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2025 , Vol. 30 >Issue 5: 962 - 975

DOI: https://doi.org/10.1007/s12204-023-2672-1

Computing & Computer Technologies

Predicting Parking Spaces Using CEEMDAN and GRU

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  • 1. School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China; 2. Gansu Longyuan Information Technology Co., Ltd., Lanzhou 730030, China

Received date: 2023-04-18

  Accepted date: 2023-06-19

  Online published: 2023-12-01

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Abstract

Accurate prediction of parking spaces plays a crucial role in maximizing the efficiency of parking resources and optimizing traffic conditions. However, the majority of earlier research has used models based on past parking data or the plethora of variables that influence parking prediction, which not only makes the data more complicated and costs more time to run but can also lead to poor model fits. To solve this problem, a hybrid parking prediction model combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and gated recurrent unit (GRU) model is proposed to predict the number of parking spaces. In this model, CEEMDAN has the ability to gradually break down time series fluctuations or trends at various scales, producing a sequence of intrinsic mode functions (IMF) with various characteristic scales. Then, by keeping the majority of the original data’s content, removing superfluous information, and enhancing predicted response time, principal component analysis (PCA) decreases the dimensionality of the IMF series. Subsequently, the high-level abstract characteristics are entered into the GRU network, and the network is built, tested, and predicted based on the deep learning framework Keras. The validity of the presented model is verified by making use of real parking datasets from two three-dimensional parking lots. The test results reveal that the model outperforms the baseline model’s predictive accuracy, i.e., a lower testing error. The real parking time series are most closely modeled by the CEEMDAN-PCA-GRU model. As a result, the method is superior to existing models for parking prediction.

Cite this article

MA Changxi, HUANG Xiaoting, MENG Wei . Predicting Parking Spaces Using CEEMDAN and GRU[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(5) : 962 -975 . DOI: 10.1007/s12204-023-2672-1

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