## 4 Experimental Results and Analyses
### Analysis of BP Neural Network Prediction Results
The relevant parameters regarding the genetic algorithm are set as follows, with the chromosome length set as C.  
\[C=I*H+H+H*O+O \tag{16}\]  
\(I\) is the number of input layer neurons, \(H\) is the number of hidden layer neurons, and \(O\) is the number of output layer neurons. The number of evolutionary generations is 350, the population size is 50, the crossover probability is 0.58, and the mutation probability is 0.15. The iterative graph of genetic algorithm adaptation is shown in Figure 10.  
After module and parameter optimization of the BP neural network model, we conducted a forecasting comparison based on the forecasting methods used in the relevant literature reviewed, on top of the regional logistics express impact indicator system established in this paper. Multi-layer perceptron (MLP), ridge regression, lasso regression, and GM (1, 1) models were used for comparison. The comparison results are shown in Table 10 and Figure 11. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Indicators** & **BPNN** & **GA-BPNN** \\ \hline SSE & 0.02034 & 0.02306 \\ MAE & 0.05616 & 0.05291 \\ MSE & 0.00407 & 0.00461 \\ RMSE & 0.06378 & 0.06792 \\ MAPE & 5.53\% & 4.08\% \\ Accuracy Rate & 94.47\% & 95.92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation of BPNN and GA-BPNN prediction results. Figure 10: Genetic optimization algorithm adaptation iterative graph. Firstly, in the comparison, it can be seen that the SSE, MAE, MSE, RMSE, and MAPE of the prediction results of the GM(1, 1) model are higher compared to the other four models, probably because GM(1, 1) is more suitable for predicting the data objects with smoother trends, and it is prone to larger errors in predicting the data with larger fluctuations. Secondly, ridge regression and lasso regression models, as two commonly used linear regression models, perform very close to each other in predicting nonlinear systematic data but underperform compared to MLP and improved GA-BP, which are nonlinear models. After the above comparison, the improved BP neural network, after using a genetic algorithm to optimize the initial weights and thresholds of the network, has higher prediction accuracy and stability and can be used as a prediction model for the future demand of logistics and express delivery in Chongqing. After that, this paper uses the time prediction model to predict Factor_1, Factor_2, and Factor_3 and inputs the predicted values of the next five years into the trained neural network. This approach aims to predict the logistics and express delivery demand for Chongqing Municipality from 2022 to 2026. The experimental results indicate that the volume of express delivery services in Chongqing Municipality is projected to reach 2,171,642,700 items by 2026, a remarkable increase of 122% compared to 979,000,000 items in 2021. The findings are illustrated in Table 11 and Figure 12.  
In addition, at the time of data collection and processing in this paper, the Chongqing Municipal Bureau of Statistics had only published the statistical yearbook for 2021. However, in January 2024, the statistical yearbook of 2022 was published, in which the volume of express delivery business in 2022 was 1,091,768,000 items, which is only 3.7% different from the predicted value of 2022 in this paper. This directly verifies the accuracy of the model in this paper.