用机器学习和空间分析，找出美国158号公路事故高发区并提升预测精度。
Statistical and Machine Learning Analysis of Traffic Accidents on US 158 in Currituck County: A Comparison with HSM Predictions
- 结合随机森林与核密度估计，识别事故时空模式。
- 事故严重程度预测准确率达67%，优于手册模型。
- 发现关键路口为热点区域，适合交通管理者参考。
本研究基于2019至2023年北卡罗来纳州柯里特克县8.4英里长的美国158号公路五年交通事故数据，融合先进统计分析、机器学习与空间建模方法，扩展了先前的热点分析与卡方检验。通过核密度估计（KDE）、负二项回归、随机森林分类及高速公路安全手册（HSM）安全绩效函数（SPF）对比，全面揭示事故的时空分布特征。随机森林分类器对伤情严重性预测准确率为67%，优于HSM SPF；Moran's I检验显示空间自相关性显著（I = 0.32, p < 0.001），KDE分析确认主要交叉口附近存在事故热点，验证并拓展了早期识别方法。研究结果为该重要交通走廊提供精准干预建议，推动农村公路安全分析从基础统计向综合方法演进。
原文摘要 · Abstract (English)
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.
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