Perception, Control, and Optimization of Intelligent Transportation Systems

Chair:	Co-Chair:
Ge Guo  Northeastern University, China	Meng Han Northeastern University, China

Key Words: Intelligent Transportation Systems, Connected and Autonomous Vehicles, State Perception, Traffic Control and Optimization

Information: With the rapid development of the Internet of Things, artificial intelligence, and big data technologies, transportation systems are evolving from traditional management paradigms toward real-time perception, autonomous decision-making, and coordinated optimization. This topic aims to explore the critical role of multidimensional sensing technologies in acquiring traffic state information. It also examines control methods that integrate data-driven and model-driven approaches, with applications in signal timing, congestion mitigation, and emergency response. In addition, attention is given to system-level optimization problems in complex transportation networks, including traffic flow assignment, travel demand management, and low-carbon transportation strategies. By combining theoretical advances with engineering practices, this topic seeks to promote the development of safe, efficient, green, and intelligent modern transportation systems.

Topics of interest include but are not limited to: 

• Intersection Signal Control and Adaptive Scheduling
• Vehicle Perception and Decision-Making
• Data- and Mechanism-Driven Traffic Control
• Traffic State Prediction and Risk Identification
• Traffic Congestion Mitigation and Optimization Scheduling
• Multi-Source Traffic Data Perception and Fusion

Submission Deadline: April 28, 2026 (the first round)