Aims and Scope

IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of intelligent transport systems and infrastructures.

The scope of the journal includes the following:

    Sustainable Traffic Solutions
    Deployments with enabling technologies
    Pervasive Monitoring Applications
    Demonstrations and evaluation
    Economic and behavioural analyses of ITS services and scenarios
    Data Integration and analytics
    Information collection and processing
    Image processing applications in ITS
    ITS aspects of electric vehicles
    Intelligent/Autonomous Vehicles
    Connected Vehicle Systems
    In-vehicle ITS, safety and vulnerable road user aspects
    Mobility as a Service Systems
    Traffic management and control
    Public transport systems technologies
    Fleet and public transport logistics
    Emergency and incident management
    Demand management and electronic payment systems
    Traffic related Air Pollution Management
    Policy and institutional issues
    Interoperability, standards and architectures
    Funding scenarios
    Enforcement
    Human machine interaction
    Education, training and outreach

Special Issue on Communication, Sensing, Computing and Networking for Intelligent and Secured Vehicles
Submission Date: 2026-01-31

There have been upsurging advancements in vehicular communications, computing, and sensing. Cutting-edge technologies and applications have emerged to address the challenges of vehicular networks. However, critical areas still require further investigations, including different communications scenarios over multiple spectrum bands and the integration of cloud and edge computing technologies in vehicular environments within the context of the 6G and beyond. AI-driven techniques that enhance intelligent decision-making and automation in vehicular communications are another pivotal area for future proof applications. Investigation into security within vehicular networks is crucial for information security and privacy preservation. This is without overlooking the importance of sustainable and energy-efficient vehicular network designs, green communications, and net-zero emissions. These technologies aim to develop intelligent and secure ITS applications, including autonomous vehicles and connected vehicles.

IET Intelligent Transport Systems invites submissions for this special issue dedicated to the latest advancements in intelligent vehicular communications, computing, and sensing networks. Authors are invited to submit original, unpublished research papers not currently under review by other journals or conferences. All submissions will undergo a rigorous peer-review process to ensure the quality and relevance of the published articles.

Topics of interest for this call for papers include but are not restricted to:

    Vehicle to everything communications: V2V, V2I, V2P, V2X;
    Localization and tracking for vehicles and other transportation participants;
    Integrated sensing and communications;
    Networked sensing for intelligent vehicles and transportation;
    Machine learning for vehicular communications and networks;
    Machine learning for vehicular sensing data and traffic data;
    Communications, signal processing and networking to support aerial vehicles and integration of ground-vehicle traffic;
    Cloud/Edge Computing for vehicular networks;
    Network slicing for vehicular networks;
    Self-organizing network for vehicles and transportation;
    Internet of vehicular networks;
    AI-driven techniques for vehicular networks;
    Blockchain technology and information security in vehicular networks;
    Software defined vehicular networks;
    Digital twins for vehicular networks;
    Net-Zero vehicular networks;
    Vehicular communications and sensing networks;
    Machine vision applications in vehicular networks;
    Vehicular communications over multiple spectrum bands (RF, mmWave, optical and Hybrid);
    Network architecture and protocols.

Guest Editors:

Farah Al-Sallami (Lead)
University of Leeds,
United Kingdom

Sujan Rajbhandari
University of Strathclyde,
United Kingdom

Bo Tan
Tampere University,
Finland

Chedlia Ben Naila
Nagoya University,
Japan

Special Issue on AI-Driven Traffic Psychology for Intelligent and Resilient Transportation
Submission Date: 2026-01-31

Artificial Intelligence (AI) and data-driven behavioural analysis are transforming modern transportation systems by enabling intelligent, adaptive, and resilient mobility solutions. However, human factors such as driver psychology, cognitive load, emotional states, and trust in AI-driven decision-making remain key challenges for the successful deployment of intelligent transportation systems (ITS). The increasing reliance on AI for real-time decision-making, traffic optimization, and autonomous vehicle control highlights the need to integrate human-centered AI methodologies to enhance safety, efficiency, and trust in ITS.

This Special Issue on AI-Driven Traffic Psychology for Intelligent and Resilient Transportation aims to bridge the gap between traffic psychology and computational intelligence, promoting interdisciplinary research in AI-driven driver behaviour modelling, emotion-aware AI, cognitive load estimation, explainable AI, computer vision-based risk assessment, and multimodal sensor fusion. As connected and autonomous vehicles become more prevalent, understanding and modelling human-AI interaction in mobility systems are essential to ensure seamless transition to AI-assisted self-driving technologies.

