融合异构交通态势的事故预测模型  

Accident prediction model fusing heterogeneous traffic situations

作　　者：杨博 段宗涛[1] 左鹏飞 肖媛媛 王艺霖 YANG Bo;DUAN Zongtao;ZUO Pengfei;XIAO Yuanyuan;WANG Yilin(School of Information Engineering,Chang’an University,Xi’an Shaanxi 710064,China)

机构地区：[1]长安大学信息工程学院,西安710064

出　　处：《计算机应用》2023年第11期3625-3631,共7页journal of Computer Applications

基　　金：陕西省重点研发计划项目(2019ZDLGY17-08,2019ZDLGY03-09-01);陕西省“特支计划”科技创新领军人才项目(TZ0336)。

摘　　要：针对事故数据信息表达有限、数据不平衡以及数据中存在动态时空特性的问题,提出一种融合异构交通态势的事故预测模型。其中:时空状态聚合模块通过代表动态交通态势的交通事件和天气特征完成语义增强,并聚合四种区域(单一区域、邻近区域、相似区域和全局区域)的历史多时段时空状态;时空关系捕获模块从微观和宏观角度捕获事故数据局部与全局的动态时空特性;时空数据融合模块进一步融合多区域、多角度的时空状态,并完成下一时段的事故状况预测任务。在US-Accident的5个城市数据集上进行实验,结果表明所提模型的正样本、负样本、加权正负样本的平均F1分数分别为85.6%、86.4%和86.6%,与传统的前馈神经网络(FNN)模型相比,在三个指标上分别提升了14.4%、5.6%和9.3%,能有效抑制事故数据不平衡对实验结果的影响。构建高效的事故预测模型有助于分析道路交通安全形势,减少交通事故的发生,提高交通安全。To address the problems of limited information expression,imbalance,and dynamic spatio-temporal characteristics of accident data,an accident prediction model fusing heterogeneous traffic situations was proposed.In which,the semantic enhancement was completed by the spatio-temporal state aggregation module through traffic events and weather features representing dynamic traffic situations,and the historical multi-period spatio-temporal states of four types of regions(single region,adjacent region,similar region,and global region)were aggregated;the dynamic local and global spatiotemporal characteristics of accident data were captured by the spatio-temporal relation capture module from both micro-and macro-perspectives;and the multi-region and multi-angle spatio-temporal states were further fused by the spatio-temporal data fusion module,and the accident prediction task in the next period was realized.Experimental results on five city datasets of US-Accident demonstrate that the average F1-scores of the proposed model for accident,non-accident,and weighted average samples are 85.6%,86.4%,and 86.6%respectively,which are improved by 14.4%,5.6%,and 9.3%in the three metrics compared to the traditional Feedforward Neural Network(FNN),indicating that the proposed model can effectively suppresses the influence of accident data imbalance on experimental results.Constructing an efficient accident prediction model helps to analyze the safety situation of road traffic,reduce the occurrence of traffic accidents and improve the traffic safety.

关 键 词：交通事故预测模型 交通事故数据 时空特性 深度学习 交通安全 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] U491.31[自动化与计算机技术—计算机科学与技术]