He graduated from the major of Traffic Information Engineering and Control and has made remarkable achievements in the field of intelligent traffic signal optimization. The new signal optimization algorithm he developed, through practical application, has reduced the average travel time on the main roads of the pilot city by 20%, greatly improving the urban road traffic efficiency.

He has been delving into intelligent vehicle perception technology for a long time, especially achieving fruitful results in the application of computer vision in traffic scenarios. The high - precision vehicle recognition system developed by the team he leads has an identification accuracy rate of up to 98%, providing key technical support for the environmental perception of intelligent driving.
 

As an outstanding figure in intelligent transportation big data analysis, he is good at mining hidden traffic patterns from massive data. The big data analysis project he led accurately predicts traffic congestion periods and sections, providing extremely valuable bases for urban traffic planning and management decision - making.
 

He has rich practical experience in the field of intelligent transportation system integration. He has successfully led the implementation of multiple large - scale intelligent transportation projects. In the urban comprehensive intelligent transportation system project, through the efficient integration of various subsystems, the overall operational stability of the system has been increased by 30%, ensuring the orderly operation of urban traffic.