This research investigates road traffic accident severity in the UK, using a
combination of machine learning, econometric, and statistical methods on
historical data. We employed various techniques, including correlation
analysis, regression models, GMM for error term issues, and time-series
forecasting with VAR and ARIMA models. Our approach outperforms naive
forecasting with an MASE of 0.800 and ME of -73.80. We also built a random
forest classifier with 73% precision, 78% recall, and a 73% F1-score.
Optimizing with H2O AutoML led to an XGBoost model with an RMSE of 0.176 and
MAE of 0.087. Factor Analysis identified key variables, and we used SHAP for
Explainable AI, highlighting influential factors like Driver_Home_Area_Type and
Road_Type. Our study enhances understanding of accident severity and offers
insights for evidence-based road safety policies.

该研究使用机器学习、计量经济学和统计方法研究了英国的道路交通事故严重程度，通过对历史数据进行相关性分析、回归模型、GMM 分析解决误差项问题以及 VAR 和 ARIMA 模型的时间序列预测等技术和方法，我们的研究方法在 MASE 指标为 0.800，ME 指标为 - 73.80 时相对于朴素预测具有更好的性能，此外我们构建了一个随机森林分类器，分类准确性达到了 73%，召回率为 78%，F1 得分为 73%，使用 H2O AutoML 优化后得到的 XGBoost 模型的 RMSE 为 0.176，MAE 为 0.087，因子分析确认了重要变量，并且应用 SHAP 可解释的人工智能方法，对 Driver_Home_Area_Type 和 Road_Type 等有影响的因素进行了突出显示，这项研究加深了对事故严重程度的理解并为基于证据的道路安全政策提供了洞见。

利用人工智能增强对英国道路交通事故严重程度的预测与分析：在公共卫生研究中整合机器学习、计量经济学技术和时间序列预测

Enhancing Prediction and Analysis of UK Road Traffic Accident Severity  Using AI: Integration of Machine Learning, Econometric Techniques, and Time  Series Forecasting in Public Health Research

The increasing adoption of econometric and machine-learning approaches by
empirical researchers has led to a widespread use of one data collection
method: web scraping. Web scraping refers to the use of automated computer
programs to access websites and download their content. The key argument of
this paper is that na\"ive web scraping procedures can lead to sampling bias in
the collected data. This article describes three sources of sampling bias in
web-scraped data. More specifically, sampling bias emerges from web content
being volatile (i.e., being subject to change), personalized (i.e., presented
in response to request characteristics), and unindexed (i.e., abundance of a
population register). In a series of examples, I illustrate the prevalence and
magnitude of sampling bias. To support researchers and reviewers, this paper
provides recommendations on anticipating, detecting, and overcoming sampling
bias in web-scraped data.

通过网页抓取收集数据的方法存在抽样偏差，本研究指出网页抓取数据存在抽样偏差的三个原因，并通过一系列实例揭示抽样偏差的普遍性和严重性，同时提供了关于如何预测、检测和克服抽样偏差的建议。