This special issue seeks cutting-edge research on deep learning, reinforcement learning, federated learning, digital twins, and affective computing applied to ITS. By fostering advancements in AI-powered traffic psychology, risk assessment, and adaptive mobility solutions, this special issue will contribute to the development of safer, more efficient, and human-centric transportation systems.

Topics for this call for papers include but are not restricted to:

    Human-Centered AI for ITS
    AI for Cognitive ITS
    Explainable AI for ITS
    Human-AI Interaction in Connected and Autonomous Vehicles
    Multimodal AI for Personalized and Adaptive Mobility
    AI-Driven Analysis of Driver Behaviour Assessment
    Computer Vision for Driving Style Recognition and Adaptation
    Pose Estimation for Driver Monitoring
    Graph Neural Networks for Traffic Pattern Prediction
    Anomaly Detection in Driving Patterns Using AI
    Emotion-Aware AI for Enhancing Road Safety
    Affective Computing for Driver Status
    Multimodal Sensor Fusion for Psychological Analytics
    Reinforcement Learning for Driver Behaviour Modelling
    Federated Learning for Personalized Driving Behaviour and Traffic Adaptation
    Digital Twin for Driver Behaviour Simulation and Traffic Resilience
    Edge AI for Real-Time Driver Assistance and Behavioural Prediction
    Blockchain for Trust and Behavioural Incentives in Smart Mobility

Guest Editors:

Celimuge Wu (Lead)
The University of Electro-Communication
Japan

Soufiene Djahel
Coventry University
UK

Rui Yin
Hangzhou City University
China

Yu Tang
Lingnan Normal University
China

Carlos T Calafate
Technical University of Valencia
Spain

Chase Wu
New Jersey Institute of Technology
USA

Special Issue on Artificial Intelligence in Intelligent Transport Systems: Optimizing Traffic Flow and Road Safety
Submission Date: 2026-03-02

This Special Issue will serve as a platform for researchers and practitioners to share their latest Artificial Intelligence (AI)-driven Intelligent Transport Systems (ITS) advancements, addressing pressing transportation challenges and shaping the future of intelligent mobility. It aims to explore how cutting-edge AI technologies (e.g., machine learning, deep learning, reinforcement learning) can enhance ITS by improving road traffic flow, safety, and efficiency. In addition, it will encourage interdisciplinary research that connects AI innovations with real-world ITS applications, including case studies, pilot projects, and deployment challenges. Another motive of this special issue would be to highlight AI-driven approaches for optimizing public road transport, reducing environmental impact, and improving urban mobility planning. Finally, it aims to investigate AI-based solutions for real-time road traffic monitoring, congestion mitigation, and accident prevention to create safer and more sustainable transportation networks.

This subject is of current interest due to rapid urbanization and traffic congestion, rising demand for safer roads, and push for sustainability and smart cities, to site a few reasons. Governments and industries are prioritizing eco-friendly transport solutions, and AI can optimize fuel consumption, reduce emissions, and improve public transportation efficiency.

Topics of interest include but are not limited to the following:

    Road Traffic Management & Optimization: AI-driven adaptive traffic signal control systems; Reinforcement learning for dynamic traffic optimization; AI-based route planning and traffic monitoring
    Autonomous and Connected Vehicles: Cooperative AI models for connected vehicle communication (V2V, V2I, V2X); AI-enhanced sensor fusion techniques for autonomous driving; Deep learning for lane detection and obstacle avoidance
    Road Safety & Accident Prevention: AI-based accident avoidance and risk assessment models; Real-time ADAS; AI-driven pedestrian and cyclist safety enhancements; Video analytics for road safety monitoring and anomaly detection
    Smart Infrastructure & Urban Mobility: AI for energy-efficient and sustainable urban mobility solutions; Edge AI for real-time traffic data processing in smart cities; AI-powered passenger flow analysis and crowd management
    Emerging AI Technologies in ITS: Federated learning for decentralized ITS data processing; AI-driven cybersecurity solutions for intelligent transport networks; Quantum computing applications in transport optimization

Guest Editors:

Dr. Nishu Gupta
VTT Technical Research Centre of Finland Ltd.
Finland

Dr. Jukka Mäkelä
VTT Technical Research Centre of Finland Ltd.
Finland

Dr. Manuel J. Cabral S. Reis
UTAD University Engineering Department Quinta de Prados
Portugal

