Table of Content

Artificial Intelligence Enabled Intelligent Transportation Systems

Submission Deadline: 25 December 2021 (closed)

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

  • Open Access


    Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

    Faizan Rasheed, Kok-Lim Alvin Yau, Rafidah Md Noor, Yung-Wey Chong
    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2225-2247, 2022, DOI:10.32604/cmc.2022.022952
    (This article belongs to this Special Issue: Artificial Intelligence Enabled Intelligent Transportation Systems)
    Abstract This paper investigates the use of multi-agent deep Q-network (MADQN) to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning (MARL) approach. The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions, particularly rainfall. MADQN is based on deep Q-network (DQN), which is an integration of the traditional reinforcement learning (RL) and the newly emerging deep learning (DL) approaches. MADQN enables traffic light controllers to learn, exchange knowledge with neighboring agents, and select optimal joint actions in a collaborative manner. A case study based on a real traffic… More >

  • Open Access


    TinyML-Based Fall Detection for Connected Personal Mobility Vehicles

    Ramon Sanchez-Iborra, Luis Bernal-Escobedo, Jose Santa, Antonio Skarmeta
    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3869-3885, 2022, DOI:10.32604/cmc.2022.022610
    (This article belongs to this Special Issue: Artificial Intelligence Enabled Intelligent Transportation Systems)
    Abstract A new wave of electric vehicles for personal mobility is currently crowding public spaces. They offer a sustainable and efficient way of getting around in urban environments, however, these devices bring additional safety issues, including serious accidents for riders. Thereby, taking advantage of a connected personal mobility vehicle, we present a novel on-device Machine Learning (ML)-based fall detection system that analyzes data captured from a range of sensors integrated on an on-board unit (OBU) prototype. Given the typical processing limitations of these elements, we exploit the potential of the TinyML paradigm, which enables embedding powerful ML algorithms in constrained units.… More >

  • Open Access


    Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

    Qaisar Abbas, Mostafa E.A. Ibrahim, Shakir Khan, Abdul Rauf Baig
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1999-2007, 2022, DOI:10.32604/cmc.2022.022553
    (This article belongs to this Special Issue: Artificial Intelligence Enabled Intelligent Transportation Systems)
    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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