Data grouping and modified initial condition in grey model improvement for short-term traffic flow forecasting

To improve the performance of the conventional grey model, emphasis should be based on the "new information prior using" principle. This paper presents detailed work on improving the precision of the conventional grey model by combining a data grouping technique with modification of initial condition to establish an optimized grey model. The data grouping technique and modification of initial condition methods have the advantage of adhering to the "new information prior using" principle. An empirical example of short-term traffic flow forecasting shows that the proposed optimized grey model, that is the modified initial condition grouped grey model, outperforms the existing models in both fitting and short-term forecasting. Moreover, the results demonstrate our claim that the distribution characteristic of the fitting error influences future short-term forecast accuracy. Now the proposed model can be of help in intelligent transportation systems for optimizing the use of existing infrastructure to enhance urban transportation systems in averting issues such as traffic congestion.