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

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2852

Editors

Dr. Yuyan Annie Pan

E-Mail Website
Guest Editor
FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL, USA
Interests: traffic signal control; public transit; CAVs; EVs; traffic demand management; machine learning applications in smart mobility; traffic state estimation and prediction; traffic flow theory; transportation network optimization; dynamic traffic assignment
Special Issues, Collections and Topics in MDPI journals
Dr. Bingyan Cui

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
Interests: transportation infrastructure resilience; infrastructure risk management; sustainable transportation materials; intelligent transportation systems
Dr. Huibo Bi

E-Mail Website
Guest Editor
Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing, China
Interests: emergency management; intelligent transportation systems; brain-inspired computing; system optimisation; energy efficiency and energy conservation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on exploring the role of symmetry and asymmetry theories in advancing intelligent transportation systems (ITSs). ITSs integrate mathematics, computer science, control theory, artificial intelligence, sensors, and IoT technologies to enhance safety, efficiency, and sustainability across multimodal transportation networks. However, real-world ITS applications still face challenges such as system complexity, data uncertainty, and the need for symmetry breaking in modeling and optimization. We are pleased to invite you to contribute to the Special Issue ‘Symmetry/Asymmetry in Intelligent Transportation System’. This Special Issue aims to explore the role of symmetry and asymmetry theories in advancing ITS research and applications. By highlighting theoretical developments, data-driven methods, and engineering applications, this Issue seeks to bridge the gap between theory and practice in the perception, prediction, and control of transportation systems. The topic aligns closely with the journal’s scope by emphasizing the interplay between mathematical principles and engineering innovations for building resilient, adaptive, and intelligent transportation infrastructures.

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

  • Symmetry and asymmetry in modeling and optimization of transportation systems.
  • Big data analytics and AI-enhanced traffic management.
  • Connected and autonomous vehicles.
  • Multimodal transport modeling and control.
  • Digital twin-based system optimization.
  • Applications of mathematical symmetry theory in ITS design and operation.

We look forward to receiving your valuable contributions.

Sincerely,

Dr. Yuyan Annie Pan
Dr. Bingyan Cui
Dr. Huibo Bi
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-anonymized 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

  • intelligent transportation systems (ITSs)
  • symmetry and asymmetry
  • artificial intelligence and big data
  • connected and autonomous vehicles (CAVs)
  • transportation network modeling
  • symmetry breaking in optimization
  • digital twin
  • infrastructure resilience
  • traffic flow prediction and control
  • multimodal transportation systems

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

Published Papers (5 papers)

