灰色变异粒子群算法在公交客流量预测中的应用

由于公交客流量是公交系统发展规划的基础依据,因此提高公交客流量预测的准确性有利于城市公交的发展。利用粒子群算法优化参数的良好性能和灰色预测法适合预测不确定因素影响系统的优势,提出用灰色变异粒子群组合预测模型来预测公交客流量,提高公交客流量预测精度,并通过实例对组合预测模型的预测精度和有效性进行了分析。结果表明,此组合预测模型的预测精度优于单一的灰色预测模型,也优于其他几种常用预测算法,能很好地预测公交客流量,为公交系统的决策规划提供了可靠的科学数据。

Application of the grey mutation particle swarm algorithm in urban public transport passenger volume prediction

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.