检索结果-内蒙古大学图书馆

Research on prediction method of expressway section traffic flow considering anomaly detection probability

IET INTELLIGENT TRANSPORT SYSTEMS 2024年第1期18卷 16-28页

作者： Wang, Zhiyu Li, Linheng Qu, Xu Mao, Peipei Ran, Bin Southeast Univ Sch Transportat Nanjing Peoples R China

Real-time and accurate short-term traffic flow prediction can provide a scientific basis for decision making by travellers and traffic management, and alleviate traffic congestion to a certain extent. The existing traffic flow prediction methods often encounter limitations in real-time performance and accuracy due to the post-processing required to rectify anomaly detection results during data pre-processing. This paper presents a novel traffic flow prediction model, termed the With-anomaly detection probability (ADP) Attention-Bidirectional Long-Short Term Memory (BiLSTM) model. This model takes the probability of anomaly detection into consideration, integrating the anomaly detection outcomes as an inherent parameter into the traffic flow prediction framework. Additionally, the model incorporates an attention mechanism within the long-short term memory network. Through a comprehensive simulation study utilizing actual measured traffic flow data from the Shanghai-Chongqing Expressway, the effectiveness of the proposed model is rigorously evaluated. The prediction results show that the model proposed in this paper is a real-time and accurate short-time traffic flow prediction model compared with the basic models. This paper presents a novel traffic flow prediction model, termed the With-ADP Attention-BiLSTM model. This model takes the probability of anomaly detection into consideration, integrating the anomaly detection outcomes as an inherent parameter into the traffic flow prediction framework and incorporates an attention mechanism within the long-short term memory network. The prediction results show that the model proposed in this paper is a real-time and accurate short-time traffic flow prediction model compared with the basic ***

关键词： anomaly detection probability Attention Mechanism Recurrent Neural Network Traffic Flow Prediction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：