Comparative Analysis of Traditional and Advanced AI Methods in Logistics and Transportation: A Focus on Sorting Center Operations

Article discusses the prediction and planning of goods logistics using two different models, LSTM and ARIMA, for data time series. After clarifying the basis and principles of the two models, the data is input into testing and debugging to obtain the final prediction results, and the correctness and accuracy are long-term or multi-factor data, the algorithm model based on the LSTM model is more effective and accurate than the short-term multi-frequency algorithm do not perform well for nonlinear and complex time series data. Therefore, in practical dealing with some data with non-absolute long-term and non-absolute short-term, its better to use a hybrid mode, which can combine the output of ARIMA and LSTM to combine the advantages of both to predict the error. Through this comparison, the advantages and disadvantages of different models are obtained, and an artificial intelligence method or reference for logistics prediction and planning is provided for professionals in the industry.