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Symmetry
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Symmetry/Asymmetry in Data Mining, Optimization Algorithms, and System Control for...
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Symmetry/Asymmetry in Data Mining, Optimization Algorithms, and System Control for Intelligent Transportation Systems

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 505

Special Issue Editors

Dr. Zhiyuan Sun

E-Mail Website
Guest Editor
College of Metropolitan Transportation, Beijing University of Technology, Beijing, China
Interests: traffic behavior and safety; intelligent transportation system; urban transportation geography; traffic big data
Dr. Jianyu Wang

E-Mail Website
Guest Editor
School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
Interests: traffic safety; intelligent transportation; transportation planning
Dr. Yongnan Zhang

E-Mail Website
Guest Editor Assistant
College of Metropolitan Transportation, Beijing University of Technology, Beijing, China
Interests: artificial intelligence; parallel computing; cloud computing; optimization control theory; traffic signal control; traffic big data; intelligent transportation systems

Special Issue Information

Dear Colleagues,

Symmetry/Asymmetry is prevalent in Intelligent Transportation Systems (ITSs). Utilizing advanced methodologies such as data mining, optimization algorithms, and system control to identify these characteristics and enhance the technological capabilities of ITSs is essential for effective traffic management. In the era of rapid AI advancement, these technologies have undergone swift innovation. The scope of this Special Issue is to present research focused on symmetry/asymmetry in ITSs utilizing big data, as well as the application of advanced methodologies to address some of the inherent challenges faced by ITSs.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Symmetrical/asymmetrical design of ITSs;
  • Analysis of symmetry/asymmetry in ITSs;
  • Application of data mining, optimization algorithms, and system control in ITSs;
  • Data-driven-based traffic safety analysis;
  • Data-driven-based traffic behavior analysis;
  • Data-driven-based traffic signal control;
  • Data-driven-based urban geographic analysis;
  • Data-driven-based trajectory tracking control.

We look forward to receiving your contributions.

Dr. Zhiyuan Sun
Dr. Jianyu Wang
Guest Editors

Dr. Yongnan Zhang
Guest Editor Assistant

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. Symmetry 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

  • symmetry design
  • data mining
  • optimization algorithms
  • system control
  • traffic safety
  • traffic behavior
  • traffic signal control
  • urban geography
  • trajectory tracking control

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Published Papers (1 paper)

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Research

27 pages, 3850 KB  
Article
A Robust Meta-Learning-Based Map-Matching Method for Vehicle Navigation in Complex Environments
by Fei Meng and Jiale Zhao
Symmetry 2026, 18(1), 210; https://doi.org/10.3390/sym18010210 - 22 Jan 2026
Viewed by 109
Abstract
Map matching is a fundamental technique for aligning noisy GPS trajectory data with digital road networks and constitutes a key component of Intelligent Transportation Systems (ITS) and Location-Based Services (LBS). Nevertheless, existing approaches still suffer from notable limitations in complex environments, particularly urban [...] Read more.
Map matching is a fundamental technique for aligning noisy GPS trajectory data with digital road networks and constitutes a key component of Intelligent Transportation Systems (ITS) and Location-Based Services (LBS). Nevertheless, existing approaches still suffer from notable limitations in complex environments, particularly urban and urban-like scenarios characterized by heterogeneous GPS noise and sparse observations, including inadequate adaptability to dynamically varying noise, unavoidable trade-offs between real-time efficiency and matching accuracy, and limited generalization capability across heterogeneous driving behaviors. To overcome these challenges, this paper presents a Meta-learning-driven Progressive map-Matching (MPM) method with a symmetry-aware design, which integrates a two-layer pattern-mining-based noise-robust meta-learning mechanism with a dynamic weight adjustment strategy. By explicitly modeling topological symmetry in road networks, symmetric trajectory patterns, and symmetric noise variation characteristics, the proposed method effectively enhances prior knowledge utilization, accelerates online adaptation, and achieves a more favorable balance between accuracy and computational efficiency. Extensive experiments on two real-world datasets demonstrate that MPM consistently outperforms state-of-the-art methods, achieving up to 10–15% improvement in matching accuracy while reducing online matching latency by over 30% in complex urban environments. Furthermore, the symmetry-aware design significantly improves robustness against asymmetric interference, thereby providing a reliable and scalable solution for high-precision map matching in complex and dynamic traffic environments. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Data Mining, Optimization Algorithms, and System Control for Intelligent Transportation Systems)
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