Prediction of remaining parking spaces based on EMD-LSTM-BiLSTM neural network

作     者：Changxi Ma Xiaoting Huang Ke Wang Yongpeng Zhao 

作者机构：School of Traffic and TransportationLanzhou Jiaotong University 

出 版 物：《Journal of Traffic and Transportation Engineering(English Edition)》 (交通运输工程学报(英文版))

年 卷 期：2025年第1期

页      面：201-213页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 082303[工学-交通运输规划与管理] 0835[工学-软件工程] 082302[工学-交通信息工程及控制] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：supported by the Gansu Provincial Science and Technology Major Special Project-Enterprise Innovation Consortium Project (No. 22ZD6GA010) National Natural Science Foundation of China (No. 52062027) Industry Support Plan Project from Department of Education of Gansu Province (No.2024CYZC-28) Key Research and Development Project of Gansu Province (No.22YF7GA142) 

摘      要：The traffic congestion caused by the mismatch between the demand of car owners and the supply of parking spaces has become one of the severe traffic problems in various places. It is important to predict the remaining parking space which can not only help the driver to plan their trips reasonably but also reduce the pressure on urban road traffic. To reduce the stochastic fluctuations of complex data and improve the predictability of parking spaces, a hybrid prediction model EMD-LSTM-BiLSTM is proposed, which is combined the adaptive ability of empirical mode decomposition(EMD) to process time series data and the advantage of long short-term memory network(LSTM) and bidirectional long short-term memory network(BiLSTM) to solve long-range dependencies. First, the EMD algorithm is employed to decompose the components of different scales in the time series and generate a series of mode functions with the same characteristic scale. Next, the construction,training, and prediction of the LSTM-BiLSTM neural network are completed in the deep learning framework of Keras. BiLSTM was built for proposing the bi-directional temporal features of the sequences, and LSTM was responsible for learning the output features,which effectively avoids large prediction errors. Finally, the performance of the model is verified by the actual parking data sets of different parking lots for parking space prediction. The proposed hybrid model is compared with a variety of current mainstream deep learning algorithms, and the effectiveness of the EMD-LSTM-BiLSTM method is *** results may provide some potential insights for parking prediction.