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.
Highlights
The nation’s transportation infrastructural systems are deteriorating [1] under adverse influences from multiple factors, such as corrosion [2,3], aging [4], impact [5,6], and vibration [7], and even with the recent advances in structural health monitoring [8,9,10] and intelligent transportation systems [11,12], traffic crashes still happen
We developed the INDSWMI generation algorithm to INDSWMI, which can conceptualize the spatial relationships between intersections, with road construct the INDSWMI, which can conceptualize the spatial relationships between intersections, with road network constraints based on the intersection table and road segment table
In this paper we successfully demonstrated an approach to directly identify crash hotspot intersections (CHIs) using spatial weights matrices (SWMs)
Summary
The nation’s transportation infrastructural systems are deteriorating [1] under adverse influences from multiple factors, such as corrosion [2,3], aging [4], impact [5,6], and vibration [7], and even with the recent advances in structural health monitoring [8,9,10] and intelligent transportation systems [11,12], traffic crashes still happen. The latest quick facts report from the National Highway Traffic Safety. Administration indicates that there were 2,746,000 people injured in 6,452,000 police-reported crashes in 2017 in the USA [13]. As junctions of traffic flow and pedestrian flow, intersections with ancillary facilities have an important impact on the frequency of crashes. Intersection-related crashes, which account for a large portion of all crashes, need more research attention. Iowa, USA, saw about 225,185 intersection-related crashes, about 40.41% of all crashes, from 2008 to 2018 [14]. Given the fact of the massive number of intersections, identifying crash hotspot intersections (CHIs) is an important but challenging task
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