## 4 Results and Discussion
### Comparison of Evaluation Index  
Using MAE and RMSE as evaluation indicators, the improved LSTM with weather influence feature, simple LSTM, MLR, and SVR algorithms for all parking spaces are compared and analyzed. This paper predicts the effects of each parking lot, as shown in Figure 11. The eight block parking lots (PL1-PL8) in Figure 11 shows that the improved LSTM with weather influence feature algorithm has the better overall prediction effect. The comparison with simple LSTM and improved LSTM is shown in Table 4. From Table 4 we can see that the mean prediction accuracy of the improved LSTM model is higher than that of the simple LSTM model, indicating that the influence of weather on the parking prediction rate should be considered. The parking lot prediction effect is relatively better in MAE in intervals of 10 min, while the parking lot prediction effect needs to be improved in intervals of 30 min according to the MAE index; especially in PL1 and PL3, the MAE prediction error at the interval of 10 min is smaller. In the interval of 10 min, 20 min, and 30 min, the least prediction error of RMSE is still the LSTM with the weather influence feature algorithm proposed in this paper.  
Figure 10: Evaluation results of PL5 using four models for 10 min, 20 min, and 30 min.  
Figure 11: Comparison of evaluation index for PL1 to PL8.  
For the overall prediction effect of eight parking lots, the prediction results of parking lots in all blocks were applied in this paper, as shown in Figure 12. From Figure 12, the prediction results of MLR in the intervals of 10 min, 20 min, and 30 min are the worst. The main reason is that the MLR solving time series problem needs more stable data, while the data in this paper fluctuates relatively. Because the main feature of the prediction of parking occupancy rate is time series, LSTM performs nonlinear conversion through activation function. As it is a nonlinear model, LSTM captures the complex dependencies between sequences and is not limited to a single logic rule. It can find the expected prediction results with low requirements for data stability. In addition, the evaluation index of simple LSTM is better than MLR and MVR, but worse than that of improved LSTM. This proves that it is very important to consider the features of weather influence. The MAE of LSTM with weather influence feature in 10 min, 20 min and 30 min intervals follows 3.07, 5.27, and 8.48, and the RMSE is 9.55, 10.84, and 13.23, respectively. The average MAE of classic LSTM in 10 min, 20 min, and 30 min is 5.37, 8.61, and 12.36, and the RMSE is 9.76, 14.87, and 18.64. The average MAE of SVR prediction in 10 min, 20 min, and 30 min intervals follows 6.52, 12.75, and 16.30, and the RMSE is 11.54, 17.77, and 20.61, respectively. LSTM with the added weather influence feature model is obviously more accurate than simple LSTM and SVR model, which proves that the improved algorithm in this paper has the better effect when compared to other methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Time Interval** & **Mean Accuracy** \\ \hline Simple LSTM & 10 min & 60.31\% \\  & 20 min & 65.43\% \\  & 30 min & 66.10\% \\ \hline  & 10 min & 77.08\% \\ Improved LSTM & 20 min & 78.28\% \\  & 30 min & 77.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison with simple LSTM and improved LSTM.  
Figure 12: The average evaluation results of all parking lots for 10 min, 20 min, and 30 min.