Statistical and Machine Learning Analysis of Traffic Accidents on US 158 in Currituck County: A Comparison with HSM Predictions

TL;DR

This paper combines advanced statistical, machine learning, and spatial analysis techniques to study traffic accidents on US 158, providing new insights into crash patterns and safety performance compared to traditional methods.

Contribution

It introduces a comprehensive methodological framework integrating KDE, negative binomial regression, and machine learning to analyze rural highway safety, extending prior hotspot analysis.

Findings

Random Forest predicts injury severity with 67% accuracy

Hotspots are confirmed near major intersections via spatial clustering

KDE reveals spatial crash hotspots aligning with previous analyses

Abstract

This study extends previous hotspot and Chi-Square analysis by Sawyer \cite{sawyer2025hotspot} by integrating advanced statistical analysis, machine learning, and spatial modeling techniques to analyze five years (2019--2023) of traffic accident data from an 8.4-mile stretch of US 158 in Currituck County, NC. Building upon foundational statistical work, we apply Kernel Density Estimation (KDE), Negative Binomial Regression, Random Forest classification, and Highway Safety Manual (HSM) Safety Performance Function (SPF) comparisons to identify comprehensive temporal and spatial crash patterns. A Random Forest classifier predicts injury severity with 67\% accuracy, outperforming HSM SPF. Spatial clustering is confirmed via Moran's I test ($I = 0.32$, $p < 0.001$), and KDE analysis reveals hotspots near major intersections, validating and extending earlier hotspot identification methods. These results support targeted interventions to improve traffic safety on this vital transportation corridor. Our objective is to provide actionable insights for improving safety on US 158 while contributing to the broader understanding of rural highway safety analysis through methodological advancement beyond basic statistical techniques.