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Neural Computing and Applications
Publishing model:
Hybrid

Neural Computing and Applications - Topical Collection on Deep Neural Networks for Traffic Forecasting

Aim and Objective

Traffic forecasting is important for the success of intelligent transportation systems and the operation of smart cities. Deep neural networks (DNNs), including convolution neural networks (CNNs) and recurrent neural networks (RNNs), have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies in the literature. DNNs have been proven more effective than traditional linear models. In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks (GNNs) have been introduced and have achieved state-of-the-art performance in a series of traffic forecasting problems. In this topical collection, we aim to collect the studies that explore the application of deep neural networks for traffic forecasting problems.

Scope

  • New models of deep neural networks for traffic forecasting problems, e.g., CNNs, RNNs, Transformers, graph convolutional and graph attention networks, spatio-temporal GNNs, etc.
  • Applications of deep neural networks for traffic forecasting problems, e.g., road traffic flow and speed forecasting, bike sharing forecasting, passenger flow forecasting in urban rail transit systems, demand forecasting in taxi and ride-hailing platforms, etc.
  • Open data and source resources for traffic forecasting problems.

Guest Editors

Prof. Weiwei Jiang (Lead Guest Editor), Beijing University of Posts and Telecommunications, China, jww@bupt.edu.cn (this opens in a new tab)
Prof. Dalin Zhang, Aalborg University, Denmark, dalinz@cs.aau.dk (this opens in a new tab)
Dr. Zhen Fang, University of Technology Sydney, Australia, zhen.fang@uts.edu.au (this opens in a new tab)
Prof. Renhe Jiang, The University of Tokyo, Japan, jiangrh@csis.u-tokyo.ac.jp (this opens in a new tab)
Prof. Shirui Pan, Griffith University, Australia, s.pan@griffith.edu.au (this opens in a new tab)

Manuscript submission deadline: 1st April 2024

Peer Review Process

All the papers will go through peer review,  and will be reviewed by at least two reviewers. A thorough check will be completed, and the guest editor will check any significant similarity between the manuscript under consideration and any published paper or submitted manuscripts of which they are aware. In such case, the article will be directly rejected without proceeding further. Guest editors will make all reasonable effort to receive the reviewer’s comments and recommendation on time.

The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended to be considered for this special issue (with at least 30% difference from the original works).

Submission Guidelines

Paper submissions for the special issue should strictly follow the submission format and guidelines (https://www.springer.com/journal/521/submission-guidelines (this opens in a new tab)). Each manuscript should not exceed 16 pages in length (inclusive of figures and tables).

Manuscripts must be submitted to the journal online system at https://www.editorialmanager.com/ncaa/default.aspx (this opens in a new tab) or via the 'Submit manuscript' button on the journal homepage.
Authors should select “TC: Deep Neural Networks for Traffic Forecasting” during the submission step ‘Additional Information’.

Author Resources

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.  

Springer provides a host of information about publishing in a Springer Journal on our Journal Author Resources page, including  FAQs (this opens in a new tab),  Tutorials (this opens in a new tab)  along with  Help and Support (this opens in a new tab).

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