Abstract:

Because urban public transit volume is the fundamental basis for the development and planning of bus system, improving its prediction accuracy is beneficial to the development of urban public transport. By using the good performance of the particle swarm algorithm to optimize the parameters and the advantage of the grey prediction method for predicting uncertainty factors affecting the system, a grey mutation particle swarm combinational prediction model is proposed to predict the urban public transit volume and improve the prediction accuracy of the urban public transit volume. The prediction accuracy and effectiveness of the combinational forecast model are analyzed and verified. The results show that the accuracy of the combinational prediction model outperforms the single gray prediction model and some commonly used prediction algorithms, can predict the urban public transit volume well, and  provides  reliable scientific data for the decision-making and planning of the public transport system.

Key words: grey model;mutation particle swarm optimization;public transport passenger volume;prediction