Short-term Traffic Flow Forecast Based on DE-RBF Fussion Model

Abstract

The road traffic system is a time-varying, complex nonlinear system. Real-time and accurate road short-term traffic flow prediction is the key to realizing the traffic flow guidance system. In order to improve the prediction accuracy of short-term traffic flow, this paper proposes an algorithm based on the fusion model of differential evolution algorithm (DE) and radial basis function (RBF). This method takes the fitness function as the measurement standard, and uses the DE algorithm to optimize the RBF parameters to obtain the optimal short-term traffic flow prediction value. Through MATLAB simulation experiments, a relatively accurate prediction of the short-term traffic flow of the DE-RBF fusion model is realized. The mean square error (MSE) and the average absolute error percentage of actual and predicted values (MAPE) analysis index are introduced as the evaluation index of the prediction model. After comparing with the two prediction network models of radial basis function (RBF) and wavelet function (WNN), the results show that the DE-RBF fusion model proposed in this paper is effective and feasible for short-term traffic flow prediction.

Improving the accuracy of short-time traffic prediction in intelligent transport system based on machine learning algorithms

“…Traffic in cities as a dynamic and completely variable phenomenon is a very big challenge. With the increase in the volume of cars and the population, its problems are increasing day by day [45][46][47][48][49]. Traffic forecasting, which is part of the intelligent transportation system, can help in optimizing and solving the traffic problem.…”