## 5 Discussion  
In most cases, good prediction results can be obtained using the fast model estimation in this study. The prediction accuracy may be higher or lower in a few exceptional cases. We can obtain some inspiration by observing and analyzing some cases. According to different types of prediction result distribution diagrams, they can be roughly divided into four categories, namely \"close fit,\" \"straight line,\" \"horizontal line,\" and \"mass of blocks.\" Close fit:  
Usually, such cases are essential parking lots that government units pay more attention to, which may be located in vital business districts (case 78, Figure 6), stations (case 483, Figure 7), markets (cases 593 and 487, Figures 8 and 9), etc., and are accompanied by good management or unlimited daily charges. Due to this, these parking lots are less likely to experience abnormal communication or long-term parking of vehicles, and the variability of each period during the day is also moderate. As the changes align with the usual way of change, more accurate forecast results can be obtained. Figure 6: Prediction Results of Case 78. Figure 7: Prediction Results of Case 483. Straight line:  
Usually, this means that the parking lot may be disconnected at some point, and the number of disconnections will be fixed. In this case, the actual number will permanently be fixed since the number of available parking spaces will not change. However, since the prediction will still be estimated according to the normal situation, the calculated result will have an extensive variation range, so it is displayed as a straight line (case 299, Figure 10) on the graph. In addition, if the parking lot is only closed for a certain period in a day, and the number at the time of closing is a fixed value, a straight line still appears on the graph, but the points cluster more closely (case 585, Figure 11). Figure 8: Prediction Results of Case 593. Figure 9: Prediction Results of Case 487. Mass of blocks:  
Usually, this means that the parking lot has multiple time points in one day when the parking quantity changes significantly, and the parking quantity before this point falls within the daily stable range. Most of these kinds of parking lots are open 24 h. With the change in neighboring business districts, there may be an abnormal number of vehicles entering and leaving at various times throughout the day. The rectangular block below the line indicates underestimation (the real value is greater than the predicted value), which usually means that this period is the vehicle entry period. The rectangular block above the line indicates overestimation (the real value is less than the predicted value), usually meaning that this period is the vehicle departure period (cases 73, 15, Figures 15 and 16). Figure 16: Prediction Results of Case 15. Figure 14: Prediction Results of Case 59. Figure 15: Prediction Results of Case 73. From the above cases, we can find that abnormal data caused by communication or business control will inevitably exist in the parking data we want to observe. Such dirty data will inevitably have a significant impact on data modeling. Even with deep learning processing, the effect of dirty data cannot be eliminated. It has been observed that most of the previous studies used simple parking lot data for data verification so that acceptable prediction results could be obtained. Restricted by resource constraints, it is impossible to continuously collect and calculate data for a large number of parking lots in a short period. Therefore, if the collected data are to be modeled and estimated in a short time, the frequency of data collection should not be too dense, and the computing performance should not be too heavy. Generally speaking, in practice, we cannot perform real-time data collection and complex modeling and prediction for a large number of parking lots simultaneously because they will require a lot of computing power to support. We also used neural networks to compare predictions and observed Pearson correlation coefficients.