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About the Journal

The Canadian Journal of Machine Learning & Intelligent Transportation (CJMLIT) is committed to the publication of rigorously peer-reviewed, high-quality research articles papers that explore the innovative application of machine learning in transportation and network systems. As an open access (OA) journal, CJMLIT ensures that all published content is freely available, with the aim of advancing the field through the dissemination of cutting-edge research and insights.

CJMLIT is dedicated to fostering interdisciplinary research that leverages machine learning to address critical challenges in transportation and network systems, promoting more efficient, sustainable, and intelligent infrastructures. The journal serves as a platform for the exchange of ideas and findings between researchers and practitioners in fields such as computer science, engineering, urban planning, and data science.

Key areas of focus include, but are not limited to:

1. Machine learning applications in autonomous and connected vehicles
2. Predictive analytics for traffic management and optimization
3. Machine learning in logistics and supply chain optimization
4. Smart city technologies and urban mobility solutions
5. Network design and infrastructure resilience analysis
6. Environmental impact assessments of transportation systems
7. Safety and security measures in transportation networks
8. Innovations in public transportation systems analysis
9. Data-driven maintenance and monitoring of infrastructure

Editor-in-Chief

Dr. Bappa Muktar

Dr. Vincent Fono