# Prediction of Vacant Parking Spaces in Multiple Parking Lots: A DWT-ConvGRU-BRC Model
###### Abstract  
For cities, the problem of \"difficult parking and chaotic parking\" increases carbon emissions and reduces quality of life. Accurately and efficiently predicting the availability of vacant parking spaces (VPSs) can help motorists reduce the time spent looking for a parking space and reduce greenhouse gas pollution. This paper proposes a deep learning model called DWT-ConvGRU-BRC to predict the future availability of VPSs in multiple parking lots. The model first uses a discrete wavelet transform (DWT) to denoise the historical parking data and then extracts the temporal correlation of the parking lots themselves and the spatial correlation between different parking lots using a convolutional gated recurrent unit network (ConvGRU) while using a BN-ReLU-Conv (\(1\times 1\)) module to further improve the propagation and reuse of features in the prediction process. In addition, the model uses availability, temperature, humidity, wind speed, weekdays, and weekends as inputs to improve the accuracy of the forecasts. The model performance is evaluated through a case study of 11 parking lots in Santa Monica. The DWT-ConvGRU-BRC model outperforms the LSTM and GRU baseline methods, with an average testing MAPE of 2.12% when predicting multiple parking lot occupancies over the subsequent 60 min.  
parking prediction; deep learning; discrete wavelet transform; convolutional gated recurrent unit network; multiple parking lots +
Footnote †: journal: Applied  
can assist traffic planning and management to reduce energy consumption and traffic congestion [2].  
In this paper, we propose a DWT-ConvGRU-BRC model. This model consists of a discrete wavelet transform (DWT) [13], convolutional GRU networks (ConvGRUs) [14], a two-layer linear network, and a composite function of three consecutive operations, i.e., batch normalization (BN), rectified linear activation (ReLU), and a \(1\times 1\) convolution (Conv), denoted BRC. First, we use the DWT to denoise the VPS data. Noise reduction before forecasting can eliminate the volatility of the VPS data themselves. Then, we use a deep learning-based prediction model that leverages ConvGRUs and a two-layer linear network to incorporate the spatial-temporal features of multiple data sources acquired in networks. Finally, the propagation and reuse of features in the prediction process are further improved using the BRC composite function.  
This paper contributes to the literature in the following ways:  
* We propose a deep learning-based parking space prediction model from the perspective of multiple parking lots. The model considers the processing of parking noise data as well as the spatial correlation of multiple parking lots and the temporal correlation of the parking lots themselves and uses a variety of factors, including parking lot occupancy, temperature, humidity, wind speed, weekdays, and holidays, to predict the number of available VPSs.
* Our proposed DWT-ConvGRU-BRC model can simultaneously predict the number of available parking spaces in multiple parking lots. Specifically, a ConvGRU is used to capture the spatial-temporal features of multiple parking lots, a two-layer linear layer is used to extract external influences, and BRC is used to further improve the propagation and reuse of features in the prediction process.
* The performance of the method is evaluated with a case study in the Santa Monica area. According to the results, the model outperforms other baseline methods, including LSTM, GRU, ConvGRU, and dConvLSTM-DCN models. Moreover, the results prove the improvement in prediction accuracy from the DWT and the effectiveness of incorporating weekday, vacation, and weather features into parking lot occupancy predictions.  
The rest of this paper is organized as follows: Section 2 summarizes the literature review. Section 3 describes the detailed DWT-ConvGRU-BRC prediction model. Section 4 presents the results and analysis of the comparison experiments. Finally, we provide our conclusions and discuss possible future work in Section 5.