A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices
Source abstract
Abstract
In this paper we develop a new approach to directly detect crash hotspot intersections (CHIs) using two customized spatial weights matrices, which are the inverse network distance-band spatial weights matrix of intersections (INDSWMI) and the k-nearest distance-band spatial weights matrix between crash and intersection (KDSWMCI). This new approach has three major steps. The first step is to build the INDSWMI by forming the road network, extracting the intersections from road junctions, and constructing the INDSWMI with road network constraints. The second step is to build the KDSWMCI by obtaining the adjacency crashes for each intersection. The third step is to perform intersection hotspot analysis (IHA) by using the Getis–Ord Gi* statistic with the INDSWMI and KDSWMCI to identify CHIs and test the Intersection Prediction Accuracy Index (IPAI). This approach is validated by comparison of the IPAI obtained using open street map (OSM) roads and intersection-related crashes (2008–2017) from Spencer city, Iowa, USA. The findings of the comparison show that higher prediction accuracy is achieved by using the proposed approach in identifying CHIs.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic and Road Safety · Automated Road and Building Extraction · Data-Driven Disease Surveillance
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Architecture and Materials Engineering, Hubei University of Education, Wuhan 430205, China
Hubei BIM Smart Construction International Science & Technology Cooperation Base, Wuhan 430205, China
Department of Mechanical Engineering, University of Houston, Houston, TX 77204, USA
Department of Information System, Arizona State University, Tempe, AZ 85281, USA
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