The Weather Paradox: Why Precipitation Fails to Predict Traffic Accident Severity in Large-Scale US Data
This work addresses traffic management by identifying key predictors of accident severity, though it is incremental as it applies existing methods to new data with mixed results.
This study investigated environmental, temporal, and spatial factors for predicting traffic accident severity in the US, using an XGBoost model on 500,000 accidents from 2016-2023, achieving 78% accuracy but finding precipitation and visibility had limited predictive power.
This study investigates the predictive capacity of environmental, temporal, and spatial factors on traffic accident severity in the United States. Using a dataset of 500,000 U.S. traffic accidents spanning 2016-2023, we trained an XGBoost classifier optimized through randomized search cross-validation and adjusted for class imbalance via class weighting. The final model achieves an overall accuracy of 78%, with strong performance on the majority class (Severity 2), attaining 87% precision and recall. Feature importance analysis reveals that time of day, geographic location, and weather-related variables, including visibility, temperature, and wind speed, rank among the strongest predictors of accident severity. However, contrary to initial hypotheses, precipitation and visibility demonstrate limited predictive power, potentially reflecting behavioral adaptation by drivers under overtly hazardous conditions. The dataset's predominance of mid-level severity accidents constrains the model's capacity to learn meaningful patterns for extreme cases, highlighting the need for alternative sampling strategies, enhanced feature engineering, and integration of external datasets. These findings contribute to evidence-based traffic management and suggest future directions for severity prediction research.