一种机器学习方法来量化几何设计特征和交通控制设备对部分三叶草交换站终端误路驾驶事件的影响
Qing Chang1, Huaguo Zhou2, Yukun Song3
1Graduate Engineer, Sain Associates, Inc 5021 Technology Dr. NW, Suite B2 Huntsville, AL 35805, USA.
Accident; analysis and prevention
|November 29, 2024
概括
通过识别关键交通控制装置 (TCD) 和设计特征,可以减少部分三叶草交叉点的错误方向驾驶 (WWD) 事件. 机器学习模型准确地预测高风险位置,帮助机构提高道路安全.
科学领域:
- 运输工程 运输工程
- 交通安全 交通安全
- 道路设计 道路设计
背景情况:
- 部分三叶草 (parclo) 交叉路口是美国错误驾驶 (WWD) 事件的常见场所.
- 几何设计和交通控制装置 (TCD) 涉及WWD事故,但其具体影响尚未完全量化.
- 现有研究突出显示,地块交互是WWD的关键入口点,需要进一步调查促成因素.
研究的目的:
- 量化特定的TCD和几何设计特征对 Parclo交换终端中反复发生的WWD事件的影响.
- 根据这些已识别的因素,开发和验证基于WWD事件的预测模型.
- 为运输机构创建一个实用的网络选工具,以识别高风险的WWD地点.
主要方法:
- 从13个州的75个包裹交换终端收集数据,重点关注28个经常发生WWD事件 (共410起事件).
- 机器学习技术的应用,特别是极度梯度提升 (XGboost) 和拉索逻辑回归,以分析WWD事件概率.
- 开发基于验证的预测模型的网络选工具.
主要成果:
- XGboost 模型的平均准确率为 80%,而 Lasso-logistic 回归模型在预测 WWD 事件方面达到 78% 的准确率.
- 确定的特定的TCD和设计特征显著影响了WWD事件的概率.
- 开发的网络选工具有效地根据TCD和设计特征识别了潜在的WWD位置.
结论:
- 机器学习模型可以准确地预测地块交换处发生的WWD事件.
- 了解TCD和设计特征的影响对于减轻WWD至关重要.
- 开发的选工具为运输机构提供了宝贵的资源,以提高货物交叉点的安全性并减少WWD事件.
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