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使用浮动车辆轨迹数据和图形的卷积网络,探索出行模式对空间聚合事故的影响
Jiahui Zhao1, Pan Liu1, Zhibin Li1
1School of Transportation, Southeast University, No.2, Southeast University Road, Jiangning District, Nanjing 211189, China.
Accident; analysis and prevention
|October 17, 2023
概括
这项研究使用机器学习来分析城市撞车模式. 它揭示了车辆行驶,道路密度和特定活动类型等因素对事故发生有重大影响.
科学领域:
- 城市规划是城市规划.
- 运输工程 运输工程 运输工程
- 数据科学是数据科学.
背景情况:
- 了解城市撞车模式对于公共安全至关重要.
- 现有的方法难以捕捉复杂的空间关系和事故数据中的隐藏因素.
- 多个数据源包含有价值的信息,用于预测事故发生.
研究的目的:
- 开发和评估一种新的机器学习模型,用于探索活动模式对城市事故数量的空间影响.
- 整合多样化的数据源,发现影响交通事故的隐藏活动模式.
- 通过考虑空间关系来提高碰撞预测模型的准确性.
主要方法:
- 一个两步框架,结合了隐性迪里克莱特分配 (LDA) 来挖掘车辆轨迹数据中的隐藏活动模式,以及图形卷积网络 (GCN) 来建模空间关系.
- 数据的空间分区为旧金山的175个交通分析区 (TAZ).
- 应用归因算法来确定各种因素对碰撞数量的影响.
主要成果:
- 拟议的 GCN 模型在预测崩数量方面明显优于传统的机器学习算法.
- 该模型成功地确定了与车祸相关的关键因素,包括每天车辆行驶的公里数,道路密度和人口密度.
- 特定的活动模式,如周末的商业活动和周日早上的住宅活动,被发现是重要的预测因素.
结论:
- 机器学习,特别是GCN,为了解城市环境中复杂的空间碰撞模式提供了一种强大的方法.
- 整合从轨迹数据中获得的隐藏活动模式可以提高碰撞预测的准确性.
- 确定关键的贡献因素为有针对性的交通安全干预提供了有价值的见解.
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