Symmetry-Aware Generative AI: Emerging Trends and Applications in Intelligent Transportation Systems

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1377

Special Issue Editors


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Guest Editor
School of Telematics, Universidad de Colima, Colima 28040, Mexico
Interests: human-computer interaction; ICT for the elderly; serious games; internet of things; intelligent systems

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Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico
Interests: human-computer interaction; medical informatics; artificial intelligence

Special Issue Information

Dear Colleagues,

Generative artificial intelligence (GenAI) is reshaping the landscape of smart transportation by offering innovative solutions to optimize traffic flow, enhance safety, and improve infrastructure management. As transportation systems become increasingly complex, symmetry and asymmetry in data patterns, model architectures, and system behaviors are pivotal in achieving efficient and adaptive designs. This Special Issue explores the intersection of symmetry principles with GenAI to address key challenges in intelligent transportation systems (ITS), such as route optimization, anomaly detection, and predictive maintenance. We invite contributions that focus on the symmetric analysis of generative models, developing symmetric and asymmetric neural network architectures, and applying generative AI in ITS but are not limited to these areas. Submissions highlighting theoretical advancements, methodological details, and experimental results demonstrating reproducibility and practical applications are particularly encouraged. By leveraging the concepts of symmetry, we seek to advance the understanding and implementation of GenAI for smarter, safer, and more efficient transportation ecosystems.

Prof. Dr. Pedro C. Santana-Mancilla
Prof. Dr. Marcela D. Rodriguez
Guest Editors

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Keywords

  • symmetry in artificial intelligence
  • generative AI
  • smart transportation systems
  • neural network architectures
  • symmetric analysis

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Published Papers (1 paper)

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Research

27 pages, 4514 KB  
Article
Sustainable Urban Mobility: Leveraging Generative AI for Symmetry-Aware Traffic Light Optimization
by Pedro C. Santana-Mancilla, Antonio Guerrero-Ibáñez, Juan Contreras-Castillo, Jesús García-Mancilla and Luis Anido-Rifón
Symmetry 2025, 17(12), 2083; https://doi.org/10.3390/sym17122083 - 4 Dec 2025
Viewed by 333
Abstract
Urban intersections are critical nodes where traffic congestion and energy inefficiency converge. Traditional signal control systems often optimize either mobility or sustainability, creating an asymmetry between flow efficiency and environmental impact. This study introduces a symmetry-aware generative optimization framework that leverages Generative Artificial [...] Read more.
Urban intersections are critical nodes where traffic congestion and energy inefficiency converge. Traditional signal control systems often optimize either mobility or sustainability, creating an asymmetry between flow efficiency and environmental impact. This study introduces a symmetry-aware generative optimization framework that leverages Generative Artificial Intelligence (GAI) to balance both dimensions. Using the microscopic simulator SUMO, we modeled a signalized intersection in Colima, Mexico, under five control strategies: Fixed Time (baseline), GPT-4o, GPT-5 Thinking, Gemini 2.5 Pro, and DeepSeek V3. Each Large Language Model (LLM) received structured simulation data and generated new phase-duration configurations to minimize queue length, travel time, and CO2 emissions while improving average speed. Step-level performance was evaluated using descriptive statistics, and Wilcoxon signed-rank tests paired with Holm–Bonferroni correction. Results show that all LLM-based controllers significantly outperformed the Fixed Time baseline (adjusted p ≤ 4.8 × 10−6), with large effect sizes (|dz| ≈ 1.5–2.6). GPT-5 achieved the strongest performance, reducing queue size by ≈ 44%, CO2 emissions by ≈ 17%, and increasing average speed by ≈ 58%. The results validate the feasibility of symmetry-aware generative reasoning for sustainable traffic optimization and establish a reproducible methodological framework applicable to future AI-driven urban mobility systems. Full article
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