About the Journal
Canadian Journal of Machine Learning & Intelligent Transportation (CJMLIT) is committed to the publication of rigorously peer-reviewed, high-quality research articles 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.
CJMLIT does not charge any Article Processing Charges (APCs) until January 31, 2027. All submissions, peer reviews, and publications are entirely free of cost to authors during this period.
Key areas of focus include, but are not limited to:
- Machine learning applications in autonomous and connected vehicles
- Predictive analytics for traffic management and optimization
- Machine learning in logistics and supply chain optimization
- Smart city technologies and urban mobility solutions
- Network design and infrastructure resilience analysis
- Environmental impact assessments of transportation systems
- Safety and security measures in transportation networks
- Innovations in public transportation systems analysis
- Data-driven maintenance and monitoring of infrastructure
Editor-in-Chief
Prof. Moskolaï Ngossaha Justin
Founder, Publisher and Managing Editor
Dr. Bappa Muktar
Associate Editors
Dr. Vincent Fono
Dr. Adama Nouboukpo
Dr. Zongo Meyo
Current Issue
Articles published in this section have successfully completed peer review and have been formally accepted for publication in the Canadian Journal of Machine Learning & Intelligent Transportation (CJMLIT).
Forthcoming articles are published online ahead of their assignment to a regular or special issue. This early publication model allows accepted research to become publicly accessible and citable without waiting for the release of the complete issue.
Publication status: Peer-reviewed, accepted, published online ahead of issue assignment, and citable using the assigned DOI.Each article has a permanent Digital Object Identifier (DOI). Once the article is assigned to its final volume and issue, bibliographic details such as volume, issue, or pagination may be updated. The DOI remains unchanged throughout this process.
Minor editorial or production corrections may be incorporated before final issue assignment where necessary. Any such corrections are limited to presentation, metadata, or production matters and do not constitute a new round of scientific review.
