Special Issue(24-04): Ubiquitous Green Artificial Intelligence based Computing for Smart Transportation

Speical Issue: Ubiquitous Green Artificial Intelligence based Computing for Smart Transportation

Using advanced mobile and ubiquitous computing in smart transportation stands as a forefront in smart city environment. Since, the future of transportation lies in Green AI-based computing shifting towards enhanced efficiency and productivity, it facilitates seamless people mobility and fosters a higher quality of life. Its significance deepens as it becomes instrumental in constructing intelligent systems, particularly within the area of intelligent transportation. In addition, the core objective of intelligent transportation systems is to provide a safer, cleaner, affordable and efficient mode of transportation. This route to smart cities necessitates the integration of cutting-edge transportation systems like autonomous vehicles, connected roads and intelligent parking solutions. This surge in smart transportation technologies demands heightened memory bandwidth, energy efficiency, accelerated computing speeds and robust edge processing capabilities.

AI-based computing extends many advantages to transportation consisting streamlined traffic flow, diminished emissions, heightened customer satisfaction and strengthen safety measures. The importance of this progress is green machine intelligence-based computing for Smart Transportation. In recent years, the role of intelligent applications in transportation has expanded by Ubiquitous Green AI-based Computing for Smart Transportation in the Future Mobile Internet. As the transportation industry undergoes a radical transformation to mitigate environmental impact, Green Machine Intelligence-based Computing emerges as a pivotal force, ensuring the long-term sustainability of land, air and sea transportation.

Further, with an increasing array of sensors and communication technologies in vehicles, the scope for intelligent transportation systems to enhance safety, efficiency and sustainability is vast. AI-based green computing will be instrumental in shaping real-time intelligent transportation applications and also ensures adaptation and seamless interaction with their environment. Also, in a landscape where automated driving, connected services and shared mobility models redefine transportation dynamics, smart cities must prioritize reducing their carbon footprint. This Special Issue aims to understand and analyze the current state of Ubiquitous Green Artificial Intelligence in Smart Transportation while fostering discussions on future directions. It also aims to contribute to the development of sustainable and intelligent transportation infrastructures for smart cities

List of topics of interest includes, but are not limited to the following:

· AI-driven Traffic Management Strategies for Urban Sustainability

· Integration of Electric and Autonomous Vehicles in Smart Transportation

· Privacy-Preserving Data Analytics in UGAI for Transportation

· Interconnected Smart Infrastructure for Seamless Transportation Systems

· Energy-Efficient Routing Algorithms for Green Transportation

· Human-Centric Design and User Experience in Smart Transportation Apps

· Carbon Footprint Reduction through AI-Optimized Logistics

· Cross-Domain Collaboration for Standardized Communication Protocols

· Resilience and Robustness of UGAI Systems in Adverse Conditions

· Social and Economic Impacts of UGAI in Smart Transportation

· Green Fleet Management: AI Applications in Vehicle Maintenance

· Autonomous Public Transportation: Challenges and Opportunities

Guest Editor Details of this Special Issue:

Dr. Kamalakanta Muduli (Lead GE)

Associate Professor,

Mechanical Engineering Department,

Papua New Guinea University of Technology,

Morobe Province, Papua New Guinea. Email-ID: [email protected], [email protected] Google Scholar: https://scholar.google.co.in/citations?user=LdMwYiIAAAAJ&hl=en Linked-In: https://in.linkedin.com/in/kamalakanta-muduli-98119076

Research Interest: Operations Management, Industry 4.0, Health Care, Waste Management, Decision Making, Machining

Dr. Jiaqi Ruan (Co-GE)

Research Associate,

Department of Electrical and Electronic Engineering,

The Hong Kong Polytechnic

University, Hong Kong. Email-ID: [email protected] Google Scholar: https://scholar.google.com/citations?user=XL1OiAUAAAAJ&hl=en Linked-In: https://cn.linkedin.com/in/jiaqi-ruan-803a05260

Research Interest: Smart Grid, Cyber-Physical Security, Deep Learning, Demand Response, Electricity Market, Renewable Energy

Dr. Karthik Chandran (Co-GE)

Professor,

Department of Robotics and Automation,

Jyothi Engineering College,

Thrissur, Kerala. Email-ID: [email protected] Google-Scholar: https://scholar.google.com/citations?user=lGmG0hQAAAAJ&hl=en Linked-In: https://in.linkedin.com/in/dr-karthik-chandran-98aa1050

Research Interest: Process Control design and Automation, Medical Image Processing, Machine learning

Important Dates (Tentative):

Manuscript Submission Deadline : Mar 31, 2025

Authors Notification : Jun 30, 2025

Revised Papers Due : Jul 31, 2025

Final notification : Sep 15, 2025