Special Session Ⅷ

AI, Expert Experience and Virtual Data Technology Innovation in Intelligent Transportation Control

(智能交通控制领域的人工智能、专家经验与虚拟数据技术创新)



Chair:

Co-chair:

Yunhu Huang

Mingjian Fu

Minjiang University, China

Fuzhou University, China


Keywords:

Topics:

· Intelligent Transportation Control (智能交通控制)

· Artificial Intelligence (人工智能)

· Expert Experience (专家经验)

· Virtual Data Technology (虚拟数据技术)

· Technological Innovation (技术创新)



· Expert-Experience-Driven Virtual Data Generation Technology for Land-Sea-Air Transportation(专家经验驱动的海陆空交通虚拟数据生成技术)

· Cross-Domain Applications of Machine Learning in Aviation/Navigation/Rail Autonomous Driving(机器学习在航空 / 航海 / 铁路自动驾驶中的跨域应用) 

· Trajectory Optimization and Energy-Efficient Control Algorithms for Multi-Modal Transportation(多交通模态下轨迹优化与能效控制算法)

· Cross-Field Interpretability Research on Intelligent Driving Data Generation (智能驾驶数据生成的跨领域可解释性研究)

· AI-Driven Algorithmic Innovations in Intelligent Transportation Control (人工智能驱动的智能交通控制算法创新)

· Expert Experience-Enabled Construction and Optimization of Traffic Control Models (专家经验赋能的交通控制模型构建与优化)

· Applications of Virtual Data Technology in Traffic System Simulation and Prediction (虚拟数据技术在交通系统仿真与预测中的应用)

· Cross-Modal Data Fusion and Technological Innovation in Intelligent Transportation Control (智能交通控制中的跨模态数据融合与技术创新)



Summary:

· This special topic focuses on technological innovation in intelligent transportation control by integrating artificial intelligence, expert experience, and virtual data technology. It leverages AI algorithms to mine traffic data, combines expert knowledge for interpretable control models, and uses virtual data to address high-dimensional data scarcity, exploring multi-technology collaboration for traffic flow optimization, intelligent decision-making, and system simulation to advance theoretical breakthroughs and engineering applications.


· 聚焦智能交通控制领域，融合人工智能技术、专家经验与虚拟数据技术创新。通过人工智能算法挖掘交通数据价值，结合专家知识构建可解释性控制模型，利用虚拟数据技术解决高维场景数据稀缺问题，探索多技术协同下的交通流优化、智能决策与系统仿真，推动智能交通控制在复杂场景下的理论突破与工程应用。


Submission Deadline: 2025.8.31