Short-term Traffic Flow Prediction of Expressway in Plateau Mountainous Areas Based on K-means and GRNN
In order to study the short-term traffic flow prediction method which can be applied to the expressway in plateau and mountains areas, and the impact of prediction method idea on performance, a mixed prediction method idea of k-means clustering algorithm (K-means) and GRNN is proposed based on the general regression neural network (GRNN), that is, the best value of GRNN model parameters is judged by K-means and performance indicator, and then the best prediction model is established. Compared with the traditional idea of judging the model parameter value through experience or certain indicators, the model parameter value obtained by using the K-means and GRNN mixed prediction idea is better, and the RMSE and MAE of the model can be improved by 45.92％ and 45.05％ respectively, so the mixed prediction method idea constructed is scientific and effective, and can provide reference for the optimization of traffic flow prediction method in plateau and mountainous areas.
transportation planning and managementshort-term traffic flow predictionGRNNK-meansexpressway in Plateau Mountainous areas
万方数据
维普
