Abstract: Vehicle trajectory data becomes an important approach to access and update of road information. However, conventional methods cannot identify road change type and extract change entities quickly using crowdsourcing trajectory data. To solve the problem, this paper propose a new method to use vehicle trajectory big data to detect and update changes rapidly in the road network. Firstly, road change type is identified by detecting and classifying the road change information using trajectory movement geometry information (direction, turn angle) and traffic semantic information(traffic volume, speed). Through analysis of trajectory data, the real physical change and traffic semantic change of road can be distinguished from each other. And then incremental information is extracted by Delaunay triangulation and traffic flow time series analysis. This method combines the road change type identifying and incremental data extraction through taking road segment buffer as basic unit. Finally, incremental information fusion is conducted according to road change type. An experiment using taxi GPS traces data in Shenzhen is verified the validity of the novel method. The experimental results prove that the method can identity road change type, and the accuracy of incremental data is improved about 18% compared with map matching method. Furthermore, the comparison analysis of the road network update results is also carried out to confirm that the method is suitable for layer-level updates.