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Research

25 pages, 4772 KB  
Article
Physics-Informed Neural Networks for Non-Recurrent Traffic Congestion Detection: A Case Study on the Seoul Ring Expressway
by Woohun Jeon, Joyoung Lee, Jinguk Kim and Md Tufajjal Hossain
Symmetry 2026, 18(8), 1394; https://doi.org/10.3390/sym18081394 - 19 Aug 2026
Viewed by 159
Abstract
Non-recurrent congestion (NRC), caused by unforeseen events such as crashes, lane closures, and adverse weather, accounts for approximately half of all delays on urban freeways, yet it remains difficult to distinguish from routine congestion at recurrent bottlenecks. This study proposes an NRC detection [...] Read more.
Non-recurrent congestion (NRC), caused by unforeseen events such as crashes, lane closures, and adverse weather, accounts for approximately half of all delays on urban freeways, yet it remains difficult to distinguish from routine congestion at recurrent bottlenecks. This study proposes an NRC detection framework based on a Physics-Informed Neural Network (PINN) that embeds the Lighthill–Whitham–Richards (LWR) conservation law into the learning process to construct a physically consistent baseline of normal traffic states. The traffic flow physics is represented by a two-regime fundamental diagram combining the Greenshields model for free-flow conditions and the Underwood model for congested conditions, and the network is trained by minimizing a composite loss that adaptively balances the data fitting error against the LWR residual. NRC is then detected when the observed density exceeds the PINN-estimated baseline density beyond a tolerance threshold of 150%. The framework was evaluated on a 12 km segment of the Seoul Ring Expressway in Korea using six months of 15 min data collected from seventeen sensor stations. The results show that the proposed model reliably isolates NRC events from recurrent peak-period congestion. From the perspective of symmetry, the framework interprets recurrent traffic as a temporally symmetric background state governed by a conservation law, and non-recurrent congestion as a local breaking of this symmetry, which the physics-constrained residual is designed to expose. The key contribution of this study is a theoretically grounded, label-free anomaly detection approach that couples machine learning with traffic flow theory, offering traffic management centers an automated and interpretable tool for incident detection and response. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Intelligent Transportation System)
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36 pages, 15272 KB  
Article
Symmetry-Aware Robust Scheduling and Energy Management of Hybrid-Powered Vessels in Maritime Multi-Port Liner Services
by Zhichao Cao, Anqi Xing, Tao Qian, Jianqiu Chen, Xiali Cao and Yize Zhang
Symmetry 2026, 18(8), 1350; https://doi.org/10.3390/sym18081350 - 11 Aug 2026
Viewed by 246
Abstract
Driven by low-carbon mandates, hybrid power vessels integrating diesel, battery, shore-power, and photovoltaic vessel (PV) systems are emerging as a key green shipping pathway. However, operation scheduling is essentially complex due to the integration between supply-side routing and load-side energy dispatch, which is [...] Read more.
Driven by low-carbon mandates, hybrid power vessels integrating diesel, battery, shore-power, and photovoltaic vessel (PV) systems are emerging as a key green shipping pathway. However, operation scheduling is essentially complex due to the integration between supply-side routing and load-side energy dispatch, which is compounded by multi-dimensional uncertainties in PV generation, port-grid loads, and feeder delays. To address this, we formulate a unified two-stage robust optimization model. The objective is to simultaneously minimize operating costs and enhance port-grid friendliness by coordinating on-board energy management and shore-power interactions. In detail, the first stage determines routing and sailing speeds, while the second stage allocates multi-source power under a worst-case budgeted polyhedral uncertainty set. A piecewise-linearization scheme handles the cubic speed–power relation, rendering a tractable mixed-integer linear programming problem. The problem is efficiently solved via a tailored Benders decomposition algorithm, utilizing a genetic-algorithm warm-start to substantially accelerate convergence. Validated on three real-world networks via 1000 Monte Carlo scenarios, the proposed model reduces mean operating costs by 20.2–23.5% and suppresses cost variance by over 60% compared to deterministic approaches. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Intelligent Transportation System)
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34 pages, 9910 KB  
Article
Transformer-Based Predictive Motion Planning at Signalized Intersections: A Symmetry-Breaking Perspective in a SUMO–CARLA Co-Simulation Environment
by Anran Li, Hongsheng Yu, Bing Han, Dong Sun, Weijie Gou, Yanyan Chen and Yuyan (Annie) Pan
Symmetry 2026, 18(7), 1165; https://doi.org/10.3390/sym18071165 - 10 Jul 2026
Cited by 1 | Viewed by 387
Abstract
Autonomous vehicles operating at signalized intersections face fundamental challenges arising from queue dynamics, signal-phase transitions, and tightly coupled multi-vehicle interactions. Conventional motion-planning methods, which rely primarily on instantaneous perception, are inherently reactive and struggle to reason about short-term traffic evolution. This paper presents [...] Read more.
