动态的短期碰撞分析和预测在收费站为主动安全管理的收费站
Weiwei Mo1, Jaeyoung Lee2, Mohamed Abdel-Aty3
1School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China.
Accident; analysis and prevention
|January 7, 2024
概括
这项研究使用交通数据预测收费站车祸. 较高的流量变化和占用率增加了碰撞风险,而更多的ETC车道减少了它,有助于实时安全管理.
科学领域:
- 运输工程 运输工程
- 交通安全分析 交通安全分析
- 机器学习应用 机器学习应用
背景情况:
- 收费站是众所周知的交通瓶和事故热点.
- 有限的研究存在于动态的,在收费站的短期碰撞风险预测.
研究的目的:
- 分析和预测主线收费站的动态短期碰撞概率.
- 识别关键事故前体和它们对事故发生的影响.
- 开发和评估用于实时崩预测的先进机器学习模型.
主要方法:
- 利用了交通,几何和天气数据.
- 采用随机效应逻辑回归来识别碰撞前体.
- 应用了长期短期记忆卷积神经网络 (LSTM-CNN) 进行碰撞预测.
- 使用合成少数群体过量采样技术 (SMOTE) 和类权重解决了数据不平衡.
主要成果:
- 流量标准偏差,上游/下游占用率和速度/占用率差异与碰撞概率正相关.
- 增加电子收费 (ETC) 车道比例和夏季季节显示,事故发生的可能性降低了.
- LSTM-CNN模型表现出优越的性能 (AUC,真正阳性率) 超过其他模型,如SSAE-LSTM和CatBoost.
结论:
- 确定了影响收费站动态撞车风险的关键因素.
- 开发的LSTM-CNN模型为实时交通安全管理提供了一个强大的工具.
- 调查结果可以为交通控制策略提供信息,以提高收费广场区域的安全性.
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