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Symmetry
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Advances in Machine Learning with Symmetry/Asymmetry in Transportation
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Advances in Machine Learning with Symmetry/Asymmetry in Transportation

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2060

Special Issue Editors

Dr. Gen Li

E-Mail Website
Guest Editor
College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: road safety; heavy trucks; intelligent transportation systems; data mining; sustainable transportation; auto driving; driving behavior
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yangsheng Jiang

E-Mail Website
Guest Editor
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
Interests: intelligent transportation and traffic big data analysis; the integrated public transport system is intelligent; optimization of transportation and logistics systems; planning and management of transportation and logistics systems

Special Issue Information

Dear Colleagues,

The continuous advancement of traffic safety relies heavily on innovative algorithmic solutions. Leveraging symmetry principles in the design of traffic safety systems can lead to significant improvements in predicting and preventing accidents. The integration of machine learning algorithms and symmetry analysis offers promising directions for enhancing road safety. Key areas of interest include the symmetrical and asymmetrical design of traffic control systems, dynamic modeling and parameter identification of traffic flow, and advanced methods for real-time accident risk assessment.

This Special Issue, therefore, invites all original and reviewed papers covering the challenging aspects of traffic safety algorithms, including, but not limited to, the following topics:

  • Symmetrical/asymmetrical design of traffic control systems;
  • Dynamic modeling and parameter identification of traffic flow;
  • Real-time traffic accident prediction and risk assessment;
  • Advanced machine learning algorithms for traffic safety optimization;
  • Environmental perception and decision-making for traffic management;
  • High-performance trajectory tracking and control for accident prevention;
  • Functional safety and reliability of intelligent traffic systems;
  • Symmetry/Asymmetry analysis in traffic safety algorithms.

Dr. Gen Li
Prof. Dr. Yangsheng Jiang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • traffic safety algorithms
  • symmetry design
  • dynamic modeling
  • parameter identification
  • real-time accident prediction
  • risk assessment
  • environmental perception
  • decision planning
  • trajectory tracking control

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

23 pages, 8147 KiB  
Article
Traffic Volume Estimation Based on Spatiotemporal Correlation Adaptive Graph Convolutional Network
by Sheng Ding, Fei Yan and Yingmin Yi
Symmetry 2025, 17(4), 599; https://doi.org/10.3390/sym17040599 - 15 Apr 2025
Viewed by 338
Abstract
Traffic volume estimation is a fundamental task in Intelligent Transportation Systems (ITS). The highly unbalanced and asymmetric spatiotemporal distribution of traffic flow combined with the sparse and uneven deployment of sensors pose significant challenges for accurate estimation. To address these issues, this paper [...] Read more.
Traffic volume estimation is a fundamental task in Intelligent Transportation Systems (ITS). The highly unbalanced and asymmetric spatiotemporal distribution of traffic flow combined with the sparse and uneven deployment of sensors pose significant challenges for accurate estimation. To address these issues, this paper proposes a novel traffic volume estimation framework. It combines a dynamic adjacency matrix Graph Convolutional Network (GCN) with a multi-scale transformer structure to capture spatiotemporal correlation. First, an adaptive speed-flow correlation module captures global road correlations based on historical speed patterns. Second, a dynamic recurrent graph convolution network is used to capture both short- and long-range correlations between roads. Third, a multi-scale transformer module models the short-term fluctuations and long-term trends of traffic volume at multiple scales, capturing temporal correlations. Finally, the output layer fuses spatiotemporal correlations to estimate the global road traffic volume at the current time. Experiments on the PEMS-BAY dataset in California show that the proposed model outperforms the baseline models and achieves good estimation results with only 30% sensor coverage. Ablation and hyperparameter experiments validate the effectiveness of each component of the model. Full article
(This article belongs to the Special Issue Advances in Machine Learning with Symmetry/Asymmetry in Transportation)
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26 pages, 6532 KiB  
Article
Analysis of the Impact of Different Road Conditions on Accident Severity at Highway-Rail Grade Crossings Based on Explainable Machine Learning
by Zhen Yang, Chen Zhang, Gen Li and Hongyi Xu
Symmetry 2025, 17(1), 147; https://doi.org/10.3390/sym17010147 - 20 Jan 2025
Cited by 2 | Viewed by 1203
Abstract
Previous studies on highway_rail grade crossing collisions have primarily focused on identifying factors contributing to the frequency and severity of driver injuries. In recent years, increasing attention has been given to modeling driver injury severity at these crossings. Recognizing the variations in injury [...] Read more.
Previous studies on highway_rail grade crossing collisions have primarily focused on identifying factors contributing to the frequency and severity of driver injuries. In recent years, increasing attention has been given to modeling driver injury severity at these crossings. Recognizing the variations in injury severity under different road surface conditions, this study investigates the impact of road surface conditions on driver injury severity at highway_rail grade crossings. Using nearly a decade of accident data (2012–2021), thi study employs a LightGBM model to predict factors influencing injury severity and utilizes SHAP values for result interpretation. The symmetry principle of SHAP esures that factors with identical influence receive equal values, enhancing the reliability of predictive outcomes. The findings reveal that driver injury severity at highway_rail grade crossings varies significantly under different road surface conditions. Key factors identified include train speed, driver age, vehicle speed, annual average daily traffic (AADT), driver presence inside the vehicle, weather conditions, and location. The results indicate that collisions are more frequent when either the vehicle or train travels at high speed. Implementing speed limits for both vehicles and trains under varying road conditions could effectively reduce accident severity. Additionally, older drivers are more prone to severe accidents, highlighting the importance of installing control devices, such as warning signs or signals, to enhance driver alertness and mitigate injury risks. Furthermore, adverse weather conditions, such as rain, snow, and fog, exacerbate accident severity on road surfaces like sand, mud, dirt, oil, or gravel. Timely removal of surface obstacles may help reduce the severity of such accidents. Full article
(This article belongs to the Special Issue Advances in Machine Learning with Symmetry/Asymmetry in Transportation)
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