Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach release_umageu4apzao3dadjkmzkr7hqe

Abstract

This paper develops a three-step spatial data mining approach to directly identify road clusters with high-frequency crashes (RCHC). The first step, preprocessing, is to store the roads and crashes in a spatial database. The second step is to describe the conceptualization of road–road and crash–road spatial relationships. The spatial weight matrix of roads (SWMR) is constructed to describe the conceptualization of road–road spatial relationships. The conceptualization of crash–road spatial relationships is established using crash spatial aggregation algorithm. The third step, spatial data mining, is to identify RCHC using the cluster and outlier analysis (local Moran's I index). This approach was validated using spatial data set including roads and road-related crashes (2008–2018) from Polk County, IOWA, U.S.A. The findings of this research show that the proposed approach is successful in identifying RCHC and road outliers.
In application/xml+jats format

Archived Files and Locations