针对 LSTM应用于城市轨道短时流量预测存在的模型参数确定困难、对预测精度影响大的问题,采用改进的 BA 算法对模型参数进行优化.对传统 BA 算法,采用自适应策略来动态调整脉冲频率和蝙蝠速率,同时蝙蝠位置更新模型中引入随机扰动项,提高了 BA 的优化性能.采用 BA 对 LSTM参数进行优化,提出了基于改进 LSTM的城市轨道短时流量预测模型.将提出的模型应用于郑州地铁 1 号线,通过和 BP 神经网络预测模型、LSTM 预测模型的对比,验证了所提出的改进 LSTM预测模型具有更高的预测精度.
Research on Short-term Flow Prediction of Urban Rail Based on Improved LSTM
Aiming at the difficulties in determining the model parameters of LSTM applied to urban rail short-term flow prediction,which has great influence on prediction accuracy,the im-proved BA algorithm is used to optimize the model parameters.For the traditional BA algorithm,the adaptive strategy is used to dynamically adjust the pulse frequency and bat rate,and the ran-dom disturbance term is introduced into the bat position update model,which improves the opti-mization performance of BA.Using BA to optimize LSTM parameters,a short-term urban rail flow prediction model based on improved LSTM is proposed.The proposed model is applied to Zhengzhou Metro Line 1,and compared with BP neural network prediction model and LSTM pre-diction model;the improved LSTM prediction model proves to have higher prediction accuracy.
NETL
NSTL
万方数据
