Spatial and Statistical Analysis of Crash Frequency and Severity: An Arlington, VA Case Study

Abstract

Understanding crash data is crucial to identifying patterns that can guide the development of safety infrastructure and countermeasures. This project conducts data-driven analyses, like geoanalysis and frequency analysis, to help understand crash risks, especially in urban areas such as Arlington. However, crashes are complex and influenced by multiple factors, therefore it is crucial to explore the nuances that could predict the relationship between crash severity and independent factors based on crash data provided by the Virginia Department of Transportation covering crashes from 2017 to today. By using tools such as ArcGIS Pro and StataBE 18, we aim to test the correlation between various factors to explain the frequency and severity of crashes. Spatial tools in ArcGIS, such as Near, Join Field, and Summary Statistics, allow us to explore the geolocation of different data layers and identify any potential connections. Comparing multiple fatal crashes in Arlington (between 2017-2025), the AADWT (average annual daily weekday traffic) varied for each, ranging from as many as 47,000 vehicles per day to as low as 740 vehicles per day. Roads with the highest AADWT (around 100,000) often had relatively few crashes, while roads with lower AADWT (below 10,000) had the greatest amount of crash frequencies, peaking at 268 crashes on a roadway with an AADWT of 9,300. These outliers highlight the need to explore infrastructure and behavioral reasons as the relationship between traffic volume and crash occurrence is not proportional but rather random. The findings from this study may help target risk areas that are in need for safety improvements and inform the need for safety features and policies.