基于集成学习的交通拥堵预测模型

Traffic Congestion Prediction Model Based on Integrated Learning

当前的导航软件在面对某些严重的交通拥堵情况的时候有明显的不准确的速度评估,不能准确地预测交通拥堵情况的持续时间。因此,我们提出一种交通拥堵预测模型,通过对速度的预测实现准确的拥堵时间预测。关于速度的预测模型,我们基于KNN算法选择高相似度样本,预测速度模型分为KNN-VA和KNN-RBF这两种主要的模型,并使用集成学习方法对两种模型进行融合,得到更可信的平均速度预测,进而对拥堵时间进行预测。为了能够确定拥堵时间,我们利用了RBF速度预测方法和固定区域内采样的方法来验证。实验证明该模型对拥堵时间预测有较高的可信度。

The current navigation software has obvious inaccurate speed assessment when facing some serious traffic congestion,and cannot accurately predict the duration of the traffic congestion.Therefore,we propose a traffic congestion prediction model to accurately predict the congestion time in the face of most congestion situations through the prediction of speed.Regarding the speed prediction model,we select high-similarity samples based on the KNN algorithm.The prediction speed model is divided into two main models,KNN-VA and KNN-RBF,and we use an integrated learning method to fuse these two models to obtain more accurate average speed prediction.Then,the congestion time can be predicted.In order to determine the congestion time,we use the RBF speed prediction method and the sampling method in a fixed area to verify.The results show that the model has high reliability for congestion time prediction.