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Symmetry
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Symmetry/Asymmetry in Intelligent Transportation
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Symmetry/Asymmetry in Intelligent Transportation

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 4434

Special Issue Editors

Dr. Lili Pei

E-Mail Website
Guest Editor
School of Data Science and Artificial Intelligence, Chang'an University, Xi'an, China
Interests: artificial intelligence-based road detection; road performance prediction and maintenance; traffic big data analysis; integration of transportation and energy
Prof. Dr. Wei Li

E-Mail Website
Guest Editor
School of Data Science and Artificial Intelligence, Chang'an University, Xi'an 710064, China
Interests: intelligent transportation model; multi-modal multi-target tracking; intelligent instrument research and manufacturing; 3D imaging and reconstruction

Special Issue Information

Dear Colleagues,

This Special Issue investigates the complex interaction of symmetry and asymmetry in Intelligent Transportation Systems (ITSs). It discusses the complex dynamics of ITSs, covering the comprehensive integration of traffic flow, dispatching, road conditions and vehicle–road interactions. We are committed to ensuring road safety and improving traffic management through careful understanding of the symmetry and asymmetry in these systems.

Our focus is on the key elements of ITSs: road detection, pavement performance prediction and maintenance, traffic flow analysis and traffic accident analysis. By analyzing the complex interaction between symmetric and asymmetric forces in intelligent transportation systems, we can formulate strategies to strengthen traffic management and safety. These strategies can also accurately predict pavement performance, optimize maintenance plans and help the industry adapt to the changing needs of traffic systems. We encourage the submission of in-depth research on these topics that provides opinions regarding their impact on the practical application of traffic engineering.

Dr. Lili Pei
Prof. Dr. Wei Li
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. 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

  • pavement detection
  • road performance
  • traffic flow
  • traffic accident analysis
  • vehicle road collaboration

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Published Papers (5 papers)

