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Open AccessArticle

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

1
School of Architecture and Materials Engineering, Hubei University of Education, Wuhan 430205, China
2
Department of Mechanical Engineering, University of Houston, Houston, TX 77204, USA
3
Department of Information System, Arizona State University, Tempe, AZ 85281, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(24), 5282; https://doi.org/10.3390/app9245282
Received: 1 November 2019 / Revised: 24 November 2019 / Accepted: 27 November 2019 / Published: 4 December 2019
(This article belongs to the Special Issue Intelligent Transportation Systems)
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. View Full-Text
Keywords: data mining; road network; traffic crash; road clusters with high-frequency crashes (RCHC); spatial weight matrix (SWM); local Moran’s I index; cluster identifying data mining; road network; traffic crash; road clusters with high-frequency crashes (RCHC); spatial weight matrix (SWM); local Moran’s I index; cluster identifying
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Zhang, Z.; Ming, Y.; Song, G. Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach. Appl. Sci. 2019, 9, 5282.

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