Real-time prediction of traffic flow values in a short period of time is an important
element in building a traffic management system. The uncertainty, complexity and
nonlinearity of traffic flow data make it difficult to predict traffic flow in real time,
and the accurate traffic flow prediction has been an urgent problem in the industry.
Based on the research of scholars, a traffic flow prediction model based on the
correlation vector machine method is constructed. The prediction accuracy of the
correlation vector machine is better than that of the logistic regression and support
vector machine methods, and the correlation vector machine method has the function
of generating prediction error range for the actual traffic sequence data. The
prediction results are very satisfactory, and the prediction speed is significantly
faster than the other two models, which meets the requirement of real-time traffic
flow prediction and is suitable for real-time online prediction, and the prediction
accuracy of the used method is relatively high. The three-way comparison analysis
shows that the traffic flow prediction by the correlation vector machine method
can describe the nonlinear characteristics of traffic flow change more accurately,
and the model performance and real-time performance are better. The case study
shows that the traffic flow prediction model based on the correlation vector machine
can improve the speed and accuracy of prediction, which is very suitable
for traffic flow prediction estimation with real-time requirements, and provides a
scientific method for real-time traffic flow measurement.