一项关于编织部分转移行为的研究:个性化交通冲突预测和因果机制分析
Renteng Yuan1, Mohamed Abdel-Aty2, Qiaojun Xiang1
1Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing, Jiangsu 210000, PR China.
Accident; analysis and prevention
|June 19, 2024
概括
本研究引入了个性化的交通冲突预测,通过分析车道更换期间的个体驾驶模式. 机器学习模型,特别是LSTM,可以准确预测冲突,从而为更安全的驾驶提供有针对性的干预措施.
科学领域:
- 交通工程是交通工程.
- 运输安全运输安全
- 数据科学数据科学数据科学
背景情况:
- 高速公路上的车道变化,特别是编织段,扰乱了交通流动,增加了冲突风险.
- 了解转移期间的个体驾驶模式对于开发有效,个性化的安全干预措施至关重要.
研究的目的:
- 通过整合个人驾驶模式来开发个性化的交通冲突预测模型.
- 基于不同的转移行为,分析交通冲突的因果机制.
主要方法:
- 车道线路重建,以获得准确的数据.
- 无监督的集群 (k-手段) 来识别不同的转移模式.
- 机器学习模型 (LSTM,XGB,SVM) 用于实时冲突预测.
- 混合逻辑模型用于调查冲突因果关系.
主要成果:
- 用四个集群进行K-平均集群显示出最佳性能.
- 长短期记忆 (LSTM) 模型展示了对个性化冲突预测的卓越准确性和概括性.
- 整合转移模式改善了LSTM模型在准确性,精度和回忆方面的性能.
- 因果分析揭示了影响交通冲突的特定模式因素,例如加速与减速时的速度差异.
结论:
- 个性化交通冲突预测模型,如LSTM,可以显著提高交通安全.
- 识别不同的驾驶模式及其相关冲突原因,可以制定有针对性的安全策略.
- 这些发现支持交通工程师创建个性化的解决方案,以减轻不安全的车道更改行为.
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