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

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 congestion 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.

Key words: traffic congestion, spatial-temporal correlativity, kernel density estimation, real-time prediction, incomplete data