Traffic Lane Number Extraction Based on the Constrained Gaussian Mixture Model

The present methods are expensive and time-consuming that can't keep up with changes in the roads due to construction or disasters. Instead of this, an attractive alternative is proposed to acquire lane numbers from Floating Car Data collected by taxis. Firstly, the Delaunay triangulation network method is used to optimize the raw GPS trajectories by considering the spatial distribution characteristics of floating car data, and then the road width is set by detecting the trajectories covered width. The primary classifier is established from training data that shows the relation between lane numbers and road width, and some candidate lane numbers can be inferred by detecting the trajectory covered width with the primary classifier. The final lane numbers are determined by fitting GPS traces using the Constrained Gaussian mixture model. Experiments results show the method proposed in this paper can acquire lane numbers from low-quality floating car data and the overall accuracy is 82.3%.