Dr. Ramon Agüero
University of Cantabria
Spain

Dr. Ferheen Ayaz
City St. George's, University of London
United Kingdom

Dr. Xabiel García Pañeda
University of Oviedo
Spain

Keywords: AI; Safety; Traffic; Road transport 

Special Issue on Towards 6G connected Intelligent Transport Systems
Submission Date: 2026-03-31

The upcoming 6G era of cellular connectivity promises transformative advancements for Intelligent Transport Systems (ITS), providing ultra-reliable, low-latency, and high-capacity communication solutions that enable real-time, data-driven decision-making for connected and autonomous vehicles. This Special Issue focuses on exploring the integration of 6G into ITS, aiming to support new applications in connected mobility, infrastructure-vehicle communications (V2I), and vehicle-to-vehicle (V2V) interactions that address safety, traffic efficiency, and environmental sustainability. We invite high-quality original research, review or industry contributions that explore the integration of 6G into ITS to tackle key challenges in connectivity, including stringent latency, Quality of Service (QoS), and seamless data integration. Suitable topics include, but are not limited to, innovations in physical-layer communications, localization and sensing for smart mobility, network architecture, resource management for autonomous driving, and energyefficient connectivity solutions, cooperative applications for advanced ITS services. Submissions should include theoretical or practical analyses validated by simulations or real testbeds and should highlight the contributions to 6G-enabled vehicular communications aimed at road safety, congestion management, or reduced emissions

Topics for this call for papers include but are not restricted to:

    6G vehicular communications for next-generation transport systems
    Beyond 5G and 6G networks for vehicular applications
    Artificial Intelligence-enabled vehicular communications and applications
    Millimetre-wave and terahertz communications for vehicular communications, including channel estimation or reconstruction
    Multi-antenna technologies in vehicular communications
    Ultra-low latency and high-reliability V2X communications
    Energy-efficient and green connectivity solutions for ITS
    Localization, tracking and sensing for cooperative vehicular applications
    Integrated sensing and communications for vehicular sensor networks

Guest Editors:

Carmen Botella-Mascarell (Lead)
Universitat de València
Spain

Sandra Roger
Universitat de València
Spain

David García-Roger
Universitat de València
Spain

Mattia Brambilla
Politecnico di Milano
Italy

Anna Guerra
Università di Bologna
Italy

Alessio Fascista
Politecnico di Bari
Italy

Special Issue on Electric Vehicles and Charging Infrastructure: Technologies, Policies, and Operational Insights
Submission Date: 2026-06-01

IET Intelligent Transport Systems invites submissions for this Special Issue focused on EVs and their supporting charging infrastructure. Submitted papers must be original and not under review by other journals or conferences. All manuscripts will undergo a rigorous peer-review process to ensure technical quality. We welcome high-quality research and review articles on topics including, but not limited to:

    Planning, siting, and optimization of EV charging infrastructure;
    Charging impacts on traffic flow, congestion, energy systems, and safety;
    Charging infrastructure in vehicle-to-grid (V2G) and smart grid integration;
    Simulation-based analysis of EV energy and traffic performance;
    Interoperability, system reliability, and charging station maintenance strategies;
    Safe operation, maintenance, and interaction of EVs with users, infrastructure, and other road users;
    Cybersecurity vulnerabilities in EV charging infrastructure and their safety implications;
    Travel pattern shifts due to home, workplace, and public charging access;
    Spatial-temporal modelling of EV charging demand under different adoption scenarios;
    Behavioural modelling of how charging accessibility influences EV purchase decisions;
    Application of digital twins to simulate EV infrastructure-adoption interactions;
    Rural vs. urban disparities in EV adoption and infrastructure access;
    Psychological and behavioural factors in EV adoption and charging decisions;
    Influence of charging availability on travel behaviour, routing, and trip timing;
    Economic evaluation of EV infrastructure and cost-benefit analyses;
    Workforce development, skill gaps, and maintenance challenges in charging infrastructure operations;
    Development and challenges of commercial electric vehicle (e.g., truck, bus).

Guest Editors:

Li Zhao (Lead)
University of Nebraska-Lincoln,
USA

Jason Hawkins
University of Calgary,
Canada

Chenhui Liu
Hunan University,
China

Chaoru Lu
Oslo Metropolitan University,
Norway