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Electronics
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Artificial Intelligence for Traffic Understanding and Control
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Artificial Intelligence for Traffic Understanding and Control

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 February 2026 | Viewed by 479

Special Issue Editors

Dr. Jinyu Chen

E-Mail Website
Guest Editor
Graduate School of Interdisciplinary Information Studies, University of Tokyo, Tokyo 113-0033, Japan
Interests: urban mobility; artificial intelligence
Dr. Jiawei Wang

E-Mail Website
Guest Editor
Graduate School of Interdisciplinary Information Studies, University of Tokyo, Tokyo 113-0033, Japan
Interests: data/AI-driven traffic analysis; reinforcement learning-based traffic optimization

Special Issue Information

Dear Colleagues,

The rapid advancement of data-driven techniques and artificial intelligence (AI) has fundamentally transformed how we understand, model, and control urban transportation systems. AI methods continue to evolve, driven by breakthroughs in deep learning architectures, generative modeling, and learning-based control optimization. For instance, diffusion-based generative models introduce a new paradigm of data generation compared to traditional approaches, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), and have shown promise in tasks such as vehicle trajectory generation. Meanwhile, large language models (LLMs) have emerged as powerful foundation models with strong reasoning and generalization capabilities, offering new possibilities for traffic analysis, forecasting, and control. These innovations present significant opportunities to address the inherent complexity and dynamism of modern transportation systems. Accordingly, there is substantial potential for further research into how such advanced AI techniques can be effectively harnessed to improve the efficiency, adaptability, and intelligence of intelligent transportation systems

This Special Issue will highlight recent advances and emerging trends in the application of data science and AI technologies to improve traffic understanding and control, particularly regarding strategies for developing more advanced intelligent transportation systems (ITSs). Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Deep generative models for traffic generation;
  • Deep learning-based traffic forecasting;
  • Reinforcement learning based traffic control and management;
  • Data-driven traffic understanding;
  • Data-driven traffic modeling and simulation;
  • Large language models for traffic generation;
  • Large language models for traffic analysis;
  • Large language models for traffic control and management.

Dr. Jinyu Chen
Dr. Jiawei Wang
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly 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

  • intelligent transporation systems
  • reinforcement learning
  • deep learning
  • large language models
  • generative models
  • urban computing

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Published Papers (1 paper)

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Research

16 pages, 1561 KB  
Article
TSAformer: A Traffic Flow Prediction Model Based on Cross-Dimensional Dependency Capture
by Haoning Lv, Xi Chen and Weijie Xiu
Electronics 2026, 15(1), 231; https://doi.org/10.3390/electronics15010231 - 4 Jan 2026
Viewed by 238
Abstract
Accurate multivariate traffic flow forecasting is critical for intelligent transportation systems yet remains challenging due to the complex interplay of temporal dynamics and spatial interactions. While Transformer-based models have shown promise in capturing long-range temporal dependencies, most existing approaches compress multidimensional observations into [...] Read more.
Accurate multivariate traffic flow forecasting is critical for intelligent transportation systems yet remains challenging due to the complex interplay of temporal dynamics and spatial interactions. While Transformer-based models have shown promise in capturing long-range temporal dependencies, most existing approaches compress multidimensional observations into flattened sequences—thereby neglecting explicit modeling of cross-dimensional (i.e., spatial or inter-variable) relationships, which are essential for capturing traffic propagation, network-wide congestion, and node-specific behaviors. To address this limitation, we propose TSAformer, a novel Transformer architecture that explicitly preserves and jointly models time and dimension as dual structural axes. TSAformer begins with a multimodal input embedding layer that encodes raw traffic values alongside temporal context (time-of-day and day-of-week) and node-specific positional features, ensuring rich semantic representation. The core of TSAformer is the Two-Stage Attention (TSA) module, which first models intra-dimensional temporal evolution via time-axis self-attention then captures inter-dimensional spatial interactions through a lightweight routing mechanism—avoiding quadratic complexity while enabling all-to-all cross-node communication. Built upon TSA, a hierarchical encoder–decoder (HED) structure further enhances forecasting by modeling traffic patterns across multiple temporal scales, from fine-grained fluctuations to macroscopic trends, and fusing predictions via cross-scale attention. Extensive experiments on three real-world traffic datasets—including urban road networks and highway systems—demonstrate that TSAformer consistently outperforms state-of-the-art baselines across short-term and long-term forecasting horizons. Notably, it achieves top-ranked performance in 36 out of 58 critical evaluation scenarios, including peak-hour and event-driven congestion prediction. By explicitly modeling both temporal and dimensional dependencies without structural compromise, TSAformer provides a scalable, interpretable, and high-performance solution for spatiotemporal traffic forecasting. Full article
(This article belongs to the Special Issue Artificial Intelligence for Traffic Understanding and Control)
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