Paper Infomation
A Short-Term Traffic Flow Prediction Model Based on Quantum Genetic Algorithm and Fuzzy RBF Neural Networks
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Author: Kun Zhang
Abstract: It is important for the successful deployment of intelligent transportation systems to predict traffic flow accurately and timely. Aiming at the limitation of traffic flow prediction model using the conventional prediction model, a short-term traffic flow prediction model based on quantum genetic algorithm and fuzzy RBF neural network (QGA-FRBFNN) is presented in this paper. First, Quantum genetic algorithm (QGA) and fuzzy RBF neural network were introduced. Since QGA have the capacity to find a global solution in a multidimensional search space rapidly, it can optimize the weights and thresholds of fuzzy RBF neural network, which can accelerate the algorithm convergence and seek global optimum. Then the time series of collection traffic flow was modeled by QGA-FRBFNN. Finally, the QGA-FRBFNN could be used in prediction of traffic flow simulation successfully and evaluate the performance of different prediction models such as ARIMA, BP neural network, RBF neural network, fuzzy RBF neural network, GA-FRBFNN and SAA-FRBFNN. Simulation results show that QGA-FRBFNN has a faster convergence speed and a better precision in calculation. At the same time, the QGA-FRBFNN neural network also has better generalization ability, and it was concluded a broad prospect on application.
Keywords: Short-term Traffic Flow; Fuzzy RBF Neural Network; Quantum Genetic Algorithm; Prediction Model
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