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Research

21 pages, 23093 KB  
Article
Keyframe-Guided Crack Segmentation and 3D Localization for UAV-Based Monocular Inspection
by Feifei Tang, Wuyuntana Gongzhabayier, Jing Li, Tao Zhou, Yue Qiu, Yong Zhan and Qiulin Song
Symmetry 2026, 18(4), 657; https://doi.org/10.3390/sym18040657 - 15 Apr 2026
Viewed by 378
Abstract
In unmanned aerial vehicle (UAV)-based monocular inspection, cracks typically present as geometrically asymmetric, elongated, low-contrast weak targets, making accurate segmentation and spatial localization challenging. Existing methods are susceptible to missed detections and false positives when handling slender cracks, and monocular 3D reconstruction for [...] Read more.
In unmanned aerial vehicle (UAV)-based monocular inspection, cracks typically present as geometrically asymmetric, elongated, low-contrast weak targets, making accurate segmentation and spatial localization challenging. Existing methods are susceptible to missed detections and false positives when handling slender cracks, and monocular 3D reconstruction for localization is often burdened by redundant frames, resulting in limited modeling efficiency. To mitigate these issues, we propose a high-precision framework for crack segmentation and spatial localization from UAV imagery. First, Oriented FAST and Rotated BRIEF–Simultaneous Localization and Mapping, version 3 (ORB-SLAM3) is adopted for keyframe selection to suppress data redundancy and improve reconstruction stability. Second, we develop an enhanced YOLOv11-seg model by integrating the Dilation-wise Residual Segmentation (DWRSeg) module, the Weighted IoU (WIoU) loss, and the Lightweight shared convolutional separator batch-normalization detection head (LSCSBD) to strengthen feature discrimination and segmentation robustness for slender cracks, yielding high-quality crack masks. Finally, the predicted masks are projected onto the reconstructed 3D surface to obtain precise spatial localization. Our experimental results demonstrate that the proposed approach improves the segmentation mAP@50 by 7.2% over the baseline while reducing computational complexity from 10.2 to 9.8 GFLOPs. In addition, keyframe-based processing reduces the 3D modeling time by 59.4% compared to that with full-frame reconstruction. Overall, the proposed framework jointly enhances crack segmentation accuracy and substantially accelerates 3D modeling and localization, providing an effective solution for efficient UAV-based crack inspection. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Intelligent Transportation)
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18 pages, 3868 KB  
Article
Anti-Wind Disturbance Algorithms for Small Rotorcraft UAVs
by Yini Cheng, Feifei Tang, Lili Pei, Huayu Zhang, Xiaoyu Cai, Feng Xu and Xiaoning Hou
Symmetry 2026, 18(4), 594; https://doi.org/10.3390/sym18040594 - 31 Mar 2026
Viewed by 390
Abstract
Small rotorcraft unmanned aerial vehicles (UAVs) are highly susceptible to wind disturbances when performing tasks such as fixed-point hovering, low-altitude inspection, and aggressive maneuvers. Under complex, variable meteorological conditions, attitude stability and position-holding accuracy are particularly critical. Although quadrotor UAVs exhibit structural and [...] Read more.
Small rotorcraft unmanned aerial vehicles (UAVs) are highly susceptible to wind disturbances when performing tasks such as fixed-point hovering, low-altitude inspection, and aggressive maneuvers. Under complex, variable meteorological conditions, attitude stability and position-holding accuracy are particularly critical. Although quadrotor UAVs exhibit structural and dynamic symmetry, real wind disturbances are often asymmetric, disrupting the original balance and leading to intensified attitude oscillations, position drift, and degraded data quality. To effectively address the challenges of wind-induced oscillation and positional deviation, this paper proposes a fuzzy logic-based linear active disturbance rejection control (Fuzzy-LADRC) strategy. This approach employs a hybrid algorithm combining particle swarm optimization and gray wolf optimization to optimize controller parameters and incorporates fuzzy logic to enhance the adaptive capability of the linear active disturbance rejection controller (LADRC). Simulation experiments conducted in MATLAB/Simulink under complex wind-field conditions demonstrate that the proposed method significantly outperforms traditional PID controllers: in the regulation of roll and pitch angles, control performance improves by approximately 5%, while in yaw angle control, the improvement reaches up to 30%. Furthermore, this method can significantly suppress position deviation and fluctuation in the X and Y directions, and reduce the overshoot in the Z-axis during the UAV’s takeoff phase by 75%. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Intelligent Transportation)
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21 pages, 3664 KB  
Article
Symmetry Breaking in Car-Following Dynamics: Suppressing Traffic Oscillations via Asymmetric Dynamic Delays
by Shuaiyang Jiao, Liyuan Xue, Aizeng Li, Zixiang Liu and Xiaoge Liu
Symmetry 2026, 18(2), 256; https://doi.org/10.3390/sym18020256 - 30 Jan 2026
Viewed by 473
Abstract
Accurately describing driver response mechanisms is fundamental to microscopic traffic modeling. Traditional car-following models typically assume a fixed reaction time, implying a temporal symmetry where drivers exhibit identical response characteristics during acceleration and deceleration. To address this limitation, this paper proposes a Delay [...] Read more.
