Abstract
Accurate prediction of traffic flow in urban networks is of great significance for smart city management. A short-term traffic flow prediction algorithm of Quantum Genetic Algorithm - Learning Vector Quantization (QGA-LVQ) neural network is proposed to forecast the changes of traffic flow. Different from BP neural network, Learning Vector Quantization (LVQ) neural network is of simple structure, easy implementation and better clustering effect. Utilizing the global optimization ability of Quantum Genetic Algorithm (QGA), it is combined with LVQ neural network to overcome some shortcomings of LVQ neural network, including sensitive to initial weights and prone to local minima. In order to test the convergence ability and the timeliness of QGA-LVQ neural network in short-term traffic flow, some contrast experiments are performed. Experimental simulation results show that, QGA-LVQ neural network obtains excellent prediction results in prediction accuracy and convergence speed. Besides, compared with GA-BP neural network and wavelet neural network, QGA-LVQ neural network performs better in short-term traffic flow prediction.
Highlights
Along with the rapid development of the economy, people’s travel patterns undergo tremendous changes
LVQ2 neural network introduces secondary winning neurons based on LVQ1 neural network to enhance the performance of network training and improve the classification accuracy of the algorithm
A method for predicting urban traffic network traffic based on Quantum Genetic Algorithm - Learning Vector Quantization (QGA-Learning Vector Quantization (LVQ)) neural network is proposed
Summary
Along with the rapid development of the economy, people’s travel patterns undergo tremendous changes. INDEX TERMS QGA, LVQ neural network, short-term traffic flow prediction, global optimization. Fu et al proposed a short-term traffic flow prediction based on BP neural network, showing a certain nonlinear fitting ability [5].
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