Internet of Vehicles traffic flow prediction method based on quantum particle swarm optimization strategy

Abstract

The invention discloses an Internet of Vehicles traffic flow prediction method (MPSO-RBF) based on a quantum particle swarm optimization strategy, and solves the problem of how to accurately predict the future traffic flow of urban roads. The method comprises the following steps: establishing a traffic flow prediction mathematical model, namely establishing a corresponding model according to traffic flow data characteristics, optimizing an initial clustering center by using a simulated annealing algorithm and a genetic algorithm, and training an RBF network by using a fuzzy mean clustering algorithm; an improved quantum particle swarm optimization strategy is used, the randomness of particle positions is increased, and optimized neural network parameters are output; and applying the optimized algorithm to parameter optimization of the radial basis function neural network prediction model, and obtaining a data result needing to be predicted through high-dimensional mapping of the radialbasis function neural network. Test results show that the algorithm provided by the invention can reduce prediction errors and obtain better and more stable prediction results.

Description

【Technical field】 [0001] The invention belongs to the field of the Internet of Vehicles, and in particular relates to a traffic flow prediction method for the Internet of Vehicles based on a quantum particle swarm optimization strategy. 【Background technique】 [0002] In the intelligent transportation system (ITS), due to the mobility and randomness of vehicles, they are all random factors affecting the traffic data flow, so the traffic flow data is often difficult to predict accurately. In order to solve the problem of forecasting traffic flow data in ITS, many scholars have proposed various forecasting methods with different characteristics. These methods can generally be divided into two types: traditional forecasting methods and intelligent forecasting methods. Traditional traffic forecasting methods include Markov, Poisson, ARMA, etc. But they are based on linear methods. With the rapid development of traffic scale, traffic presents complex, nonlinear and time-varyin...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the invention is to solve the problems of slow convergence speed, low precision and premature population in the PSORBF neural network algorithm

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