Deep learning has achieved good performance in short-term traffic forecasting recently.
However, the stochasticity and distribution imbalance are main characteristics to traffic flow …

This study proposes a short-term traffic flow prediction framework. The vector autoregression
(VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model …

Traffic prediction is one of the most important use cases for smart cities. Accurate traffic
information is key to managing traffic issues. Many approaches that use traffic time series …

With the rapid economic development, urban areas are seeing more and more vehicles,
leading to frequent urban traffic congestion. To solve this problem, the forecasting of traffic …

Fin stabilizers are widely used for to reduce ship rolling. However, reducing rolling motion at
low ship speeds remains a challenge because of uncertainties in ship dynamics, transient …

To solve the nonlinear fitting problem of 3-D data, an improved multidimensional Taylor
network (MTN) structure based on the error backpropagation (BP) algorithm is proposed in …

Considering the fact that crude oil markets have various noise, fluctuations and actual needs
of investors in different trading cycles, in this study, we propose a multiwavelet denoising …

The design parameters serve as an integral part of developing a robust short-term traffic
forecasting model. These parameters include scope determination, input data preparation …

In road transport systems, various traffic risks in certain condition could produce joint actions,
which increases the complexity of traffic risk assessment. Previous single risk assessment …

In the prognosis of radar transmitter degradation fault, there are some problems, such as the
total sample size and fault sample size of sensor monitoring data are small, and the …