Fatal Crash Data Analysis
Tableau dashboards on the FARS 2022 fatal-crash dataset, surfacing which collision types and lighting/weather conditions drive the most fatalities.
UGA · MIST 4610 (Group Project 2) · with Kipras Kairys, Adriano Onate, Maxwell Shank
Dataset
We used the Fatality Analysis Reporting System (FARS) 2022 Accidents dataset from Data.gov, which records every fatal motor-vehicle crash in the United States in 2022. Each row is one fatal crash; columns cover location (state, county, coordinates), date and time, road and weather conditions, the vehicles involved, and the type of crash.
The data mixes numeric and string fields — numeric values are mostly coded identifiers for things like location or crash factors, with string values describing those categories more clearly. Together they give a broad, detailed picture of fatal crashes across the country, well suited to pattern analysis and visualization.
Question 1 — Fatalities by collision type
What is the total number of fatalities associated with each type of collision event? Knowing which crash types lead to the most fatalities guides where road-safety effort should go — policy, awareness campaigns, and roadway design — and carries real economic weight through emergency response, medical care, and property damage.
This visualization compares fatality counts across crash types in FARS 2022, focusing on the ten most common types for clarity. It makes the highest-risk events immediately legible.
Question 2 — Lighting × weather and fatal outcomes
How do different combinations of lighting and weather relate to fatal crash outcomes? Identifying which environmental conditions contribute most to fatal crashes can guide lighting infrastructure, weather-related safety guidance, and driver awareness in high-risk situations.
This visualization compares fatality counts across combinations of lighting and weather in FARS 2022, surfacing which conditions create the highest risk for drivers.