# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 5 Conclusions  
In the context of the road transportation environment in northwest China, vehicle supply forecasting faces several challenges. This region is characterized by its vast geographic space and relatively low population density, resulting in an efficient and smooth transportation network. However, the long sequences of supply volume data, numerous influencing factors, and significant spatiotemporal evolution patterns contribute to low accuracy in traditional supply forecasting methods. Consequently, logistics companies urgently require improved forecasting methods to better manage vehicle scheduling and resource allocation.  
This paper examines the logistics supply data from a certain consolidated freight logistics company, addressing issues related to long sequences of supply volume data and the multitude of influencing factors, which hinder prediction accuracy. This paper proposes a logistics supply forecasting method based on an enhanced Informer model. Building upon the Informer model, we design a multidimensional feature engineering approach and a spatiotemporal convolutional network while integrating an LSTM module to improve prediction performance.  
According to the experimental results, the proposed enhanced Informer model demonstrates higher prediction accuracy for logistics supply volume, effectively reducing forecasting errors and significantly capturing local variation features. This improvement in fitting quality is crucial for enhancing supply-demand matching, vehicle scheduling, and decision-making in modern logistics transportation systems. In future work, we aim to incorporate a more diverse range of data sources to enhance the model's overall predictive capability. Additionally, we will explore the development of adaptive supply forecasting strategies tailored to different traffic conditions.  
Conceptualization, D.G. and Y.Q.; methodology, D.G., P.J. and Y.Q.; software, D.G.; validation, D.G., P.J. and Y.Q.; formal analysis, D.G. and P.J.; investigation, X.Z., J.Z. and Y.Q.; resources, D.G. and Y.Q.; data curation, D.G., P.J. and Y.Q.; writing--original draft preparation, D.G.; writing--review and editing, D.G., P.J. and Y.Q.; supervision, D.G. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Key Research and Development Program Project of the Department of Science and Technology of the Autonomous Region (grant no. 2022B01015) and the Science and Technology Program Project of the Bureau of Ecology, Environment and Industrial Development of Ganquanbao Economic and Technological Development Zone (Industrial Zone) (grant no. GKJ2023 XTWL04).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the Xinjiang Hualing Logistics & Distribution Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.