A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices

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.

Crash Patterns in the COVID-19 Pandemic: The Tale of Four Florida Counties

“…Figure 8c also illustrates that the census block group with the highest CCD is located near universities (marked by a star on the map). It confirms the conclusion that the "youth (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29) population" ratio in Leon County is highly correlated with crash count decrease; mainly because of the university closure during the COVID-19 pandemic, particularly around the campus area. It is worth mentioning that we had to drop "Households below Poverty Level" and "Aging 65+" variables from the model developed for Leon County in consideration of the existence of multicollinearity with "Young (18-29)", which led to inflation in the regression model.…”

“…Although both are mid-size counties, NBR models for Escambia and Leon provide different results. The contribution of the youth (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29) population ratio in the model reveals a percentage CCD change of 65% for every unit increase in the ratio of youth population living in census block groups of Leon County. This is mainly because of the county's college-oriented nature.…”

“…Based on Table 6, these regressors have different contributions to the models, given their different sign of coefficients and p-values. For Escambia County, neither youth (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29) nor aging (+65) population ratios were highly correlated with CCD. On the other hand, the "Aging (+65)" ratio variable has a significant impact on CCD in Hillsborough County, due to the negative coefficient.…”

“…Specifically, GIS-based crash clustering has been utilized by many agencies to identify roadway segments and intersections that pose a high crash risk [14][15][16]. There are several clustering methods found in the literature, including Getis-Ord (Gi*) statistics [17,18], Bayesian spatiotemporal modeling [19], k-nearest neighbor (KNN) algorithm [20,21], kernel density estimation (KDE) [22], spatial weights matrices [23]. One of the common methodologies used for such a spatial analysis is kernel density estimation (KDE) which can identify the density of events.…”

“…Two specific age group ratios, namely youth (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29) and the elderly (65+), have been considered in the NBR models to determine if they have significant correlations with CCD in census block groups. Based on Table 6, these regressors have different contributions to the models, given their different sign of coefficients and p-values.…”