Method and system for dynamic signal timing optimization based on vehicle-road-cloud integration
Through the collaborative processing of roadside sensing devices and cloud data centers, signal timing optimization based on deep learning has been achieved, solving the problem of real-time perception and prediction accuracy in existing traffic signal control systems and improving the dynamic adaptability and traffic efficiency of the traffic system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- AIPARK TECHNOLOGY CO LTD
- Filing Date
- 2026-03-25
- Publication Date
- 2026-07-17
AI Technical Summary
Existing traffic signal control systems struggle to detect real-time changes in traffic flow and have low prediction accuracy, resulting in rigid and fixed signal timings that cannot adapt to dynamic traffic demands. This leads to problems such as low green light utilization and traffic congestion.
By collecting multi-source traffic data in real time through roadside sensing devices and combining it with deep learning prediction and multi-objective optimization in the cloud data center, traffic light timing optimization strategies are dynamically generated to achieve intelligent and dynamic control of traffic lights.
It improved intersection traffic efficiency, enhanced the adaptive capability of signal control, optimized traffic flow, and reduced vehicle delays and stops.
Smart Images
Figure CN122416754A_ABST