模拟自行车撞车频率的混合方法:将基于随机森林的SHAP模型与随机参数负二项回归模型集成
Hongliang Ding1, Ruiqi Wang2, Tiantian Chen3
1Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu 611756, Sichuan, China.
Accident; analysis and prevention
|September 17, 2024
概括
本研究引入了混合RF-SHAP和RPNB模型来分析自行车撞车频率,提高预测准确性和针对性安全干预的因素解释.
科学领域:
- 运输安全运输安全
- 数据科学数据科学数据科学
- 统计建模 统计建模
背景情况:
- 自行车撞车频率分析需要采用捕捉复杂关系和未观察到因素的模型.
- 现有的方法经常在碰撞数据中的非线性和异质性方面扎.
研究的目的:
- 开发和验证一种新的混合框架,将基于随机森林的SHapley添加式扩展 (RF-SHAP) 与随机参数负二项回归 (RPNB) 模型集成在一起.
- 增强对自行车撞车频率数据中复杂,非线性关系的解释,同时考虑未观察到的异质性.
主要方法:
- 对四种机器学习算法 (RF,SVM,GBM,XGBoost) 进行了对变量重要性进行比较分析.
- 将表现最好的随机森林算法与可解释变量影响评估 (RF-SHAP) 的SHAP集成.
- 将RF-SHAP方法与RPNB模型结合起来,分析影响碰撞预测的个体特异变化.
主要成果:
- 拟议的RF-SHAP和RPNB框架证明了对自行车撞车频率的优异预测准确性.
- 通过SHAP值和RPNB因果洞察,提高了影响自行车事故的风险因素的可解释性.
- 该模型显示一致的Akaike信息标准 (AIC) 和贝叶斯信息标准 (BIC) 值,表明可靠的解释能力.
- 在平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 中观察到显著改善.
结论:
- 混合框架有效地将统计模型的解释能力与数据驱动模型的预测能力相结合.
- 可解释的SHAP值和RPNB因果洞察为决策者提供可操作的信息,以制定有针对性的自行车安全干预措施.
- 这种方法为了解和减轻与自行车有关的交通事故提供了一个强大的工具.
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