Sustainable Intelligent Transport Systems: AI-Driven Multi-Modal Fusion for Green Development

Dear Colleagues,

Modern transportation systems, encompassing road, maritime, aviation, and logistics networks, serve as critical arteries for global economic activities and societal well-being. The rapid evolution of large-scale data collection technologies (e.g., IoT sensors, mobile trajectories) and breakthroughs in artificial intelligence (AI) have opened new frontiers for addressing long-standing challenges in traffic flow optimization, intermodal coordination, and sustainability.

With the acceleration of global urbanization and the intensification of the climate crisis, transportation systems are facing multidimensional challenges: low efficiency of multimodal collaboration, high carbon emissions and energy waste, uncertainty regarding user behaviors, aging transportation infrastructure, and driving safety issues for the elderly. This Special Issue focuses on the innovative applications of data-driven and artificial intelligence technology in multimodal transportation systems, aiming to promote breakthroughs in the following areas:

• Theory and methods of multimodal traffic data fusion;
• Green transportation energy management and optimization;
• AI-powered dynamic decision-making models for transportation systems;
• The systemic integration of autonomous driving and shared mobility;
• Low-carbon operation and maintenance technology for transportation infrastructure;
• Digital transformation and upgrades of transportation infrastructure;
• Evaluation and optimization of traffic sign and line setting plans;
• Driving safety protection technology and strategies for the elderly;
• Multi-modal carbon emission trading mechanisms and green incentive strategies;
• Coordinated scheduling of the electric truck charging network and power grid;
• Modeling of the spatiotemporal distribution of traffic noise and air pollution exposure;
• Energy consumption optimization and path planning for autonomous ships and port systems;
• Maritime traffic perception: fusion analysis of multi-source sensory data;
• Optimization of ship paths in uncertain environments.

Dr. Xinqiang Chen
Dr. Qiang Luo
Dr. Gen Li
Guest Editors

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