Autonomous vehicles operating at signalized intersections face fundamental challenges arising from queue dynamics, signal-phase transitions, and tightly coupled multi-vehicle interactions. Conventional motion-planning methods, which rely primarily on instantaneous perception, are inherently reactive and struggle to reason about short-term traffic evolution. This paper presents a Transformer-based predictive motion-planning framework that embeds short-term traffic state prediction directly into the structure of the planning problem. A lightweight spatial–temporal Transformer model is designed to forecast traffic occupancy, queue evolution, and interaction patterns using historical trajectories, signal-phase information, and road topology. By converting predicted traffic dynamics into explicit spatial–temporal constraints, a hierarchical motion planner jointly optimizes path geometry and speed profiles through dynamically constructed feasible corridors. The proposed framework is evaluated using a joint SUMO–CARLA simulation platform under realistic traffic conditions derived from real-world datasets, including pNEUMA and CitySim. The experimental results across straight-through, queueing, and turning scenarios show that prediction-aware planning significantly reduces high-risk driving time and intersection travel time while maintaining stable real-time computational performance. Beyond scenario-level improvements, the results indicate that transforming traffic prediction into planning constraints provides a generalizable paradigm for proactive, feasibility-aware autonomous driving at signalized intersections. From a methodological perspective, the proposed framework can be interpreted through the lens of symmetry and asymmetry in intelligent transportation systems: the conventional symmetric decoupling between prediction and planning modules is deliberately broken by embedding predicted traffic states as time-varying, directionally asymmetric constraints, while the permutation symmetry of the multi-head attention mechanism is preserved over lane-segment tokens to provide a structured inductive bias for traffic state forecasting. This symmetry-aware design highlights how controlled symmetry breaking in modeling and optimization can yield safer, more efficient, and more adaptive autonomous driving behaviors in signalized urban environments. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Intelligent Transportation System)
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18 pages, 2599 KB  
Article
Collaborative Scheme for Speed Limit and Illumination at Rural Highway Intersection Based on Drivers’ Ability to Visually Recognize VRUs
by Mengyuan Huang, Ying Hu, Jiaming Liu, Jinjun Sun and Ayinigeer Wumaierjiang
Symmetry 2026, 18(4), 687; https://doi.org/10.3390/sym18040687 - 21 Apr 2026
Viewed by 460
Abstract
Poor visibility contributes to nighttime accidents at highway intersections, especially in developing countries where vehicles mix with vulnerable road users (VRUs) such as pedestrians and cyclists. Unlike downtown intersections with traffic signals and ambient lighting, rural intersections have no signals and minimal ambient [...] Read more.
Poor visibility contributes to nighttime accidents at highway intersections, especially in developing countries where vehicles mix with vulnerable road users (VRUs) such as pedestrians and cyclists. Unlike downtown intersections with traffic signals and ambient lighting, rural intersections have no signals and minimal ambient light, forcing drivers to rely on roadway lighting for hazard recognition. Improving illumination arrangements can significantly reduce the likelihood of crashes. However, there are significant differences in the effects of illumination on drivers’ visual search ability at different vehicle speeds. Therefore, the collaborative matching of illumination and speed limits can effectively improve traffic efficiency and reduce the probability of nighttime accidents. In this paper, we establish a collaborative optimization model of illumination and speed limits at rural highway intersections that considers drivers’ visual recognition of VRUs. We then design an experiment with illuminance, vehicle speed, and VRU type/location as control variables to collect recognition distances, and finally analyze their effects to calculate speed limits under different illuminances. Results indicate that pedestrians and cyclists appearing from the left side are recognized 24.73% and 15.79% earlier than those from the right, suggesting that VRUs from the right side are more vulnerable. Additionally, the safety benefit of improving illumination on increasing speed limits gradually diminishes as illuminance rises. Therefore, determining the most suitable illumination and speed limit configuration requires a comprehensive evaluation of the cost–benefit relationship between lighting investments and the gains resulting from higher speed limits. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Intelligent Transportation System)
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23 pages, 11512 KB  
Article
Realizing Fuel Conservation and Safety for Emerging Mixed Traffic Flows: The Mechanism of Pulse and Glide Under Signal Coordination
by Ayinigeer Wumaierjiang, Jinjun Sun, Hongang Li, Wei Dai and Chongshuo Xu
Symmetry 2025, 17(12), 2170; https://doi.org/10.3390/sym17122170 - 17 Dec 2025
Cited by 1 | Viewed by 616
Abstract
Pulse and glide (PnG) has limited application in urban traffic flows, particularly in emerging mixed traffic flows comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), as well as at signalized intersections. In light of this, green wave coordination is applied to [...] Read more.
Pulse and glide (PnG) has limited application in urban traffic flows, particularly in emerging mixed traffic flows comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), as well as at signalized intersections. In light of this, green wave coordination is applied to the urban network of multiple signalized intersections. Under perception asymmetries, HDVs lack environmental perception capabilities, while CAVs are equipped with perception sensors of varying performance. CAVs could activate the PnG mode and set its average speed based on signal phase and safety status, enabling assessment of fuel savings and safety. The findings reveal that (i) excluding idling fuel consumption, when the traffic volume is low and market penetration rate (MPR) of CAVs exceeds 70%, CAVs could significantly reduce regional average fuel consumption by up to 8.8%. (ii) Compared to HDVs, CAVs could achieve a fuel saving rate (FSR) ranging from 7.1% to 50%. In low-traffic-volume conditions, CAVs with greater detection ranges could swiftly activate the PnG mode to achieve fuel savings, while in higher-traffic-volume conditions, more precise sensing aids effectiveness. (iii) the PnG mode could ensure safety for CAVs and HDVs, with CAVs equipped with highly precise sensing exhibiting particularly robust safety performance. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Intelligent Transportation System)
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