## 2 Literature Review
### Demand Forecasting Methodology  
In the realm of logistics demand forecasting, early approaches relied on grey model forecasting, time series analysis, and linear regression methods, often treating the volume of transported goods as the primary indicator for logistics demand prediction. These methods typically operated within relatively narrow data dimensions. However, with the emergence of artificial intelligence, machine learning, and related technologies, researchers have increasingly gravitated towards the utilization of neural networks and the integration of these with traditional methods to forecast regional logistics demand. This trend has involved the incorporation of more comprehensive indicator data, leading to continual improvements in prediction accuracy. Nevertheless, it is important to note that neural network methods present unique challenges such as overfitting and gradient disappearance. [PERSON] et al. [13] presented a method to forecast the transportation demands of large quantities of goods by leveraging the production and transport coefficient. They also outlined a strategy for adjusting the transportation infrastructure based on the evolving trends of these goods. Through a case study in Heilongjiang Province, the authors took into account various factors such as fertilizer usage, rural electricity consumption, total power of agricultural machinery, and the area of grain crops in their prediction of grain output. To achieve this, they developed GM(1, 1) and GM(1, 1)-MLP hybrid neural network models, which were then validated using real-world data. [PERSON] et al. [14] establish a regional logistics demand prediction indicator system. The GM(1, 1) grey prediction model with a weakened Boolean operator was used to predict the regional rural logistics demand. [PERSON] et al. [11] analyzed the intrinsic correlation between potential factors and the regional cold chain logistics demand of agricultural products and then established a hierarchical cumulative grey model GM(1, N) to predict the cold chain logistics demand of the study area in the next five years. [PERSON] et al. [15] constructed a regional logistics demand forecasting indicator system, classified 12 indicators into three levels, analyzed the coupling strength between the indicators at the same level and the logistics demand, reduced the dimensionality of the indicators at the three levels by using principal component analysis (PCA), input the reduced dimensionality of the three principal components into BP neural network for model training, and then finally predicted the logistics demand of the study area in the next three years. Based on analyzing the factors affecting logistics demand, [PERSON] et al. [16] took into account that the logistics demand data series had the characteristics of non-linearity and small-sample modeling and introduced the ant colony algorithm to optimize the penalty parameters \"c\" and \"g\" of the radial basis function in the support vector machine in the process of modeling from the perspective of the urban freight transport volume. The optimized support vector machine model is used to forecast the logistics demand in the study area. In a study by [PERSON] [17], the suggestion was made to integrate adaptive learning rates and momentum terms into the gradient descent technique used in BP neural networks to improve the speed of convergence. Furthermore, adjustments were made to the network architecture to guarantee the stability of the model. [PERSON] et al. [18] designed a multi-layer perceptron (MLP) neural network model based on the simulation platform of the MLP neural network according to the matrix-vector multiplication operation and weight-updating operation and proposed a method to improve the MLP neural network by using the deep learning training mechanism on the basis of the standard MLP neural network, choosing the RBF function as the kernel function of the model and optimizing the parameter combination by using the PSO algorithm.