基于GA-SLSTM模型的城市轨道交通短时客流预测

Short-term passenger flow prediction of urban rail transit based on optimized GA-SLSTM

城市轨道交通短时客流预测可为相关运营部门实时调整行车调度、提高运营效率提供重要的决策依据,为乘客提供合理出行建议。因此,针对具有非线性和随机性等特性的地铁进出站短时客流预测问题,文章在堆叠式长短时记忆(SLSTM,Stacked Long Short Term Memory)模型的基础上,引入遗传算法(GA,Genetic Algorithm),构建了GA-SLSTM预测模型。以10 min为预测粒度对地铁历史运营数据进行整理,分析了客流变化特征,并将其与GA-循环神经网络(RNN,Recurrent Neural Network)模型和LSTM模型的预测效果进行对比。GA-SLSTM预测模型对普通站点和换乘站点预测值的决定系数R2的平均值分别达到0.95和0.90,预测值对真实值的拟合效果较好,预测误差低于其他2种模型,证明该方法可提高地铁短时客流预测的准确性。

Short-term passenger flow prediction of urban rail transit can provide important decision-making basis for relevant operation departments to adjust traffic scheduling in real time and improve operation efficiency,and provide reasonable travel suggestions for passengers.Therefore,aiming at the prediction of short-term passenger flow in and out of subway stations with nonlinear and random characteristics,this paper introduced Genetic Algorithm(GA)on the basis of Stacked Long Short Term Memory(SLSTM)network,and constructed a GA-SLSTM prediction model.The paper sorted out the historical operation data of the subway granularity of 10 minutes,analyzed the characteristics of passenger flow changes,and compared their prediction effects with GA-RNN and LSTM network models.The average value of the decision coefficient R2 of GA-SLSTM method for the predicted values of ordinary stations and transfer stations reached 0.95 and 0.90 respectively.The predicted value has a good fitting effect on the real value,and the prediction error is lower than the other two models,which proves that this method can improve the accuracy of shortterm passenger flow prediction of subway.