This invention provides a parameter self-learning
signal control method based on cloud-edge
collaboration, solving problems such as heavy reliance on traffic parameter configuration, excessive dependence on training samples, and difficulty in achieving real-time, all-weather, and all-
scenario application of
traffic signal algorithm control. The method mainly includes: collecting traffic holographic trajectory data and performing edge-sensing
signal control; extracting holographic sensing
control parameters from the holographic data through a parameter self-learning method; establishing a cloud-edge collaborative control architecture, interacting with parameter self-
learning data, and controlling traffic signals in real time. By determining the traffic state of each phase through holographic data, employing the parameter self-learning method, and establishing a cloud-edge collaborative
control system, it achieves all-weather, all-
scenario application of traffic control, possessing advantages such as strong real-
time control capabilities, adaptability to various
traffic flow conditions and scenarios, and avoiding the computational power and trial-and-error costs required for
parameter learning.