Special Issue "Artificial Intelligence Enabled Intelligent Transportation Systems"

Submission Deadline: 25 December 2021
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Guest Editors
Dr. Parul Agarwal, Jamia Hamdard, India.
Prof. Kavita Khanna, Northcap University, India.
Dr. Surbhi Bhatia, King Faisal University, Saudi Arabia.


AI is continually evolving and so are the applications and methodologies of AI. When we understand that AI can solve real life problems, this call is a platform for the same for analyzing its application in the transportation sector. But, we also realize that it still needs to grow and evolve. And the real potential can be realized when effort is made to make it more robust and more useful. AI not only provides autonomous transportation means, rather, it provides smoother, cleaner, greener, efficient ways of transport. Some of the potential benefits it offers are in form of green vehicles, reducing accident occurrence, effective traffic design and its management, reduced carbon emissions, effective analysis of traffic, etc. Also, contributions from scientific, and engineering fields that exploit data science, analytics, and other supporting techniques, highlight the advances, and future solutions using AI are invited. These should be supported by extensive Literature survey, comparative studies, user experiences, models/ architectures, and experiments/ evaluation. This call shall serve as a platform to share research ideas on the above mentioned aspects of AI and use them to impact the future in a positive manner.

• AI based autonomous vehicles
• Design, control and management of real-time traffic
• Advanced Traveler Information systems and predictions on transport usage
• Related technologies (like Big data analysis/ Expert systems/ Cloud Computing/ IoT) for smart and intelligent transport
• Artificial transportation systems and simulation models • Handling human and behavioral factors in intelligent systems
• Building smart and efficient modes of transport using AI
• Green Vehicles
• Smart Parking solutions
• Challenges and opportunities associated with AI based Sustainable Transportation systems
• Future Scenario of smart transportation

Published Papers
  • Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System
  • Abstract Traffic accidents are caused by driver fatigue or distraction in many cases. To prevent accidents, several low-cost hypovigilance (hypo-V) systems were developed in the past based on a multimodal-hybrid (physiological and behavioral) feature set. Similarly in this paper, real-time driver inattention and fatigue (Hypo-Driver) detection system is proposed through multi-view cameras and biosignal sensors to extract hybrid features. The considered features are derived from non-intrusive sensors that are related to the changes in driving behavior and visual facial expressions. To get enhanced visual facial features in uncontrolled environment, three cameras are deployed on multiview points (0°, 45°, and 90°) of… More
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