The traffic forecasting model, when considered as a system with inputs of historical and current data and outputs of future data, behaves in a nonlinear fashion and varies with time of day. Traffic data are found to change abruptly during the transition times of entering or leaving rush hours. Accurate and real-time models are needed to approximate the… (More)
The paper surveys various software fault tolerance techniques and methodologies. The techniques include traditional techniques: recovery for each technique based on its attribution has also been presented.
This paper addressed a framework of a traffic prediction model which could eliminate the noises caused by random travel conditions. In the meantime, this model can also quantitatively calculate the influence of special factors. This framework combined several artificial intelligence technologies such as wavelet transform, neural network, and fuzzy logic. In… (More)
This paper addresses the issue of the interval forecasting (constructing prediction intervals for future observations) of the traffic data time series using one of local polynomial nonparametric models - the local linear predictor. Two methods are proposed and compared. One is based on the theoretical formulation of the asymptotic prediction intervals and… (More)
For the ATSC-Mobile DTV standard has much more training-sequence than the conventional ATSC digital television standard, in this paper, we propose a modified channel estimation structure using the additional training sequence to track the channel changes, which can fast start-up the receiver equalizer of ATSC-M/H digital television system in time varying… (More)