关键词: 交通运输工程；静态交通；停车泊位预测；深度学习；LSTM神经网络

Abstract: Aiming at the problem that the traditional effective parking space prediction method could not describe the time correlation before and after the parking, LSTM neural network based on deep learning was given to improve traditional prediction methods, and a prediction model of effective parking space based on LSTM was proposed to analyze and predict different types of parking areas. Based on the established model, the temporal and spatial characteristics of effective parking space prediction were comprehensively considered, the historical parking data of multiple adjacent parking lots in the target area were selected to form a data set. The comparison model of effective parking space prediction was constructed to test the prediction accuracy of the model. The results show that in the prediction of effective parking spaces in different types of parking areas, the prediction results of the proposed model are in good agreement with the real value, and its prediction accuracy is higher than that of BP prediction model and ARIMA prediction model. It is indicated that the proposed model is reliable and effective in the effective parking space prediction.

Key words: transportation engineering; static traffic; parking space prediction; deep learning; LSTM neural network