基于EMD优化NAR动态神经网络的地铁客流量短时预测模型

为了能够更加准确地实现地铁客流预测,提出了一种基于经验模态分解算法(empirical mode decomposition, EMD)优化非线性自回归(nonlinear auto regressive,NAR)动态神经网络的地铁客流量短时预测模型.分析地铁客流量数据后发现日客流量具有一定的变化规律,为此使用了基于时间序列的NAR动态神经网络,该网络具有优秀的非线性动态拟合能力和反馈记忆的功能.结合EMD经验模态分解算法优化NAR动态神经网络预测模型,以此来减少预测误差,提高预测精度.结果显示,EMD-NAR神经网络组合预测模型适用于地铁客流的短时预测,预测精度可达93%,具有较好的应用价值.

Short-Term Prediction Model of Subway Passenger Flow Based on EMD Optimized NAR Dynamic Neural Network

In order to forecast the subway passenger flow more accurately, a short-term prediction model of subway passenger flow based on empirical mode decomposition (EMD) optimization of nonlinear auto regressive (NAR) dynamic neural network is proposed. First, by analyzing subway passenger flow data, we find that the daily passenger flow performs in a certain change rule. Second, we select a time-based NAR dynamic neural network, which has excellent nonlinear dynamic fitting ability and feedback memory function. Furthermore, in order to reduce prediction errors and improve prediction accuracy, we use EMD empirical mode decomposition algorithm to optimize the NAR dynamic neural network prediction model. Simulation results show that the EMD-NAR neural network combined prediction model is well applicable for short-term prediction of subway passenger flow with high prediction result accuracy about 93%.