  Traffic flow forecasting is hot spot research of intelligent traffic system
construction. The existing traffic flow prediction methods have problems such
as poor stability, high data requirements, or poor adaptability. In this paper,
we define the traffic data time singularity ratio in the dropout module and
propose a combination prediction method based on the improved long short-term
memory neural network and time series autoregressive integrated moving average
model (SDLSTM-ARIMA), which is derived from the Recurrent Neural Networks (RNN)
model. It compares the traffic data time singularity with the probability value
in the dropout module and combines them at unequal time intervals to achieve an
accurate prediction of traffic flow data. Then, we design an adaptive traffic
flow embedded system that can adapt to Java, Python and other languages and
other interfaces. The experimental results demonstrate that the method based on
the SDLSTM - ARIMA model has higher accuracy than the similar method using only
autoregressive integrated moving average or autoregressive. Our embedded
traffic prediction system integrating computer vision, machine learning and
cloud has the advantages such as high accuracy, high reliability and low cost.
Therefore, it has a wide application prospect.
