动态路网下城市交通事故风险预测模型研究与实现
Research and Implementation of Urban Traffic Accident Risk Prediction in Dynamic Road Network
董婉青 1赵子榕 2廖惠敏 3肖晖 4张晓亮4
作者信息
- 1. 北京市交通运输综合执法总队 北京 100044
- 2. 北京小马智行科技有限公司 北京 100094
- 3. 北京市交通运输综合执法总队执法保障中心 北京 100044
- 4. 中路高科交通科技集团有限公司 北京 100088
- 折叠
摘要
通过图卷积神经网络对交通事故进行风险预测是交通领域的研究热点.然而,现有的使用图卷积神经网络对交通事故进行风险预测的研究存在着缺乏语义邻接性的构造、无法进行图权重的自适应学习的问题.针对以上问题,文中基于多源交通大数据,构建了数据驱动的多粒度、多视角的时空拓扑图,实现了交通网络中时空关联性和依赖性的精准建模.图上的结点从时间和空间两个维度对路段结点的交通状态进行综合描述,边则从地理邻接性和语义邻接性两个视角表现了路段之间的抽象邻接关系.在时空拓扑图的基础上,文中设计了基于动态时空图网络的交通事故风险预测模型,实现了路段级交通事故风险的准确预测.该模型引入了具有多头注意力机制的空间图网络层对空间关联性进行学习,同时采用了基于一维扩张卷积的时间学习单元捕获短时依赖性与长时周期性.在北京地区的实际交通数据集上进行大规模实验,所提方法的召回率达到0.899,F1-Score达到0.860,其他指标与主流方法相比也均有所提升.
Abstract
Accident risk prediction of traffic accidents through graph convolution networks is a research hotspot in the transpor-tation field.However,the existing researches on using graph convolution networks for accident risk prediction lack semantic adja-cency in graph construction and unable to perform adaptive learning of graph weights.To address these problems,a data-driven,multi-granularity and multi-view spatio-temporal topology graph is constructed based on multi-source traffic big data to realize the accurate modeling of spatio-temporal correlation and dependency in traffic network.The nodes on the graph provide a compre-hensive description of the traffic state from time and space two dimensions,while the edges show the abstract adjacency relation-ship between roadways from geography and semantics two perspectives.Then,a dynamic spatio-temporal graph network based on the spatio-temporal topology graph is designed to achieve accurate prediction of roadway-level traffic accident risk.The model in-troduces spatial graph network layers with multi-headed attention mechanism to learn spatial correlations,while temporal learning units based on 1-D dilated convolution are used to capture short-time dependencies and long-time periodicity.According to large-scale experiments carried out on real traffic data in Beijing area,our method achieves the recall of 0.899 and the F-1 Score of 0.860.Meanwhile,there are also improvements in other indicators comparing to mainstream methods.
关键词
交通事故风险预测/图神经网络/时空数据挖掘Key words
Traffic accident risk prediction/Graph neural network/Spatio-Temporal data mining引用本文复制引用
基金项目
北京市交通行业科技项目(0686-2241B1251414Z)
出版年
2024
NETL
NSTL
万方数据