Optimisation study of express delivery quantity prediction based on ARIMA model and grid search algorithm

The traditional logistics model is difficult to adapt to personalised and frequent express demand, and urban development and road network restrictions increase the difficulty of path planning and cost control. This study is dedicated to optimising logistics transport, proposing intelligent path planning and dynamic demand forecasting. By accurately predicting the number of urban express delivery, resources are deployed in advance to avoid insufficient or excess capacity and improve the quality of express delivery service. Finding the optimal cost solution under path constraints improves logistics efficiency and achieves more economical and sustainable logistics transport. Apply ARIMA model combined with grid search algorithm to forecast the number of express delivery with actual data.