Author(s)

Yuhao Song ¹, Ziyan Lu ¹

Affiliation(s)

¹ College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China

Corresponding Author

Yuhao Song

ABSTRACT

With the rapid development of e-commerce and global trade, logistics sorting centers are faced with increasing cargo volume volatility and complexity, and traditional cargo volume prediction methods are difficult to cope with such changes. In this study, a cargo volume prediction model based on Long Short-Term Memory (LSTM) neural network is proposed by utilizing the logistics data from Jinan City's Caijiao Post. First, the historical data are preprocessed, including missing value filling and outlier processing. Then, the daily cargo volume of each sorting center is predicted based on the LSTM model, and the results show that the model performs well in capturing the long-term dependence in the time series data, and the deviation between the predicted cargo volume data and the actual value is small. Finally, based on the prediction results, suggestions are made to optimize the resource allocation and operation process of the sorting center. The study shows that the LSTM model has important practical significance and application prospects in improving the accuracy of cargo volume prediction and the overall efficiency of the logistics system.

KEYWORDS

Time Series, LSTM, Logistics Sorting, Shipment Estimation, E-commerce

CITE THIS PAPER

Yuhao Song, Ziyan Lu, Logistics Sorting Center Volume Prediction Based on Time Series Prediction LSTM Model. Frontiers in Traffic and Transportation Engineering (2024) Vol. 4: 115-122. DOI: http://dx.doi.org/10.23977/ftte.2024.040114.

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Sponsors, Associates, and Links

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