Special Issue "Regional Logistics Demand Forecasting Based on Neural Networks"

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Forecasting in Economics and Management".

Deadline for manuscript submissions: closed (22 November 2023) | Viewed by 241

Special Issue Editors

1. Graduate School, Northern Arizona University, Flagstaff, AZ 86011, USA
2. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Interests: neural networks; forecast modeling; deep learning
Special Issues, Collections and Topics in MDPI journals
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Interests: time-series prediction; pattern recognition; deep learning; blockchain traceability
Special Issues, Collections and Topics in MDPI journals
Xi’an Research Institute of High Technology, Xi’an 710025, China
Interests: computer vision; forecast modeling
Department of Management, Birmingham Business School, University of Birmingham, Birmingham B15 2TY, UK
Interests: time series prediction with neural networks and statistical methods; forecast combination and model selection

Special Issue Information

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • 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

Published Papers

There is no accepted submissions to this special issue at this moment.
Back to TopTop