Call for Papers-SI on AI-Driven ITS 2026
Call for Papers-SI on AI-Driven Intelligent Transportation Systems
Special Issue on Machine Learning, Digital Twins, and Connected Mobility
Aims and Scope
The Canadian Journal of Machine Learning & Intelligent Transportation (CJMLIT) invites submissions for a Special Issue devoted to AI-Driven Intelligent Transportation Systems: Machine Learning, Digital Twins, and Connected Mobility. Transportation systems are entering a new era shaped by artificial intelligence, machine learning, connected mobility, digital infrastructures, and data-intensive decision-making. These advances are transforming how mobility networks are monitored, modeled, optimized, and managed in real time.
The rapid development of connected and autonomous vehicles, Vehicular Ad hoc Networks (VANETs), Internet of Things (IoT)-enabled infrastructures, edge intelligence, Mobility-as-a-Service (MaaS), and Digital Twin technologies is creating new opportunities for safer, more efficient, resilient, and sustainable transportation systems. In parallel, emerging paradigms such as Agentic AI, Frugal AI, distributed learning, and multi-agent systems are opening new research directions for intelligent mobility, particularly in smart cities and resource-constrained urban environments.
This Special Issue seeks to bring together researchers, engineers, practitioners, and policy-oriented experts working at the intersection of machine learning, intelligent transportation systems, smart mobility platforms, connected transportation infrastructures, and transportation data science. We particularly welcome original contributions that combine theoretical innovation, methodological rigor, experimental validation, simulation-based analysis, or real-world deployment in the context of intelligent and sustainable mobility.
Lead Guest Editor
Prof. Justin Moskolaï Ngossaha
Faculty of Sciences, University of Douala, Cameroon
General Co-Chair, AEEF 2026
Research Interests: Artificial Intelligence, Decision Support Systems, Knowledge Engineering, Sustainable Systems, Transportation Systems.
Dr. Bappa Muktar
Université du Québec en Outaouais (UQO), Canada
Research Interests: Networks, Intelligent Transportation Systems, Connected Vehicles, Applied Artificial Intelligence.
Special Issue Information
Authors are invited to submit original, unpublished, and high-quality manuscripts addressing emerging theories, models, architectures, algorithms, applications, and case studies related to AI-driven intelligent transportation systems. Topics of interest include, but are not limited to:
Intelligent Transportation Systems
- Machine learning for Intelligent Transportation Systems (ITS)
- Reinforcement learning for adaptive traffic signal control
- Traffic flow prediction using deep learning
- AI-based congestion detection and mitigation
- Spatiotemporal modeling of mobility patterns
Autonomous and Connected Vehicles
- Machine learning models for autonomous vehicle perception and control
- Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication
- Cooperative and connected mobility systems
- Safety and risk prediction for autonomous transportation
- Multi-agent coordination for autonomous mobility
Vehicular Networks and VANETs
- Machine learning techniques for Vehicular Ad hoc Networks (VANETs)
- Intelligent routing protocols for vehicular communication networks
- Edge intelligence and distributed learning in vehicular environments
- Security, trust, and privacy in vehicular networks
Digital Twins for Transportation Systems
- Digital twin architectures for transportation infrastructures
- Urban mobility digital twins for smart cities
- Real-time traffic monitoring and simulation using digital twins
- AI-enabled predictive transportation management using digital twins
IoT and Edge Intelligence for Transportation
- IoT-based sensing and monitoring of transportation infrastructures
- IoT orchestration frameworks for transportation ecosystems
- Edge computing for real-time transportation analytics
- Sensor networks for traffic monitoring and infrastructure management
Mobility-as-a-Service and Smart Mobility Platforms
- AI-driven Mobility-as-a-Service (MaaS) platforms
- Machine learning for multimodal mobility optimization
- Integration of public transport, shared mobility, and micro-mobility services
- Personalized mobility recommendation systems
- Pricing and demand prediction models for MaaS ecosystems
- Data-driven urban mobility platforms
Agentic AI and Multi-Agent Systems in Mobility
- Agentic AI for autonomous decision-making in transportation systems
- Multi-agent reinforcement learning for traffic optimization
- Intelligent agents for mobility coordination and routing
- Agent-based simulations for transportation planning
Frugal AI for Sustainable Mobility
- Frugal AI approaches for resource-constrained transportation systems
- Lightweight machine learning models for edge transportation systems
- Energy-efficient AI models for intelligent mobility applications
- Low-cost intelligent mobility solutions for emerging cities
Infrastructure Monitoring and Predictive Maintenance
- Machine learning for predictive maintenance of transportation infrastructures
- AI-based road condition monitoring using sensor and vision data
- Infrastructure health monitoring using data-driven approaches
Logistics and Transportation Networks
- Machine learning for logistics and supply chain optimization
- Intelligent freight transportation systems
- AI-driven routing and last-mile delivery optimization
Sustainable and Green Transportation
- Machine learning for emission prediction and reduction
- AI-driven sustainable mobility solutions
- Environmental impact assessment of transportation systems
Transportation Data Science
- Big data analytics for mobility systems
- Graph-based learning for transportation networks
- Federated learning for transportation data sharing
- Privacy-preserving machine learning in transportation systems
Types of Submissions
The Special Issue welcomes the following categories of manuscripts:
- Original research articles
- Review papers
- Case studies
- Methodological contributions
- Experimental and simulation-based studies
All submitted manuscripts will undergo a rigorous double-blind peer review process in accordance with the editorial standards of the Canadian Journal of Machine Learning & Intelligent Transportation.
Manuscript Submission Information
Authors should submit their manuscripts through the CJMLIT online submission system and select the Special Issue: “AI-Driven Intelligent Transportation Systems: Machine Learning, Digital Twins, and Connected Mobility.”
Manuscripts must follow the journal author guidelines. Submissions must present original work that has not been published previously and is not under consideration elsewhere.
More information about the journal is available at: https://cuspidescience.com/index.php/cjmlit
Important Dates
| Milestone | Date |
|---|---|
| Call for Papers Announcement | April 2026 |
| Paper Submission Deadline | 30 August 2026 |
| First Review Round | September 2026 |
| Revised Paper Submission | October 2026 |
| Final Decision | November 2026 |
| Publication of Special Issue | December 2026 |
Publication Fees
The Canadian Journal of Machine Learning & Intelligent Transportation (CJMLIT) is an open access journal. No Article Processing Charges (APCs) are required for submissions accepted before January 31, 2027.
Journal Information
The Canadian Journal of Machine Learning & Intelligent Transportation (CJMLIT) publishes peer-reviewed research at the intersection of machine learning, intelligent transportation systems, connected mobility, transportation data science, and sustainable smart infrastructures.
Contact Information
For inquiries regarding this Special Issue, please contact the editorial office: cjmlit@cuspidescience.com
Keywords
Machine Learning; Intelligent Transportation Systems; Digital Twins; Connected Mobility; VANETs; Smart Cities; Mobility-as-a-Service; Edge Intelligence; Agentic AI; Frugal AI; Sustainable Transportation; Transportation Data Science.
