基于VMD-LSTM轨道交通客流预测模型

客流量预测是城市智能交通系统的重要组成部分.为实现客流量的准确预测,首先采用变分模态分解(VMD)将时序客流数据分解成不同时间尺度下的本征模态函数(IMF),降低数据噪声对客流预测模型的影响,再结合长短时记忆神经网络(LSTM)进行预测,提出VMD-LSTM预测模型.采集明尼苏达州州际轨道交通客流数据对模型进行验证.结...

Rail Transit Passenger Flow Prediction Model Based on VMD-LSTM

Passenger flow prediction is an important part of urban intelligent transportation system. In order to realize accurate prediction of passenger flow, variational mode decomposition was adopted to decompose the time series into intrinsic mode function in different time scales, the long short-term memory neural network of deep learning was used to predict, and the VMD-LSTM prediction model was proposed. Data of minnesota interstate subway passenger flow were collected to validate the model. The results show that compared with the traditional LSTM prediction model, the average absolute percentage error and the root mean square error decreases by 8.38% and 256.99% respectively after improved by VMD, the prediction accuracy and robustness of LSTM neural network are improved effectively.