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.
Highlights
According to the World Health Organization, ~1.25 million people die each year on the roads as a result of crashes [1]
File is constructed based on geometric and topological adjacency of road network to describe the conceptualization of road spatial relationships as a foundation of spatial analysis
To improve the versatility of the approach, we use ASCII encoded gwt format file, which is compatible with spatial analysis software such as ArcGIS [32] and GeoDa [33], to store spatial weight matrix of roads (SWMR) data
Summary
According to the World Health Organization, ~1.25 million people die each year on the roads as a result of crashes (traffic accidents) [1]. As a carrier of traffic, roads with ancillary facilities have an important impact on the frequency of crashes. From the perspective of transportation authorities and safety specialists, strategies such as renovating road facilities, improving road traffic conditions, and using prompt signs of crash warning at road clusters with high-frequency crashes (RCHC) are effective in reducing crashes. A review of previous studies shows that data mining [4,5,6] has been widely used to traffic crash analysis. Taamneh et al [9] established a set of rules that can be used by the United Arab Emirates Traffic Agencies to identify the main factors that contribute to accident severity
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