Short-time prediction of parking demand based on LSTM neural network model

In order to study the problem of short term prediction of parking demand in the region, long short-term memory (LSTM) neural network model has been used to predict the corresponding parking demand at subsequent time points based on the historical parking demand changes. Using the historical order data of on-street parking in Guilin, the data were organized into time series of 15-minute periods, and processed by noise reduction using wavelet threshold denoising method to train and test the model. The experimental results show that the prediction accuracy of the LSTM model is higher (MSE=11.588, RMSE=3.404, MAE=2.079, R2=0.945) compared with the traditional back propagation (BP) and wavelet neural network (WNN) neural network algorithm, and the prediction results are more similar to the real results. It can be seen that the use of LSTM recurrent neural network is effective and feasible for short time forecasting of berth demand in the region.