## 5 Conclusions and Prospect  
The generation of logistics express demand is determined by comprehensive environmental factors, which is precisely why it is necessary to select impact indicators from various aspects such as regional economy, industrial environment, and infrastructure construction. The significance of its prediction is not only the assessment of the development of the logistics industry but also the outlook for the development of socioeconomic industries. The research in this paper is mainly for the use of grey correlation analysis indicators and the establishment of regional logistics express impact factor indicator system. Then, according to the characteristics of the processed indicator data, the adapted neural network activation function and training algorithm are selected, and finally, according to the defects in the BP neural network prediction experiment, the GA-BP algorithm is designed for optimization. After comparing the experimental results, GA-BPNN has higher prediction accuracy, and its predicted logistics and express delivery demand in Chongqing in 2026 is 2,171,642,700 items.  
From a practical point of view, the research in this paper can provide data support for local government's policy-making and logistics planning and can model ideas for enterprises' production planning.  
Firstly, according to the forecast results of this paper, by 2026, the logistics express volume in Chongqing will increase by 118% compared to 2022 under such a development trend. Chongqing government should pay attention to the future planning of regional logistics to avoid duplication of construction due to unreasonable planning of logistics infrastructure. Policies should strongly promote the innovation of logistics and distribution modes (e.g., logistics alliance, crowdsourcing logistics, etc.) to cope with the significant growth of logistics demand in the future.  
Figure 12: Prediction result diagram of express demand.  
Secondly, the growth of logistics demand has obvious seasonality. In the next few years, logistics demand may not be a smooth rise, but it will occur in some time nodes of large growth. Logistics enterprises should follow suit, such as shopping carnivals, large international activities before the logistics demand prediction and assessment, in order to avoid too many orders and insufficient capacity caused by the malfunctioning of the operation. Therefore, the forecasting model in this paper provides ideas for logistics enterprises to forecast logistics express demand.  
The limitations of the study mainly lie in the limitations of the research object and the limitations of the model parameters. The development of the logistics industry includes comprehensive factors such as national policies and the international economic situation. Therefore, the scope of the study can be expanded in future research to include the consideration of policies and economic situations to analyze and evaluate from a more comprehensive level.  
Additionally, this paper mainly focuses on optimizing and improving the activation function and training algorithm of the BP neural network, but whether other neural network models can be improved in the same way is worthy of further research.  
Methodology, S.W.; formal analysis, T.X.; data curation, S.W.; writing--original draft, S.W.; writing--review and editing, F.M. and C.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.