摘要
短时客流预测在为人类构建智慧城市,提供风险预警,保证出行安全中扮演着重要的角色.本文在神经网络算法的基础上,结合卡尔曼滤波,提出了一种新型有效的地铁客流短时预测算法.对于要预测的时刻t,算法利用它之前24小时的客流量作为输入特征.由于实验数据存在噪声,本文利用卡尔曼滤波对实验数据进行去躁平滑处理.最后算法利用BP神经网络和LSTM递归神经网络进行建模与预测.我们利用杭州地铁提供的真实购票数据进行大量实验,证明了BP神经网络(基于adam算法和relu激活函数)以及LSTM递归神经网络(基于adam算法和tanh激活函数的)准确度最高,预测的平均绝对误差最小(5%左右).另外,实验还证明了卡尔曼滤波能够有效减少预测的平均绝对误差.相比于不使用卡尔曼滤波的神经网络,使用卡尔曼滤波后的神经网络算法可以降低相对25%的MAE.
Subway short-term passenger flowforecast plays an important role in construction of intelligent city,providing risk warning and ensuring the safety. In this paper,based on the neural network,combined with Kalman filter,we propose an effective subway shortterm passenger flowforecast algorithm. The algorithm uses the 24 hours before the prediction time as the input feature. After filtering through the Kalman filter,the BP neural network and the LSTMrecurrent neural network are used to predict. The Finally,we use the real purchase data provided by Hangzhou subway to carry out a large number of experiments. The results showthat the BP neural network based on adam algorithm and relu activation function has a similar MAE( mean absolute error) with the recursive neural network algorithm based on adam algorithm and tanh activation function. Both of them have the minimum MAE( about 5%). In addition,it is proved that Kalman filter can effectively reduce the MAE of prediction. Compared with the non-use of Kalman filter neural network,the neural network algorithm with Kalman filter can reduce the relative 25% of the MAE.
引文
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