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Symmetry
Special Issues
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: 28 February 2026 | Viewed by 1537

Special Issue Editors

Dr. Lili Pei

E-Mail Website
Guest Editor
School of Data Science and Artificial Intelligence, Chang'an University, Xi'an 710064, 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 (1 paper)

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Research

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
Viewed by 767
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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