一种时空深度学习方法,用轨迹数据模拟高速公路上的冲突风险传播
Tao Wang1, Ying-En Ge1, Yongjie Wang2
1School of Transportation Engineering, Chang'an University, Xi'an, Shaanxi 710064, China.
Accident; analysis and prevention
|November 20, 2023
概括
本研究引入了一个空间时间变压器网络 (STTN) 来模拟高速公路冲突风险传播,提高交通安全. 通过分析车辆行为和替代安全措施,STTN有效地预测潜在的撞车事故.
科学领域:
- 交通工程与安全工程
- 交通运输中的人工智能
背景情况:
- 在高速公路上突然的车辆机动会造成扩散冲突的风险,造成碰撞的危险,并挑战先进的车辆技术.
- 模拟冲突风险传播 (CRPS) 是复杂的,因为复杂的时空依赖和高分辨率的数据需求.
研究的目的:
- 开发一种有效的方法来模拟高速公路上的冲突风险传播模式.
- 引入一个新的空间时间变压器网络 (STTN) 用于细粒度冲突风险推断.
主要方法:
- 引入了冲突风险指数,汇总了替代安全措施 (SSM),如修改的碰撞时间 (MTTC),停车距离的比例 (PSD) 和减速率以避免碰撞 (DRAC).
- 提出了一个具有多头注意力和空间/时间学习组件的空间时间变压器网络 (STTN),以建模空间时间依赖.
- 使用现实世界高速公路数据集,与基准模型 (LSTM,CNN,ConvLSTM) 验证了STTN.
主要成果:
- 与传统的机器学习模型相比,STTN在模拟冲突风险传播方面表现优异.
- 基于PSD的模型显示了跨任务的强大性能,而基于DRAC的模型有效地捕捉了时空异质性.
- 在STTN中的注意力机制在CRPS任务中被证明是有效的.
结论:
- 拟议的STTN提供了一个强大的工具,用于准确地模拟高速公路上的冲突风险传播.
- 这些发现为开发先进的交通安全预警系统,高速公路管理和驾驶员辅助系统提供了宝贵的见解.
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