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分析因素对出租车司机高速行驶的异质影响:可解释的深度学习是否比随机参数方法提供了更多的见解?

Yue Zhou¹, Chuanyun Fu², Xinguo Jiang³

¹Flight Technology College, Civil Aviation Flight University of China, Guanghan, China.

Accident; analysis and prevention

|August 24, 2024

概括

本研究将一般线性模型 (GLM) 的随机参数与机器学习 (ML) 和深度学习 (DL) 进行比较,用于预测出租车超速. 深度学习模型在概括方面表现出色,而GLM在完整数据方面表现更好,验证了可解释的AI方法.

科学领域:

运输科学运输科学
数据科学数据科学数据科学
人工智能的人工智能

背景情况:

具有随机参数的通用线性模型 (GLM) 常用于模拟超速,但缺乏预测性概括.
可解释的人工智能 (XAI) 提供了分析危险驾驶行为的新方法.
在将XAI衍生的见解与传统的GLM出租车超速处理方法进行比较时存在差距.

研究的目的:

为了比较随机参数GLM的预测性能和因子异质性分析,与出租车高距离超速的ML/DL模型进行比较.
评估不同模型的概括能力,以了解影响出租车司机超速行为的因素.
验证使用SHapley添加式扩展 (SHAP) 来量化ML/DL模型中的异质效应.

主要方法:

开发了一个带随机参数 (BGLM-RP) 的Beta GLM来建模出租车超速的可能性.
实现了XGBoost,简单-CNN,DCNN和DCNN与自我注意 (DCNN-SA) 进行比较.
使用SHAP值来导出伪系数,量化ML/DL模型中的异质因子效应.

主要成果:

DCNN-SA在测试数据上表现出优异的预测,表明更好的概括性.
在完整数据上,BGLM-RP的表现优于其他模型.
BGLM-RP和DCNN-SA都显示了异质效应,其中BGLM-RP显示了更大的样本大小,DCNN-SA提供了样本之外的见解.
BGLM-RP系数和ML/DL伪系数之间的相同符号和意义验证了XAI框架.

结论:

深度学习模型,特别是DCNN-SA,为预测出租车超速提供了卓越的概括性.
随机参数GLM在样本分析中仍然有效,并为XAI方法提供了基准.
基于SHAP的解释框架是用于驾驶行为分析的ML/DL模型中量化异质因子效应的有效和有效方法.

关键词:

贝塔概括的线性模型贝塔概括的线性模型深度学习是一种深度学习.高距离超速的可能性.这就是 SHAP SHAP 的意思.出租车超速行为没有观察到的异质性

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