  Traffic flow characteristics are one of the most critical decision-making and
traffic policing factors in a region. Awareness of the predicted status of the
traffic flow has prime importance in traffic management and traffic information
divisions. The purpose of this research is to suggest a forecasting model for
traffic flow by using deep learning techniques based on historical data in the
Intelligent Transportation Systems area. The historical data collected from the
Caltrans Performance Measurement Systems (PeMS) for six months in 2019. The
proposed prediction model is a Variational Long Short-Term Memory Encoder in
brief VLSTM-E try to estimate the flow accurately in contrast to other
conventional methods. VLSTM-E can provide more reliable short-term traffic flow
by considering the distribution and missing values.

Machine Learning;Neural and Evolutionary Computing;Signal Processing