Regional Logistics Demand Forecasting Based on Neural Networks

Dear Colleagues,

Regional logistics systems in areas with large and medium-sized cities as the center can significantly enhance the level and efficiency of logistics activities, based on the scale and scope of the regional economy, combined with the effective scope of logistics radiation, and the effective physical flow of all kinds of goods from suppliers to end users both inside and outside the region. In recent years, however, the amount of logistics data has increased dramatically, logistics demand has grown rapidly, and regional logistics systems urgently need accurate regional logistics demand models because the accurate prediction of regional logistics demand is a prerequisite for scientific decision-making in regional logistics.

The rapid development of neural networks provides the technical basis for accurate logistics demand forecasting. Neural networks have multiple nonlinear mapping feature transformations, which can be fitted to highly complex functions and can extract richer data features. Therefore, this Special Issue is concerned with original research and review articles on neural networks applied to regional logistics demand forecasting, especially on logistics demand forecasting and applications based on recurrent neural networks and graphical neural networks.

Potential topics include, but are not limited to, those listed in the keywords below.

• Recurrent neural networks for regional logistics demand forecasting;
• Graph neural networks for regional logistics demand forecasting;
• Convolutional neural networks for regional logistics demand forecasting;
• Machine learning technology for regional logistics demand forecasting;
• Reinforcement learning for regional logistics demand forecasting.

Dr. Weiwei Cai
Dr. Jianlei Kong
Dr. Yao Ding
Dr. Devon K. Barrow
Guest Editors

Manuscript Submission Information

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• recurrent neural networks for regional logistics demand forecasting
• graph neural networks for regional logistics demand forecasting
• convolutional neural networks for regional logistics demand forecasting
• machine learning technology for regional logistics demand forecasting
• reinforcement learning for regional logistics demand forecasting