A real time neural network learning approach for traffic forecasting
Reliable and accurate short-term traffic forecasting system is crucial in supporting any Intelligent Transportation System. The past two decades have witnessed many forecasting models being developed, yet none of them could consistently outperform the others under various traffic conditions. To deal with the nonlinearity and non-stationarity of dynamic traffic process, a real time neural network learning approach is taken and a traffic flow mode based forecasting method is presented. Results obtained from case study indicate the proposed approach can enhance adaptability of short-term traffic forecasting and has the advantages of better flexibility and transferability.
In this paper, we propose an exact model-based method for near-field sources localization with a bistatic multiple input, multiple output (MIMO) radar system, and compare it with an approximated model-based method. The aim of this paper is to propose an efficient way to use the exact model of the received signals of near-field sources in order to eliminate the systematic error introduced by the use of approximated model in most existing near-field sources localization techniques. The proposed method uses parallel factor (PARAFAC) decomposition to deal with the exact model. Thanks to the exact model, the proposed method has better precision and resolution than the compared approximated model-based method. The simulation results show the performance of the proposed method.