Traffic accidents pose a severe global public health issue, leading to 1.19
million fatalities annually, with the greatest impact on individuals aged 5 to
29 years old. This paper addresses the critical need for advanced predictive
methods in road safety by conducting a comprehensive review of recent
advancements in applying machine learning (ML) techniques to traffic accident
analysis and prediction. It examines 191 studies from the last five years,
focusing on predicting accident risk, frequency, severity, duration, as well as
general statistical analysis of accident data. To our knowledge, this study is
the first to provide such a comprehensive review, covering the state-of-the-art
across a wide range of domains related to accident analysis and prediction. The
review highlights the effectiveness of integrating diverse data sources and
advanced ML techniques to improve prediction accuracy and handle the
complexities of traffic data. By mapping the current landscape and identifying
gaps in the literature, this study aims to guide future research towards
significantly reducing traffic-related deaths and injuries by 2030, aligning
with the World Health Organization (WHO) targets.

此研究通过对最近在交通事故分析和预测中应用机器学习技术的全面回顾，解决了道路安全领域中对先进预测方法的需求。通过分析 191 项研究，重点关注预测事故风险、频率、严重程度、持续时间以及事故数据的统计分析，该研究展示了整合多样化数据源和先进机器学习技术以提高预测准确性并处理交通数据复杂性的有效性。该综述是首次提供如此全面的回顾，概述了与事故分析和预测相关的广泛领域最新研究现状。通过绘制当前研究格局并鉴别文献中的不足，该研究旨在引导未来研究，实现到 2030 年显著减少与交通相关的死亡和伤害目标，与世界卫生组织（WHO）的目标相一致。