Urban road traffic bottleneck model predictive control method based on online parameter calibration
By using a predictive control method for urban road traffic bottlenecks with real-time calibration of traffic flow model parameters and dynamic boundary conditions, the problem of lack of global information and time-varying parameters in traditional methods is solved, achieving efficient traffic flow control and safe lane-changing operations, and improving road traffic efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- LIAONING UNIVERSITY OF TECHNOLOGY
- Filing Date
- 2026-04-23
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional traffic flow control methods lack global information exchange and coordination when facing dynamic bottleneck road sections, resulting in local traffic flow disorder, difficulty in adapting to the time-varying nature of traffic flow parameters, and affecting road traffic efficiency.
A model-based predictive control method for urban road traffic bottlenecks based on online parameter calibration is adopted. By collecting traffic flow data in real time, the model parameters are calibrated using the recursive least squares method with memory anchors. Combined with dynamic boundary conditions and MPC optimization problems, a three-layer control architecture is constructed to achieve macro-micro coordinated control.
It improved the accuracy of traffic flow prediction, enhanced the safety of vehicle lane changes and road throughput, effectively suppressed local congestion, and improved the road network capacity.
Smart Images
Figure CN122416786A_ABST