连续变速限制下的短期驾驶速度预测:使用广域轨迹数据的可解释深度学习方法
Junhua Wang1, Yiwei Ren1, Ting Fu1
1The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China; College of Transportation, Tongji University, 4800 Cao'an Highway, Shanghai 201804, China.
Accident; analysis and prevention
|November 18, 2025
概括
本研究引入了一种深度学习模型,用于预测可变速度限制 (VSL) 下的驾驶员速度. 重型车辆始终减速,而轻型车辆在较低的VSL中更适应,车道位置显著影响了响应.
科学领域:
- 运输工程 运输工程
- 人工智能的人工智能
- 交通安全 交通安全 交通安全
背景情况:
- 当前变速极限 (VSL) 研究往往缺乏现实世界的微观轨迹数据.
- 了解VSL下的驾驶员行为对于交通安全和效率至关重要.
研究的目的:
- 开发一个可解释的深度学习框架,用于在连续VSL控制下预测短期驾驶速度.
- 量化分析驾驶员行为和时空特征对VSL响应的影响.
主要方法:
- 利用了2.2公里高速公路段的宽带车辆轨迹数据,其中有两个连续的VSL标志.
- 开发了一个卷积神经网络 - 双向长期短期记忆 (CNN-BiLSTM) 模型,结合了多视图时空注意力机制 (MSTAM).
主要成果:
- 在VSL下,重型车辆持续减速;轻型车辆根据VSL水平和车道位置表现出不同的反应.
- 左车道的司机比右车道的司机更迅速,更果断地做出反应.
- 第二个VSL标志显示出比第一个更高的监管效率,MSTAM模型的表现优于基线CNN-BiLSTM.
结论:
- 拟议的MSTAM模型准确地预测驾驶员的速度,并捕捉时空注意力模式,提供对适应性驾驶员反应的见解.
- 调查结果支持加强VSL部署和特定车道的速度控制策略,以改善交通管理.
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