使用可解释的反向传播神经网络,在信号交叉点对车道分散的建模和分析
Jing Zhao1, Ruoming Ma1, Jian Sun2
1Department of Traffic Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Fundamental research
|August 8, 2025
概括
这项研究开发了一种可解释的神经网络模型,用于预测信号交叉点的车道分散. 该模型准确地识别了影响通过和左转路径分散的关键因素,改进了交通分析.
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
背景情况:
- 车辆在信号交叉点的路径分散是一个复杂的问题.
- 司机的行为和互动有助于路径变化.
- 了解这些分散对于十字路口设计和交通管理至关重要.
研究的目的:
- 开发一种可解释的神经网络模型,用于车辆路径分散.
- 分析路径分散与外部因素之间的关系.
- 为改善交通流动提供有关影响因素的见解.
主要方法:
- 使用了反向传播神经网络模型.
- 分析了20个上海十字路口的真实轨迹数据.
- 考虑了12个几何,交通,信号和管理因素.
主要成果:
- 该模型实现了低平均绝对百分比误差 (14.67%通过,17.65%左转).
- 通过路径的关键因素:出口车道,车道偏移,和度.
- 左转的关键因素:出口车道,转角度,导线.
结论:
- 可解释的神经网络模型有效地预测了车辆路径分散.
- 特定的几何和交通因素显著影响路径分散.
- 这些发现为优化十字路口设计和交通控制提供了宝贵的见解.
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