance threshold and the minimum number of neighbor points, the clustering is more accurate, and the noise point identified in the data is more accurate. Through the observation of the calculation model of the Calinski-Harabasz index, the evaluation index of the clustering algorithm, the selection of the optimal best distance threshold and the minimum number of neighborhood points, the accuracy of noise point recognition is improved by 5 times in the clustering algorithm, and the Calinski-Harabasz index improved by about 39.84%. The applicability of the algorithm in clustering the locations of urban road traffic accidents is verified.