## 2 Related Work and Research  
With the arrival of the 5G era and the breakthrough of key technologies, such as sensors, intelligent transportation has become a reality. Most of the experimental data shows that in the 5G era, systems can transmit data every five minutes in cloudy conditions [11]. How to deal with the data collected by sensors in real time, and improve the accuracy of the algorithms, have become urgent problems to be solved. From parking data, the large amount of historical information recorded by infrastructure sensors provide information guarantees for parking occupancy prediction. At the same time, the existing big data processing capacity has been greatly improved, and the rapid development of parking guidance system will make it a reality. A wide range of prediction models have been developed in the literature and used to estimate current parking occupancies and parking demand in transportation networks [12]. The prediction of parking occupancy availability is closely related to traffic conditions and estimated arrival time based on predicted traffic flows and travel times. Currently, the statistical theory used to predict the count (cnt) of parking spaces in the interval of a certain time is based on the Poisson distribution, such as the multiple regression model [13; 14], Markov model [15], Kalman filter [16], and auto-regressive integrated moving average models (ARIMA) [17]. [PERSON] and [PERSON] [14] studied a vector regressive model at the estimated arrival time of the driver with the highest probability using real-time parking data. [PERSON] et al. [18] combined the wavelet neural network model and wavelet transform to estimate the short-term prediction of APS. [PERSON] et al. [19] deduced a general average field relationship using the mechanism of statistical physics and graph theory, and took the parking search time as a function of parking space occupancy. [PERSON] et al. [20] smoothed the original parking data using support vector regressions (SVR), and then trained a multidimensional SVR model, which represents the availability of parking space and is suitable for parking predictions. [PERSON] et al. [21] proposed the number of vacant parking spaces in a specific period of time based on SVR with the fruit fly optimization algorithm (FOA). [PERSON] et al. [22] proposed a discrete-time Markov model to present a model framework to estimate parking occupancy from actual occupancy data. [PERSON] et al. [23] studied a multiple linear regression model with an autoregressive moving average model including the characteristics of the parking lots and external environmental factors. [PERSON] et al. [24] proposed a new multi-step forecasting model, which has a more accurate performance and better learning ability in short-term prediction. [PERSON] et al. [25] proposed an integrated path analysis discrete-choice model with the car ownership and travel distance as model variables using Mplus software. [PERSON] et al. [26] presented an auto-regressive integrated moving average (ARIMA) model for medium-term parking demand-forecasting. The simulation results show the high accuracy of the proposed method for parking occupancy prediction. From the above reviews, multivariate or multi-scale regression models can simply and quickly predict parking occupancy. However, the model parameters are affected by multi-level and multi-factor interaction, and the weight of each parameter in the model cannot be accurately measured. Moreover, environmental factors, such as the convenience of surrounding traffic and weather factors, seriously affect parking prediction, thus leading to inaccuracies. In the last decade, models of deep neural networks have become more and more popular in the area of traffic modeling and prediction. Compared with traditional algorithms, various machine learning and deep learning algorithms have greatly improved the accuracy of short-term traffic state prediction and data processing scales [27].