基于注意力的时空图形卷积网络与焦点损失,用于基于多源风险的城市道路交通网络上的碰撞风险评估
Xian Liu1, Jian Lu1, Xiang Chen2
1Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China; School of Transportation, Southeast University, Nanjing 211189, China.
Accident; analysis and prevention
|August 20, 2023
概括
本研究引入了基于注意力的空间时间图卷积网络 (ASTGCN),以准确评估城市道路事故风险. 该模型有效地处理复杂的数据,并通过识别交通流动等关键风险因素来改善安全管理.
科学领域:
- 运输工程 运输工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 由于复杂的结构和多源数据,城市道路网络面临重大事故风险.
- 由于高维空间时间相关性和不平衡的数据集,评估碰撞风险具有挑战性.
研究的目的:
- 开发和评估一种先进的模型,用于评估城市道路网络中的碰撞风险.
- 为了应对复杂的网络结构,多源数据和崩风险预测中的数据不平衡所带来的挑战.
主要方法:
- 开发了一个基于注意力的空间时间图卷积网络 (ASTGCN) 模型,结合了焦点损失函数.
- 该模型利用图形卷积来捕捉时空属性和注意力机制来识别关键风险.
- 使用现实世界城市交通数据评估性能,并与ANN,RF和DSTGCN等基线模型进行比较.
主要成果:
- 与基线方法相比,ASTGCN模型在评估撞车风险方面表现优越.
- 焦点损失函数通过解决数据集不平衡,显著改善了模型性能.
- 交通流被确定为影响模型性能的最关键因素.
结论:
- 拟议的ASTGCN模型为评估城市道路网络中的动态撞车风险提供了一个有效的解决方案.
- 通过准确的风险评估,研究结果支持加强城市道路运输的安全管理策略.
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