用随机森林预测美国49个州交通事故数量，助力交通安全管理
United States Road Accident Prediction using Random Forest Predictor
- 基于随机森林模型分析49州多源交通数据，预测事故数量
- 结合环境、行为、基建等多因素，准确识别高风险区域与时段
- 适合交通政策制定者和城市规划人员参考使用
道路交通事故严重威胁公共安全，亟需深入分析以制定有效预防策略。本文基于涵盖美国49个州的综合交通数据集，整合来自交通部门、执法机构及交通传感器的信息，聚焦于交通事故数量的预测。采用随机森林、回归分析与时间序列分析等机器学习方法，纳入环境条件、人类行为与基础设施等多种影响因素，实现对道路安全动态的全面理解。通过时空分析，识别出事故趋势、季节性变化及高风险区域。研究结果可为政策制定者与交通管理部门提供精准预测与量化洞察，支持资源高效配置与针对性干预措施，助力制定科学政策，提升道路安全水平。
原文摘要 · Abstract (English)
Road accidents significantly threaten public safety and require in-depth analysis for effective prevention and mitigation strategies. This paper focuses on predicting accidents through the examination of a comprehensive traffic dataset covering 49 states in the United States. The dataset integrates information from diverse sources, including transportation departments, law enforcement, and traffic sensors. This paper specifically emphasizes predicting the number of accidents, utilizing advanced machine learning models such as regression analysis and time series analysis. The inclusion of various factors, ranging from environmental conditions to human behavior and infrastructure, ensures a holistic understanding of the dynamics influencing road safety. Temporal and spatial analysis further allows for the identification of trends, seasonal variations, and high-risk areas. The implications of this research extend to proactive decision-making for policymakers and transportation authorities. By providing accurate predictions and quantifiable insights into expected accident rates under different conditions, the paper aims to empower authorities to allocate resources efficiently and implement targeted interventions. The goal is to contribute to the development of informed policies and interventions that enhance road safety, creating a safer environment for all road users. Keywords: Machine Learning, Random Forest, Accident Prediction, AutoML, LSTM.
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