动态的短期撞车风险预测,从信号交叉点的交通冲突与新兴的混合流量交叉点:一个新的冲突指标
Chuanyun Fu1, Zhaoyou Lu2, Huahua Liu2
1School of Transportation Science and Engineering, Harbin Institute of Technology, China; Department of Civil Engineering, The University of British Columbia, Canada.
Accident; analysis and prevention
|April 29, 2025
概括
这项研究引入了一种新的方法,用于预测交叉路口的交通事故风险,使用一种新的冲突指标和先进的机器学习. 该方法改善了混合交通流的积极安全管理.
科学领域:
- 交通工程是交通工程.
- 运输安全运输安全
- 交通运输中的人工智能
背景情况:
- 准确地预测与混合交通交叉路口的车祸风险是具有挑战性的,因为缺乏独家冲突指标.
- 现有的方法与新出现的混合交通流动的动态性质作斗争,影响主动安全措施.
研究的目的:
- 开发一个动态的短期撞车风险预测模型,用于新兴混合流量交叉点的信号交叉点.
- 引入一种新的冲突指标,并将其与先进的机器学习集成,以提高预测准确度.
主要方法:
- 开发了一种新的冲突指标:避免碰撞的时间 (TTAC).
- 使用一个非静止的通用极值 (GEV) 模型与加速变异来计算每分钟的风险值 (VaR).
- 采用基于自我注意力机制的在线学习长期短期记忆 (SAM-OL-LSTM) 方法进行动态VaR预测,使用流量量和速度分布不确定性作为输入.
主要成果:
- 与基线方法相比,SAM-OL-LSTM方法显示出更高的预测准确性.
- 风险值 (VaR) 有效量化了在一分钟内时间分辨率上的撞车风险.
- 开发的TTAC指标显示了在混合交通流中识别交通冲突的强大能力.
结论:
- 拟议的方法为在信号交叉点的动态短期碰撞风险预测提供了一个强大的框架.
- 研究结果支持主动的交通控制策略,通过准确预测混合交通环境中的未来撞车风险.
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