AI-Driven Spatiotemporal Computing in Complex Traffic Systems
A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".
Deadline for manuscript submissions: 20 January 2027 | Viewed by 604
Editors
Interests: autonomous driving; traffic demand forecasting; mixed traffic flow modelling and simulation; traffic state estimation; multimodal vehicle trajectory; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: traffic flow dynamic modeling and control; trajectory prediction; fleet control; graph neural networks; physical information deep learning; neural operators
Interests: traffic spatiotemporal big data; intelligent traffic management and control; travel demand analysis; urban computing; tourism transportation; large language model
Special Issue Information
Dear Colleagues,
This Special Issue, “AI-Driven Spatiotemporal Computing in Complex Traffic Systems,” aims to showcase cutting-edge theories, methodologies, and applications that advance the modeling, understanding, prediction, simulation, and optimization of complex transportation systems through artificial intelligence. With the rapid development of multi-source sensing, connected and automated vehicles, digital twin platforms, and large-scale traffic data infrastructures, transportation research is undergoing a paradigm shift—from mechanism-driven analysis and isolated prediction tasks toward data-driven, knowledge-guided, and decision-oriented spatiotemporal computing. Traffic systems exhibit complex spatial interactions, temporal dependencies, nonlinear dynamics, and strong uncertainty. Artificial intelligence provides new opportunities to capture these characteristics by integrating traffic flow theory, machine learning, reinforcement learning, graph learning, neural operators, foundation models, and simulation-based optimization. This Special Issue seeks to promote research that bridges theoretical innovation and practical deployment, with particular attention to scalable, interpretable, robust, and trustworthy AI methods for traffic spatiotemporal analysis and control.
We invite submissions of original research articles and comprehensive reviews on topics including, but not limited to, the following:
Scientific machine learning for traffic spatiotemporal computing.
AI-driven traffic state estimation, reconstruction, and prediction.
Travel behavior mining and analysis.
Intelligent traffic signal control and lane management.
Mixed traffic flow modeling, simulation, and optimization of mixed traffic systems.
Graph neural networks and foundation models.
Prof. Dr. Rongjun Cheng
Dr. Ting Wang
Dr. Guojian Zou
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly 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 2400 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.
Keywords
- traffic spatiotemporal computing
- scientific machine learning
- traffic system simulation and optimization
- traffic system modeling and control
- traffic spatiotemporal big data analysis and mining
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