## 2 Related Work and Research
Taking the street parking occupancy rate and vehicle departure probability in a specific area as the prediction performance indicators, [PERSON] et al. [28] used the long short-term memory (LSTM) model to predict the parking occupancy rate. [PERSON] et al. [29] proposed a hybrid deep learning framework to intelligently predict the availability area of vacant parking spaces in the short term (within 30 min) and long term (more than 30 min). The hybrid deep learning framework can obtain considerably high accuracy in both short-term and long-term predictions. [PERSON] et al. [30] proposed a novel periodic weather-influenced LSTM model, which successfully predicts the parking occupancy rate according to actual data, weather feature, environment condition, and weekdays. [PERSON] et al. [31] discussed the effect of weather features on parking behavior and found that the random forest model can make the most accurate parking behavior prediction. However, the above two papers obtained data at an interval of one hour, which failed to consider the need of real-time parking. [PERSON] et al. [32] constructed an optimized LSTM model based on LSTM and a bi-directional LSTM network with a better prediction precision and training speed. [PERSON] et al. [33] presented a travel demand management system including parking demand, parking occupancy, and future changes. However, this model did not consider the demand of actual parking and limited on-street parking application. [PERSON] [34] proposed a forecasting model of short-term unoccupied parking space using the method of combining wavelet transformation and an extreme learning machine. The results of the prediction example show that the method shortens the training time and improves the prediction results. [PERSON] et al. [35] used long short-term memory (LSTM) to model the temporal closeness and period and the current general factors. [PERSON] et al. [36] proposed a semi-supervised hierarchical recurrent graph neural network for predicting city-wide parking availability within the city from the spatial and temporal domains. [PERSON] et al. [37] applied the fuzzy c-means clustering method to identify the traffic state of the roundabouts according to the headway changes in the inner and outer ring-road traffic flow to provide the time basis of signal control for traffic managers. [PERSON] et al. [38] presented a game model between a parking guidance system and users based on [PERSON]'s multi-round game theory, including the profit function, game tree, and equilibrium conditions. However, the above study did not consider the influence of weather factors and surrounding condition [30]. Meanwhile, some researchers used other deep learning methods for the prediction of parking occupancy [39; 40]. [PERSON] et al. [41] presented a parking occupancy prediction mechanism with feature datasets with selected parameters to illustrate these features, and analyz the comparative advantages of deep learning methods for parking occupancy prediction. [PERSON] et al. [42] proposed a parking guidance model with a solution algorithm, and verified the effectiveness of the model and algorithm through simulation. [PERSON] [43] gave a weighted first-order local-region method to forecast unoccupied parking space based on the actual data and the chaotic time series. Accurate and reliable short-term parking occupancy prediction are the most important factors of parking guidance and information systems. Recently, the most developed deep learning methods, such as recurrent neural network (RNN) and long-short term memory (LSTM), have shown great advantage in other fields' prediction. [PERSON] et al. [30] presented a long-term car parking behavior prediction with a periodic weather-aware LSTM model with one hour, two hour, and three hour predictions.