Details of Research Outputs

The existing traffic parameter prediction methods generally adopt a single prediction model, but the fusion of different theories and methods can complement each other and improve the prediction performance of the model. Starting from the statistical distribution characteristics of traffic flow, this work introduces the Richards equation to conduct grey modeling, which is used to simulate the development trend of traffic parameters; and then fuses the abilities of error feedback adjustment and complex nonlinear fitting of neural network to estimate the parameters of the grey model and forecast the volatility of traffic flow respectively. At the same time, a dynamic prediction framework of real-time data update is built for the new model, and finally establish the dynamic grey Richards neural network model (DGR-NN).Apply the new model to different traffic parameters (Speed; Volume; Jam mileage) and data resolutions (5 min; 15 min; 1 h), the modeling effect of DGR-NN is significantly improved compared to the grey Richards model (GRM), and the training and testing errors of the model in the three forecast scenarios are reduced to varying degrees, where the testing MAPE, RMSE and STD are reduced by 1.80% ∼ 10.87%, 2.55% ∼ 7.26% and 8.08–25.56% respectively. Furthermore, the results of the new model were verified and analyzed with the other five comparison models, among which the boxplot of APE shows that the error distribution of DGR-NN prediction data is concentrated and the value level is relatively low. It can be seen that DGR-NN can accurately and stably forecast traffic parameters of different time granularities.