# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 1 Introduction  
With the rapid development of road transportation services in the remote regions of northwest China, the volume of road transport has also shown a significant upward trend. Vehicle transport capacity plays a crucial role in the logistics system. In an environment characterized by limited resources and diverse demands, accurately forecasting vehicle supply becomes particularly important. Therefore, enhancing vehicle supply forecasting capabilities will have a profound impact on optimizing road transportation operations and promoting economic development.  
Logistics and transport supply capacity refers to the range of resources provided by logistics and transport services, including transport equipment, route facilities, service providers, and other transport resources. As the logistics transportation industry evolves, the number of transportation vehicles serves as a key quantitative indicator of logistics transportation supply capacity [1]. The adequate and judicious allocation of vehicles significantly influences transportation capacity, efficiency, and service quality. Thoughtful planning and management of vehicle numbers can ensure a balance between supply anddemand, relieve transport congestion and delays, and enhance the efficiency and dependability of logistics transport. Additionally, logistics supply plays a crucial role in the supply chain, focusing on logistics transport and distribution services. It involves coordination and collaboration with other supply chain components to optimize the seamless functioning of each logistics process, elevate supply chain efficiency and service levels, and drive the high-quality development of regional economies [2]. The management of logistics supply is considered a pivotal strategy for cost reduction, efficiency enhancement, and competitiveness. It is often referred to as the \"third source of profit\" for logistics and transport enterprises [3]. However, due to excessive costs, structural mismatches, outdated service quality, and lack of innovation, effective reforms on the supply side are necessary. Key to these reforms is the forecasting of logistics and transport supply, which aids in estimating future transport demand and facilitating the effective planning and allocation of transport resources.  
In summary, the main contributions of this paper include the following three points:  
1. Multidimensional feature engineering based on historical data has been established. This is used to extract and select features closely related to the model's prediction task, enhance the interpretability of label data, and improve the model's generalization ability and predictive performance.
2. A new method for forecasting logistic transportation supply has been introduced. It is based on the enhanced Informer model. The original Informer model did not account for sequential spatial features, so a spatiotemporal convolutional network has been developed to address this. Additionally, an LSTM model has been incorporated to capture the long- and short-term dependencies in the temporal data.
3. In-depth experimental evaluations demonstrate that this method is suitable for long-sequence time series forecasting, exhibiting high predictive accuracy. It shows better capture capabilities for local changes and fluctuations, resulting in overall improved predictive performance.  
The remaining parts of the article are as follows: the second part discusses the related work in the vehicle supply forecasting field; the third part introduces the model design, outlining the structure of the improved Informer model proposed; and the fourth part covers the design of the experiments and the analysis of the experimental results. Finally, the fifth part summarizes the entire text and provides an outlook on the next steps for this work.