###### Abstract
Currently, the problems of uneven spatial development, inadequate construction of logistics infrastructure, and mismatch between urban transportation capacity and freight volume growth caused by unreasonable logistics planning have become more and more prominent, resulting in high logistics costs and difficulties in improving logistics efficiency. Logistics planning infrastructure such as logistics hubs, logistics centers, logistics distribution centers, and other investments in fixed assets having a long-term nature, if there is no logistics demand data in line with the law of future economic development of the region as the planning support, will lead to the original logistics service system and the region's future logistics demand not matching, the status of the logistics infrastructure repeatedly building logistics infrastructure, and an imbalance between logistics supply and demand and other increases in logistics costs. In this paper, we review a substantial body of literature to employ grey correlation analysis for establishing a logistics express demand impact indicator system in Chongqing. Subsequently, we normalize the extracted indicator data and apply a BP neural network, utilizing Bayesian regularization as the training method and the LeCun Tanh activation function to predict the demand for logistics express services in Chongqing over the next five years. To address the issue of exceeding the maximum number of iterations during training, we design a genetic algorithm to optimize the initial weights and thresholds of the BP neural network. The main research contributions of this paper are as follows:  
1. Various influencing factors in the literature were comprehensively considered to establish the logistics express impact indicator system in Chongqing. Additionally, tertiary industry and related indicators were added to link the panning of the logistics express industry with the development of the tertiary industry. The strong correlation between the tertiary industry and the logistics express industry is verified in this paper. 2. When using short series data such as indicators of logistics demand influencing factors as input data for neural networks, it should be considered whether the training algorithm of the neural network will lead to overfitting of the network, and the BR training algorithm used in this paper can to a certain extent avoid the problem of overfitting when predicting short series data. 3. Selecting the appropriate activation function for the hidden layer of a neural network can improve the accuracy of predictions based on data features. In this research, the LeCun Tanh function was successfully applied to counteract the issue of gradient disappearance in the training of backpropagation neural networks. 4. In this paper, a kind of genetic algorithm will be developed to enhance the stability of the neural network structure by optimizing the initial weights and thresholds when extended iterations lead to unstable prediction accuracy. This process is intended to speed up iterations and improve the stability of the network. The following is the arrangement for the rest of the paper: Section 2 provides an overview and summary of the relevant literature on the logistics demand impact indicator system and logistics demand forecasting. Section 3 describes the methods and models used in this study and highlights the improved parts of the paper and the research line. Section 4 compares and analyses the application of the previously introduced methods and models in the example. The optimal model is also chosen to forecast the logistics express demand in Chongqing. Section 5 summarizes the research in this paper and gives an outlook for future research given the limitations of this paper.