Special Session: Application of Artificial Intelligence in Transportation and Logistic Engineering

The rapid advancement of artificial intelligence (AI) is fundamentally transforming transportation and logistic systems, driving significant improvements in efficiency, safety, and sustainability. This session examines core AI applications within these domains, with a focus on intelligent traffic management, autonomous vehicles, predictive analytics, and logistics optimization.
In traffic management, machine learning algorithms—including deep neural networks and reinforcement learning—enable real-time traffic flow forecasting and dynamic congestion control, substantially reducing urban delays. Computer vision technologies, such as object detection and video analytics, enhance traffic monitoring and accident prevention through automated incident detection and driver behavior analysis.
Within autonomous transportation, AI integrates multi-sensor data from LiDAR, cameras, and radar to ensure precise navigation and collision avoidance. Coupled with vehicle-to-everything (V2X) communication, these systems support optimized routing and coordinated movement. In public transit and logistics, AI-driven demand forecasting and scheduling optimization improve resource allocation and service reliability.
AI also plays a critical role in predictive maintenance, analyzing infrastructure data—such as road conditions and bridge health—to preempt failures and lower operational costs. In logistics engineering, AI enhances route planning, warehouse automation, inventory management, and supply chain visibility, contributing to end-to-end operational resilience.
Despite these advancements, key challenges remain, including data privacy, algorithmic bias, and the need for robust regulatory frameworks. Future developments will emphasize explainable AI (XAI) for transparent decision-making, along with greater integration of AI into smart city ecosystems. By addressing these challenges, AI will continue to revolutionize transportation and logistics, supporting global objectives such as carbon neutrality and intelligent mobility.

Related Topics for this Session (but not limited to):

1. Deep Learning for Multi-Modal Traffic Flow Prediction and Dynamic Congestion Management
2. Perception, Decision-Making, and Safety Assurance in Autonomous Driving Systems
3. Reinforcement Learning and Large Language Models for Adaptive and Explainable Traffic Control
4. AI-Driven Optimization for Sustainable and Resilient Logistics and Public Transit
5. Predictive Analytics and Digital Twins for Transportation Infrastructure Health Management
6. Ethical AI, Privacy, and Robust Integration in Next-Generation Smart Mobility Systems
7. Others

Short Biography of Organizers

Li Zhihong, Beijing University of Civil Engineering and Architecture, China
Vice Dean, Professor and PhD Supervisor at the School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture. Graduated from Chongqing Jiaotong University and Beijing Jiaotong University, with further academic training at the University of Washington (Visiting Scholar), Purdue University (Postdoctoral Researcher), and Beihang University (Visiting Scholar). Specializes in traffic safety and behavior, traffic big data and deep learning for long-term teaching and research.

Zhao Xia, Beijing University of Civil Engineering and Architecture, China
Ph.D., is an associate professor and master’s supervisor at Beijing University of Civil Engineering and Architecture. She is also a selected member of the Beijing Association for Science and Technology Young Talent Support Program. She has conducted academic visits to the University of Sydney and Beihang University. Her research focuses on transportation big data mining and traffic pattern recognition, with an interdisciplinary background in artificial intelligence. She has led more than 20 research projects, including the National Natural Science Foundation of China Youth Program, China Postdoctoral Science Foundation General Program, and Beijing Social Science Foundation. She holds 7 authorized national invention patents and has published over 20 academic papers in journals such as IEEE Transactions on Intelligent Transportation Systems (TITS) and China Journal of Highway and Transport. She serves as an editorial board member for journals including Journal of Highway and Transportation Research and Development. Additionally, she has co-edited and published the textbook Traffic Management and Control for transportation engineering. She has received more than 10 provincial- and ministerial-level scientific awards from organizations such as the China Communications and Transportation Association and the China Invention Association.

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