计算机工程与应用2019，Vol.55Issue(4)：96-100,124,6.DOI:10.3778/j.issn.1002-8331.1809-0023
不完全数据下基于时空相关性拥堵预测方法
Traffic Congestion Prediction Based on Spatial-Temporal Correlation with Incomplete Data
摘要
Abstract
Traffic congestion prediction is an important part of smart traffic, but a large amount of traffic data cannot be obtained in a public way. Based on incomplete data, a method based on spatial-temporal correlativity for traffic conges-tion prediction is proposed. The improved kernel density estimation method is adopted, so that the prediction process does not rely on a large amount of historical data for training, and the traffic congestion can be accurately predicted in real time with the partially collected data. The proposed traffic congestion prediction method is validated in the real data set, and the experimental results show the feasibility of this method in real-time traffic prediction.关键词
交通拥堵/时空相关性/核密度估计/实时预测/不完全数据Key words
traffic congestion/ spatial-temporal correlativity/ kernel density estimation/ real-time prediction/ incomplete data分类
信息技术与安全科学引用本文复制引用
安纪存,吕鑫,季琳雅..不完全数据下基于时空相关性拥堵预测方法[J].计算机工程与应用,2019,55(4):96-100,124,6.基金项目
国家重点研发计划课题(No.2018YFC0407105,No.2016YFC0400910). （No.2018YFC0407105,No.2016YFC0400910）