Accurately describing driver response mechanisms is fundamental to microscopic traffic modeling. Traditional car-following models typically assume a fixed reaction time, implying a temporal symmetry where drivers exhibit identical response characteristics during acceleration and deceleration. To address this limitation, this paper proposes a Delay Adaptive Car-following Model that incorporates an asymmetric dynamic delay function to capture the symmetry breaking in driving behavior. Calibrated using empirical trajectory data from the Next Generation Simulation program, the proposed model demonstrates superior accuracy over the conventional Full Velocity Difference Model by effectively reproducing the realistic phenomenon of sluggish acceleration and agile deceleration. Linear stability analysis and numerical simulations reveal that, unlike fixed symmetric delays which often induce instability, the asymmetric dynamic delay acts as a self-adaptive damper. This mechanism suppresses the amplification of disturbances and prevents the formation of stop-and-go waves. The results confirm that incorporating temporal symmetry breaking into delay mechanisms significantly enhances the robustness of traffic flow against oscillations. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Intelligent Transportation)
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31 pages, 4648 KB  
Article
GF-NGB: A Graph-Fusion Natural Gradient Boosting Framework for Pavement Roughness Prediction Using Multi-Source Data
by Yuanjiao Hu, Mengyuan Niu, Liumei Zhang, Lili Pei, Zhenzhen Fan and Yang Yang
Symmetry 2026, 18(1), 134; https://doi.org/10.3390/sym18010134 - 9 Jan 2026
Viewed by 674
Abstract
Pavement roughness is a critical indicator for road maintenance decisions and driving safety assessment. Existing methods primarily rely on multi-source explicit features, which have limited capability in capturing implicit information such as spatial topology between road segments. Furthermore, their accuracy and stability remain [...] Read more.
Pavement roughness is a critical indicator for road maintenance decisions and driving safety assessment. Existing methods primarily rely on multi-source explicit features, which have limited capability in capturing implicit information such as spatial topology between road segments. Furthermore, their accuracy and stability remain insufficient in cross-regional and small-sample prediction scenarios. To address these limitations, we propose a Graph-Fused Natural Gradient Boosting framework (GF-NGB), which combines the spatial topology modeling capability of graph neural networks with the small-sample robustness of natural gradient boosting for high-precision cross-regional roughness prediction. The method first extracts an 18-dimensional set of multi-source features from the U.S. Long-Term Pavement Performance (LTPP) database and derives an 8-dimensional set of implicit spatial features using a graph neural network. These features are then concatenated and fed into a natural gradient boosting model, which is optimized by Optuna, to predict the dual objectives of left and right wheel-track roughness. To evaluate the generalization capability of the proposed method, we employ a spatially partitioned data split: the training set includes 1648 segments from Arizona, California, Florida, Ontario, and Missouri, while the test set comprises 330 segments from Manitoba and Nevada with distinct geographic and climatic conditions. Experimental results show that GF-NGB achieves the best performance on cross-regional tests, with average prediction accuracy improved by 1.7% and 3.6% compared to Natural Gradient Boosting (NGBoost) and a Graph Neural Network–Multilayer Perceptron hybrid model (GNN-MLP), respectively. This study reveals the synergistic effect of multi-source texture features and spatial topology information, providing a generalizable framework and technical pathway for cross-regional, small-sample intelligent pavement monitoring and smart maintenance. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Intelligent Transportation)
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22 pages, 2875 KB  
Article
Short-Term Road Traffic Flow Prediction Based on the KAN-CNN-BiLSTM Model with Spatio-Temporal Feature Integration
by Xiang Yang, Yongliang Cheng and Xiaolan Xie
Symmetry 2025, 17(11), 1920; https://doi.org/10.3390/sym17111920 - 10 Nov 2025
Cited by 2 | Viewed by 1320
Abstract
Short-term traffic flow prediction is a critical component of efficient management in Intelligent Transportation Systems (ITS), providing real-time travel guidance for commuters and supporting informed decision-making by transportation authorities. To address the current challenges of insufficient prediction accuracy and excessive reliance on time-series [...] Read more.
Short-term traffic flow prediction is a critical component of efficient management in Intelligent Transportation Systems (ITS), providing real-time travel guidance for commuters and supporting informed decision-making by transportation authorities. To address the current challenges of insufficient prediction accuracy and excessive reliance on time-series features, we propose a spatio-temporal feature-integrated short-term traffic flow prediction model named KAN-CNN-BiLSTM. In this model, traffic flow data from the target road segment and its two adjacent segments are jointly fed into the model to fully leverage spatio-temporal features for prediction. Subsequently, a Convolutional Neural Network (CNN) extracts spatial features from the combined traffic flow data. To overcome the limitation of traditional LSTMs, which can only process unidirectional time series, we introduce a bidirectional long short-term memory network (BiLSTM) with symmetric time series extraction capability. This enables simultaneous capture of historical and future traffic flow dependencies. Finally, we replace the conventional fully connected network with a Kolmogorov–Arnold network (KAN) to enhance the representation of complex nonlinear features. Experimental results using traffic flow data from the UK Highways Agency website demonstrate that the KAN-CNN-BiLSTM model outperforms existing mainstream methods, achieving superior prediction accuracy and minimal error. The model’s MAE, RMSE, MAPE, and R2 values are 27.4696, 40.3923, 8.65%, and 0.9615, respectively. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Intelligent Transportation)
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