优化中国交通事故损失预测:将重要性指标选与ET模型集成,以获得更高的准确性和稳定性
Jian Liu1,2, Bin Lyu1, Rui Feng1,3
1School of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing, People's Republic of China.
Traffic injury prevention
|August 8, 2025
概括
这项研究利用机器学习增强了中国交通事故损失预测. 具有特征选的Extra Trees模型准确地预测了事故数量,死亡,受伤和损失,改善了交通安全洞察力.
科学领域:
- 运输科学 运输科学
- 数据科学数据科学数据科学
- 预测分析是一种预测分析.
背景情况:
- 交通事故在中国造成重大损失.
- 准确预测事故损失对于有效的安全管理至关重要.
- 现有的预测模型可能缺乏准确性和稳定性.
研究的目的:
- 提高中国交通事故损失预测的准确性和稳定性.
- 应用额外树木机器学习模型与特征重要性选相结合.
- 预测关键事故指标:事故数量,死亡,受伤和财产损失.
主要方法:
- 从国家统计来源收集运输行业指标.
- 采用两步特征选方法 (平均重要性和重要性比) 来减少维度.
- 利用额外树木算法进行预测建模,并通过多次实验运行评估准确性和稳定性.
主要成果:
- 功能选方法提高了预测准确性和可解释性.
- 平均预测错误达到4.66% (事故),1.92% (死亡),10.03% (伤害) 和5.01% (损失).
- 确定了30个有影响力的指标,其中高速公路里程,级别高速公路里程和平均货物距离的影响最大.
结论:
- 将特征重要性选与Extra Trees模型的整合有效地预测了交通事故损失.
- 该方法提高了预测准确度,并确保了各种事故指标的稳定性能.
- 通过量化指标的重要性,为交通安全政策和规划提供数据驱动的见解.
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