GCPSO优化混合核SVM的地铁车站客流预测

Subway station passenger flow forecast based on mixed kernel support vector machine optimized by golden section chaotic particle swarm optimization

地铁中站点客流量为地铁运营调度部门提供实时调度管理依据。将径向基核函数与多项式核函数线性组合,构建了混合核支持向量回归机(SVM)预测模型。采用基于黄金分割的混沌粒子群(GCPSO)对混合核SVM的参数进行寻优,得到最佳的参数组合。利用该混合核SVM预测广州地铁3号线站点短期客流量。结果表明,GCPSO优化的混合核SVM预测模型对地铁站点的短期客流的预测精度高,预测数据和实测数据拟合良好,相对误差较小,明显优于SVM其他三种预测方法及Elman神经网络预测方法。

Real-time traffic management of subway operation scheduling departments depends on subway station passenger flow. This paper builds a hybrid kernel Support Vector regression Machine（SVM）forecasting model with a linear combination of radial basis function and polynomial kernel function. This paper proposes the Golden section Chaotic Particle Swarm Optimization（GCPSO）to search the optimal combination of mixed kernel SVM parameters. The results of short-term passenger flow prediction with the hybrid kernel SVM in Guangzhou Metro Line 3 indicate that GCPSO mixed kernel SVM forecasting model is superior to other three SVM prediction methods and Elman neural network forecasting with high precision of short-term passenger flow forecasting, good fitting of predicted and measured data and less proportional errors.